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621ffdd236468d709f181d58 | amirveyseh/acronym_identification | amirveyseh | false | False | 2024-01-09T11:39:57.000Z | 19 | false | 15ef643450d589d5883e289ffadeb03563e80a9e |
Dataset Card for Acronym Identification Dataset
Dataset Summary
This dataset contains the training, validation, and test data for the Shared Task 1: Acronym Identification of the AAAI-21 Workshop on Scientific Document Understanding.
Supported Tasks and Leaderboards
The dataset supports an acronym-identification task, where the aim is to predic which tokens in a pre-tokenized sentence correspond to acronyms. The dataset was released for a Shared… See the full description on the dataset page: https://huggingface.co/datasets/amirveyseh/acronym_identification. | 206 | acronym-identification | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2010.14678",
"region:us",
"acronym-identification"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d59 | ade-benchmark-corpus/ade_corpus_v2 | ade-benchmark-corpus | false | False | 2024-01-09T11:42:58.000Z | 25 | false | 4ba01c71687dd7c996597042449448ea312126cf |
Dataset Card for Adverse Drug Reaction Data v2
Dataset Summary
ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug.
DRUG-AE.rel provides relations between drugs and adverse effects.
DRUG-DOSE.rel provides relations between drugs and dosages.
ADE-NEG.txt provides all sentences in the ADE corpus that DO NOT contain… See the full description on the dataset page: https://huggingface.co/datasets/ade-benchmark-corpus/ade_corpus_v2. | 244 | null | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:coreference-resolution",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d5a | UCLNLP/adversarial_qa | UCLNLP | false | False | 2023-12-21T14:20:00.000Z | 32 | false | c2d5f738db1ad21a4126a144dfbb00cb51e0a4a9 |
Dataset Card for adversarialQA
Dataset Summary
We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial… See the full description on the dataset page: https://huggingface.co/datasets/UCLNLP/adversarial_qa. | 135 | adversarialqa | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2002.00293",
"arxiv:1606.05250",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d5b | Yale-LILY/aeslc | Yale-LILY | false | False | 2024-01-09T11:49:13.000Z | 12 | false | 2305f2e63b68056f9b9037a3805c8c196e0d5581 |
Dataset Card for "aeslc"
Dataset Summary
A collection of email messages of employees in the Enron Corporation.
There are two features:
email_body: email body text.
subject_line: email subject text.
Supported Tasks and Leaderboards
More Information Needed
Languages
Monolingual English (mainly en-US) with some exceptions.
Dataset Structure
Data Instances
default
Size of downloaded dataset… See the full description on the dataset page: https://huggingface.co/datasets/Yale-LILY/aeslc. | 132 | aeslc | [
"task_categories:summarization",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1906.03497",
"region:us",
"aspect-based-summarization",
"conversations-summarization",
"multi-document-summarization",
"email-headline-generation"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d5c | nwu-ctext/afrikaans_ner_corpus | nwu-ctext | false | False | 2024-01-09T11:51:47.000Z | 6 | false | 445834a997dce8b40e1d108638064381de80c497 |
Dataset Card for Afrikaans Ner Corpus
Dataset Summary
The Afrikaans Ner Corpus is an Afrikaans dataset developed by The Centre for Text Technology (CTexT), North-West University, South Africa. The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Afrikaans language. The dataset uses CoNLL shared task annotation standards.
Supported Tasks and Leaderboards
[More… See the full description on the dataset page: https://huggingface.co/datasets/nwu-ctext/afrikaans_ner_corpus. | 103 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:af",
"license:other",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d5d | fancyzhx/ag_news | fancyzhx | false | False | 2024-03-07T12:02:37.000Z | 124 | false | eb185aade064a813bc0b7f42de02595523103ca4 |
Dataset Card for "ag_news"
Dataset Summary
AG is a collection of more than 1 million news articles. News articles have been
gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of
activity. ComeToMyHead is an academic news search engine which has been running
since July, 2004. The dataset is provided by the academic comunity for research
purposes in data mining (clustering, classification, etc), information retrieval
(ranking, search, etc)… See the full description on the dataset page: https://huggingface.co/datasets/fancyzhx/ag_news. | 6,975 | ag-news | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d5e | allenai/ai2_arc | allenai | false | False | 2023-12-21T15:09:48.000Z | 116 | false | 210d026faf9955653af8916fad021475a3f00453 |
Dataset Card for "ai2_arc"
Dataset Summary
A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also
including a corpus of over 14 million science sentences… See the full description on the dataset page: https://huggingface.co/datasets/allenai/ai2_arc. | 836,988 | null | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:multiple-choice-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1803.05457",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d5f | google/air_dialogue | google | false | False | 2024-03-07T15:22:15.000Z | 15 | false | dbdbe7bcef8d344bc3c68a05600f3d95917d6898 |
Dataset Card for air_dialogue
Dataset Summary
AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.
News in v1.3:
We have included the test split of the AirDialogue dataset.
We… See the full description on the dataset page: https://huggingface.co/datasets/google/air_dialogue. | 77 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:conversational",
"task_ids:dialogue-generation",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d60 | komari6/ajgt_twitter_ar | komari6 | false | False | 2024-01-09T11:58:01.000Z | 4 | false | af3f2fa5462ac461b696cb300d66e07ad366057f |
Dataset Card for Arabic Jordanian General Tweets
Dataset Summary
Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.
Supported Tasks and Leaderboards
The dataset was published on this paper.
Languages
The dataset is based on Arabic.
Dataset Structure
Data Instances
A binary datset with with negative… See the full description on the dataset page: https://huggingface.co/datasets/komari6/ajgt_twitter_ar. | 135 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ar",
"license:unknown",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d61 | legacy-datasets/allegro_reviews | legacy-datasets | false | False | 2024-01-09T11:59:39.000Z | 4 | false | 71593d1379934286885c53d147bc863ffe830745 |
Dataset Card for [Dataset Name]
Dataset Summary
Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review).
We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden. You can evaluate your… See the full description on the dataset page: https://huggingface.co/datasets/legacy-datasets/allegro_reviews. | 124 | allegro-reviews | [
"task_categories:text-classification",
"task_ids:sentiment-scoring",
"task_ids:text-scoring",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d62 | tblard/allocine | tblard | false | False | 2024-01-09T12:02:24.000Z | 10 | false | a4654f4896408912913a62ace89614879a549287 |
Dataset Card for Allociné
Dataset Summary
The Allociné dataset is a French-language dataset for sentiment analysis. The texts are movie reviews written between 2006 and 2020 by members of the Allociné.fr community for various films. It contains 100k positive and 100k negative reviews divided into train (160k), validation (20k), and test (20k).
Supported Tasks and Leaderboards
text-classification, sentiment-classification: The dataset can be used… See the full description on the dataset page: https://huggingface.co/datasets/tblard/allocine. | 342 | allocine | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:fr",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d63 | mutiyama/alt | mutiyama | false | False | 2024-01-09T12:07:24.000Z | 16 | false | afbd92e198bbcf17f660e03076fd2938f5a4bbb2 |
Dataset Card for Asian Language Treebank (ALT)
Dataset Summary
The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under ASEAN IVO as described in this Web page.
The process of building ALT began… See the full description on the dataset page: https://huggingface.co/datasets/mutiyama/alt. | 156 | alt | [
"task_categories:translation",
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"multilinguality:translation",
"source_datasets:original",
"language:bn",
"language:en",
"language:fil",
"language:hi",
"language:id",
"language:ja",
"language:km",
"language:lo",
"language:ms",
"language:my",
"language:th",
"language:vi",
"language:zh",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d64 | fancyzhx/amazon_polarity | fancyzhx | false | False | 2024-01-09T12:23:33.000Z | 38 | false | 9d9c45c18f8c3cf1b23a3c27917b60cbf28f3289 |
Dataset Card for Amazon Review Polarity
Dataset Summary
The Amazon reviews dataset consists of reviews from amazon.
The data span a period of 18 years, including ~35 million reviews up to March 2013.
Reviews include product and user information, ratings, and a plaintext review.
Supported Tasks and Leaderboards
text-classification, sentiment-classification: The dataset is mainly used for text classification: given the content and the title, predict… See the full description on the dataset page: https://huggingface.co/datasets/fancyzhx/amazon_polarity. | 458 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:1509.01626",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d65 | defunct-datasets/amazon_reviews_multi | defunct-datasets | false | False | 2023-11-02T14:52:21.000Z | 95 | false | b6115b04af1d02b3c30849bdd4c55899bff0ae63 | We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language. | 90 | null | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:multilingual",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:ja",
"language:zh",
"license:other",
"size_categories:100K<n<1M",
"arxiv:2010.02573",
"region:us"
] | 2022-03-02T23:29:22.000Z | @inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
} |
|
621ffdd236468d709f181d66 | defunct-datasets/amazon_us_reviews | defunct-datasets | false | False | 2023-11-02T14:57:03.000Z | 68 | false | e1bfd57e2da5dc7dc4c748eb4a4a112c71e85162 | Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written. | 118 | null | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
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"source_datasets:original",
"language:en",
"license:other",
"size_categories:100M<n<1B",
"region:us"
] | 2022-03-02T23:29:22.000Z | \ |
|
621ffdd236468d709f181d67 | sewon/ambig_qa | sewon | false | False | 2024-01-09T12:27:07.000Z | 7 | false | e969d0132f4dd28c2939d55be34f1788c00ccfe7 |
Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions
Dataset Summary
AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with
14,042 annotations on NQ-OPEN questions… See the full description on the dataset page: https://huggingface.co/datasets/sewon/ambig_qa. | 699 | ambigqa | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|natural_questions",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2004.10645",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d68 | nala-cub/americas_nli | nala-cub | false | False | 2024-01-23T09:18:27.000Z | 3 | false | 1f3f4fa57acb59b2f352031de45ba08227d972c0 |
Dataset Card for AmericasNLI
Dataset Summary
AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a… See the full description on the dataset page: https://huggingface.co/datasets/nala-cub/americas_nli. | 1,429 | null | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"source_datasets:extended|xnli",
"language:ay",
"language:bzd",
"language:cni",
"language:gn",
"language:hch",
"language:nah",
"language:oto",
"language:qu",
"language:shp",
"language:tar",
"license:cc-by-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2104.08726",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d69 | legacy-datasets/ami | legacy-datasets | false | False | 2024-01-18T11:01:45.000Z | 14 | false | 81c6507a5cead40db13e77610fdcdf5c0f6261e4 | The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
synchronized to a common timeline. These include close-talking and far-field microphones, individual and
room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,
the participants also have unsynchronized pens available to them that record what is written. The meetings
were recorded in English using three different rooms with different acoustic properties, and include mostly
non-native speakers. \n | 128 | null | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"region:us"
] | 2022-03-02T23:29:22.000Z | @inproceedings{10.1007/11677482_3,
author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain and Post, Wilfried and Reidsma, Dennis and Wellner, Pierre},
title = {The AMI Meeting Corpus: A Pre-Announcement},
year = {2005},
isbn = {3540325492},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/11677482_3},
doi = {10.1007/11677482_3},
abstract = {The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting
recordings. It is being created in the context of a project that is developing meeting
browsing technology and will eventually be released publicly. Some of the meetings
it contains are naturally occurring, and some are elicited, particularly using a scenario
in which the participants play different roles in a design team, taking a design project
from kick-off to completion over the course of a day. The corpus is being recorded
using a wide range of devices including close-talking and far-field microphones, individual
and room-view video cameras, projection, a whiteboard, and individual pens, all of
which produce output signals that are synchronized with each other. It is also being
hand-annotated for many different phenomena, including orthographic transcription,
discourse properties such as named entities and dialogue acts, summaries, emotions,
and some head and hand gestures. We describe the data set, including the rationale
behind using elicited material, and explain how the material is being recorded, transcribed
and annotated.},
booktitle = {Proceedings of the Second International Conference on Machine Learning for Multimodal Interaction},
pages = {28–39},
numpages = {12},
location = {Edinburgh, UK},
series = {MLMI'05}
} |
|
621ffdd236468d709f181d6a | gavinxing/amttl | gavinxing | false | False | 2024-01-09T12:28:18.000Z | 2 | false | 271a5aa99e75e936e334b3c52ec178f08bced629 |
Dataset Card for AMTTL
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… See the full description on the dataset page: https://huggingface.co/datasets/gavinxing/amttl. | 53 | null | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:zh",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d6b | facebook/anli | facebook | false | False | 2023-12-21T15:34:02.000Z | 33 | false | 8e4813d81f46d313dac7892e1c28076917cfcdf9 |
Dataset Card for "anli"
Dataset Summary
The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
ANLI is much more difficult than its predecessors including SNLI and MNLI.
It contains three rounds. Each round has train/dev/test splits.
Supported Tasks and Leaderboards
More Information Needed
Languages… See the full description on the dataset page: https://huggingface.co/datasets/facebook/anli. | 6,777 | anli | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"source_datasets:extended|hotpot_qa",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1910.14599",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d6c | sealuzh/app_reviews | sealuzh | false | False | 2024-01-09T12:30:17.000Z | 24 | false | 9eaa95f66364367e8752b0f34c00f67aafa95d15 |
Dataset Card for [Dataset Name]
Dataset Summary
It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches)
Supported… See the full description on the dataset page: https://huggingface.co/datasets/sealuzh/app_reviews. | 177 | null | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentiment-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d6d | deepmind/aqua_rat | deepmind | false | False | 2024-01-09T12:33:06.000Z | 40 | false | 33301c6a050c96af81f63cad5562cb5363e88971 |
Dataset Card for AQUA-RAT
Dataset Summary
A large-scale dataset consisting of approximately 100,000 algebraic word problems.
The solution to each question is explained step-by-step using natural language.
This data is used to train a program generation model that learns to generate the explanation,
while generating the program that solves the question.
Supported Tasks and Leaderboards
Languages
en
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/aqua_rat. | 936 | aqua-rat | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1705.04146",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d6e | google-research-datasets/aquamuse | google-research-datasets | false | False | 2024-01-09T12:36:37.000Z | 12 | false | 84df3ebd8bfe31e2875d242300161ea64ac2b06b |
Dataset Card for AQuaMuSe
Dataset Summary
AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)
This dataset contains versions of automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in AQuaMuSe… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/aquamuse. | 53 | aquamuse | [
"task_categories:other",
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:extended|natural_questions",
"source_datasets:extended|other-Common-Crawl",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2010.12694",
"region:us",
"query-based-multi-document-summarization"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d6f | bigIR/ar_cov19 | bigIR | false | False | 2023-09-19T06:52:17.000Z | 1 | false | 447b2a5a20c9e8ffaee0f14b31697be7b0dec403 | ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others | 86 | arcov-19 | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ar",
"size_categories:1M<n<10M",
"arxiv:2004.05861",
"region:us",
"data-mining"
] | 2022-03-02T23:29:22.000Z | @article{haouari2020arcov19,
title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks},
author={Fatima Haouari and Maram Hasanain and Reem Suwaileh and Tamer Elsayed},
journal={arXiv preprint arXiv:2004.05861},
year={2020} |
|
621ffdd236468d709f181d70 | hadyelsahar/ar_res_reviews | hadyelsahar | false | False | 2024-01-09T12:38:13.000Z | 5 | false | d51bf2435d030e0041344f576c5e8d7154828977 |
Dataset Card for ArRestReviews
Dataset Summary
Dataset of 8364 restaurant reviews from qaym.com in Arabic for sentiment analysis
Supported Tasks and Leaderboards
[More Information Needed]
Languages
The dataset is based on Arabic.
Dataset Structure
Data Instances
A typical data point comprises of the following:
"polarity": which is a string value of either 0 or 1 indicating the sentiment around the review… See the full description on the dataset page: https://huggingface.co/datasets/hadyelsahar/ar_res_reviews. | 86 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ar",
"license:unknown",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d71 | iabufarha/ar_sarcasm | iabufarha | false | False | 2024-01-09T12:42:05.000Z | 12 | false | 557bf94ac6177cc442f42d0b09b6e4b76e8f47c9 |
Dataset Card for ArSarcasm
Dataset Summary
ArSarcasm is a new Arabic sarcasm detection dataset.
The dataset was created using previously available Arabic sentiment analysis
datasets (SemEval 2017
and ASTD) and adds sarcasm and
dialect labels to them.
The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic.
For more details, please check the paper
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset
Supported Tasks… See the full description on the dataset page: https://huggingface.co/datasets/iabufarha/ar_sarcasm. | 89 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|other-semeval_2017",
"source_datasets:extended|other-astd",
"language:ar",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"sarcasm-detection"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d72 | abuelkhair-corpus/arabic_billion_words | abuelkhair-corpus | false | False | 2024-01-18T11:01:47.000Z | 22 | false | c948146dc6e63d56b3469be209ea7e35a4ed5579 | Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
It contains over a billion and a half words in total, out of which, there are about three million unique words.
The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
Also it was marked with two mark-up languages, namely: SGML, and XML. | 196 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ar",
"license:unknown",
"size_categories:100K<n<1M",
"arxiv:1611.04033",
"region:us"
] | 2022-03-02T23:29:22.000Z | @article{el20161,
title={1.5 billion words arabic corpus},
author={El-Khair, Ibrahim Abu},
journal={arXiv preprint arXiv:1611.04033},
year={2016}
} |
|
621ffdd236468d709f181d73 | QCRI/arabic_pos_dialect | QCRI | false | False | 2024-01-09T12:43:34.000Z | 7 | false | 897e2cecae33a242f5003922d3f1564f0c55c3dd |
Dataset Card for Arabic POS Dialect
Dataset Summary
This dataset was created to support part of speech (POS) tagging in dialects of Arabic. It contains sets of 350 manually segmented and POS tagged tweets for each of four dialects: Egyptian, Levantine, Gulf, and Maghrebi.
Supported Tasks and Leaderboards
The dataset can be used to train a model for Arabic token segmentation and part of speech tagging in Arabic dialects. Success on this task is… See the full description on the dataset page: https://huggingface.co/datasets/QCRI/arabic_pos_dialect. | 54 | null | [
"task_categories:token-classification",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:extended",
"language:ar",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1708.05891",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d74 | halabi2016/arabic_speech_corpus | halabi2016 | false | False | 2024-08-14T14:21:32.000Z | 24 | false | a66b1d6ba1c5cc79570bffcd4d83b9ce566db2b4 | This Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton.
The corpus was recorded in south Levantine Arabic
(Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice.
Note that in order to limit the required storage for preparing this dataset, the audio
is stored in the .flac format and is not converted to a float32 array. To convert, the audio
file to a float32 array, please make use of the `.map()` function as follows:
```python
import soundfile as sf
def map_to_array(batch):
speech_array, _ = sf.read(batch["file"])
batch["speech"] = speech_array
return batch
dataset = dataset.map(map_to_array, remove_columns=["file"])
``` | 131 | arabic-speech-corpus | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:ar",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"region:us"
] | 2022-03-02T23:29:22.000Z | @phdthesis{halabi2016modern,
title={Modern standard Arabic phonetics for speech synthesis},
author={Halabi, Nawar},
year={2016},
school={University of Southampton}
} |
|
621ffdd236468d709f181d75 | hsseinmz/arcd | hsseinmz | false | False | 2024-01-09T12:44:24.000Z | 5 | false | cc6906b6eda547e4ffc63b8d88ccca7e0515187a |
Dataset Card for "arcd"
Dataset Summary
Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
plain_text
Size of downloaded dataset files: 1.94 MB
Size of the generated dataset: 1.70 MB
Total amount… See the full description on the dataset page: https://huggingface.co/datasets/hsseinmz/arcd. | 297 | arcd | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:ar",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d76 | ramybaly/arsentd_lev | ramybaly | false | False | 2024-01-18T11:01:50.000Z | 3 | false | ce4d032917566e486a90330392bc7853280e7249 | The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV) contains 4,000 tweets written in Arabic and equally retrieved from Jordan, Lebanon, Palestine and Syria. | 34 | arsentd-lev | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:apc",
"language:ajp",
"license:other",
"size_categories:1K<n<10K",
"arxiv:1906.01830",
"region:us"
] | 2022-03-02T23:29:22.000Z | @article{ArSenTDLev2018,
title={ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets},
author={Baly, Ramy, and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Bashir Shaban, Khaled},
journal={OSACT3},
pages={},
year={2018}} |
|
621ffdd236468d709f181d77 | allenai/art | allenai | false | False | 2024-01-09T12:45:10.000Z | 5 | false | df6c96ba77462a86dc1cf530c12a69da47ea42e7 |
Dataset Card for "art"
Dataset Summary
ART consists of over 20k commonsense narrative contexts and 200k explanations.
The Abductive Natural Language Inference Dataset from AI2.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
anli
Size of downloaded dataset files: 5.12 MB
Size of the generated dataset: 34.36 MB… See the full description on the dataset page: https://huggingface.co/datasets/allenai/art. | 30 | art-dataset | [
"task_categories:multiple-choice",
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1908.05739",
"region:us",
"abductive-natural-language-inference"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d78 | arxiv-community/arxiv_dataset | arxiv-community | false | False | 2024-01-18T11:01:52.000Z | 84 | false | c70944cb158dcdab8a5403b1fa20f28119f701a6 | A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces. | 435 | null | [
"task_categories:translation",
"task_categories:summarization",
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:explanation-generation",
"task_ids:fact-checking-retrieval",
"task_ids:text-simplification",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"size_categories:1M<n<10M",
"arxiv:1905.00075",
"region:us"
] | 2022-03-02T23:29:22.000Z | @misc{clement2019arxiv,
title={On the Use of ArXiv as a Dataset},
author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
year={2019},
eprint={1905.00075},
archivePrefix={arXiv},
primaryClass={cs.IR}
} |
|
621ffdd236468d709f181d79 | tuanphong/ascent_kb | tuanphong | false | False | 2024-01-09T14:44:26.000Z | 2 | false | 9157196d77890cf20b57075353813b34dba3426e |
Dataset Card for Ascent KB
Dataset Summary
This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline developed at the Max Planck Institute for Informatics.
The focus of this dataset is on everyday concepts such as elephant, car, laptop, etc.
The current version of Ascent KB (v1.0.0) is approximately 19 times larger than ConceptNet (note that, in this comparison, non-commonsense knowledge in ConceptNet such as lexical relations is… See the full description on the dataset page: https://huggingface.co/datasets/tuanphong/ascent_kb. | 15 | ascentkb | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2011.00905",
"region:us",
"knowledge-base"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d7a | achrafothman/aslg_pc12 | achrafothman | false | False | 2024-01-09T12:45:54.000Z | 4 | false | cb7cd272db8fcd4004ee04ddf50e194c15ea24d6 |
Dataset Card for "aslg_pc12"
Dataset Summary
Synthetic English-ASL Gloss Parallel Corpus 2012
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
default
Size of downloaded dataset files: 12.77 MB
Size of the generated dataset: 13.50 MB
Total amount of disk used: 26.27 MB
An example of 'train' looks as follows.
{… See the full description on the dataset page: https://huggingface.co/datasets/achrafothman/aslg_pc12. | 16 | aslg-pc12 | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"source_datasets:original",
"language:ase",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d7b | AmazonScience/asnq | AmazonScience | false | False | 2024-01-09T15:33:53.000Z | 1 | false | 32291fc9663b9ee88abb97114e52501bdd58a129 |
Dataset Card for "asnq"
Dataset Summary
ASNQ is a dataset for answer sentence selection derived from
Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
Each example contains a question, candidate sentence, label indicating whether or not
the sentence answers the question, and two additional features --
sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the
candidate sentence is contained in the long_answer and if the… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/asnq. | 14 | asnq | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|natural_questions",
"language:en",
"license:cc-by-nc-sa-3.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:1911.04118",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d7c | facebook/asset | facebook | false | False | 2023-12-21T15:41:23.000Z | 10 | false | c7f2fa4bae55ae656091805d4416c1374582bb4e |
Dataset Card for ASSET
Dataset Summary
ASSET (Alva-Manchego et al., 2020) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from TurkCorpus (Xu et al., 2016) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence
splitting in HSplit), the… See the full description on the dataset page: https://huggingface.co/datasets/facebook/asset. | 55 | asset | [
"task_categories:text-classification",
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"source_datasets:extended|other-turkcorpus",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"simplification-evaluation"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d7d | nilc-nlp/assin | nilc-nlp | false | False | 2024-01-09T12:47:28.000Z | 9 | false | 6535e48351178e07ade013b05b69f0e35cb28bbb |
Dataset Card for ASSIN
Dataset Summary
The ASSIN (Avaliação de Similaridade Semântica e INferência textual) corpus is a corpus annotated with pairs of sentences written in
Portuguese that is suitable for the exploration of textual entailment and paraphrasing classifiers. The corpus contains pairs of sentences
extracted from news articles written in European Portuguese (EP) and Brazilian Portuguese (BP), obtained from Google News Portugal
and Brazil… See the full description on the dataset page: https://huggingface.co/datasets/nilc-nlp/assin. | 193 | assin | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:pt",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d7e | nilc-nlp/assin2 | nilc-nlp | false | False | 2024-01-09T12:48:38.000Z | 12 | false | 0ff9c86779e06855536d8775ce5550550e1e5a2d |
Dataset Card for ASSIN 2
Dataset Summary
The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1.
The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese,
annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5, and text entailment
classes are either entailment or none. The test data are composed of approximately… See the full description on the dataset page: https://huggingface.co/datasets/nilc-nlp/assin2. | 472 | assin2 | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:pt",
"license:unknown",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d7f | allenai/atomic | allenai | false | False | 2024-01-18T11:01:54.000Z | 12 | false | a6ea1d221fa3a5c953b1e69f2594816046bb57c7 | This dataset provides the template sentences and
relationships defined in the ATOMIC common sense dataset. There are
three splits - train, test, and dev.
From the authors.
Disclaimer/Content warning: the events in atomic have been
automatically extracted from blogs, stories and books written at
various times. The events might depict violent or problematic actions,
which we left in the corpus for the sake of learning the (probably
negative but still important) commonsense implications associated with
the events. We removed a small set of truly out-dated events, but
might have missed some so please email us (msap@cs.washington.edu) if
you have any concerns. | 51 | atomic | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"region:us",
"common-sense-if-then-reasoning"
] | 2022-03-02T23:29:22.000Z | @article{Sap2019ATOMICAA,
title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning},
author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi},
journal={ArXiv},
year={2019},
volume={abs/1811.00146}
} |
|
621ffdd236468d709f181d80 | nwu-ctext/autshumato | nwu-ctext | false | False | 2024-01-18T11:01:55.000Z | 3 | false | d1951a019d5dedcb8ce47f55bce6328d31f69956 | Multilingual information access is stipulated in the South African constitution. In practise, this
is hampered by a lack of resources and capacity to perform the large volumes of translation
work required to realise multilingual information access. One of the aims of the Autshumato
project is to develop machine translation systems for three South African languages pairs. | 84 | null | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:en",
"language:tn",
"language:ts",
"language:zu",
"license:cc-by-2.5",
"size_categories:100K<n<1M",
"region:us"
] | 2022-03-02T23:29:22.000Z | @article{groenewald2010processing,
title={Processing parallel text corpora for three South African language pairs in the Autshumato project},
author={Groenewald, Hendrik J and du Plooy, Liza},
journal={AfLaT 2010},
pages={27},
year={2010}
} |
|
621ffdd236468d709f181d81 | facebook/babi_qa | facebook | false | False | 2023-01-25T14:26:58.000Z | 6 | false | 021d7aeb7307b7856dd0632f92827bc607dc2f1b | The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading
comprehension via question answering. Our tasks measure understanding
in several ways: whether a system is able to answer questions via chaining facts,
simple induction, deduction and many more. The tasks are designed to be prerequisites
for any system that aims to be capable of conversing with a human.
The aim is to classify these tasks into skill sets,so that researchers
can identify (and then rectify)the failings of their systems. | 61 | babi-1 | [
"task_categories:question-answering",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"size_categories:10K<n<100K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:1502.05698",
"arxiv:1511.06931",
"region:us",
"chained-qa"
] | 2022-03-02T23:29:22.000Z | @misc{weston2015aicomplete,
title={Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks},
author={Jason Weston and Antoine Bordes and Sumit Chopra and Alexander M. Rush and Bart van Merriënboer and Armand Joulin and Tomas Mikolov},
year={2015},
eprint={1502.05698},
archivePrefix={arXiv},
primaryClass={cs.AI}
} |
|
621ffdd236468d709f181d82 | legacy-datasets/banking77 | legacy-datasets | false | False | 2024-01-10T08:23:17.000Z | 39 | false | f54121560de48f2852f90be299010d1d6dc612ec |
Dataset Card for BANKING77
Dataset Summary
Deprecated: Dataset "banking77" is deprecated and will be deleted. Use "PolyAI/banking77" instead.
Dataset composed of online banking queries annotated with their corresponding intents.
BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
It comprises 13,083 customer service queries labeled with 77 intents.
It focuses on fine-grained single-domain intent detection.
Supported… See the full description on the dataset page: https://huggingface.co/datasets/legacy-datasets/banking77. | 623 | null | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2003.04807",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d83 | phiwi/bbaw_egyptian | phiwi | false | False | 2024-01-10T08:24:41.000Z | 6 | false | f9dde1200348af9b531e8fd09096bd9f9ddfeb34 |
Dataset Card for "bbaw_egyptian"
Dataset Summary
This dataset comprises parallel sentences of hieroglyphic encodings, transcription and translation as used in the paper Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyph. The data triples are extracted from the digital corpus of Egyptian texts compiled by the project "Strukturen und Transformationen des Wortschatzes der ägyptischen Sprache".
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/phiwi/bbaw_egyptian. | 11 | null | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:extended|wikipedia",
"language:egy",
"language:de",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d84 | midas/bbc_hindi_nli | midas | false | False | 2024-01-10T10:00:44.000Z | 2 | false | bca982bebdd497ab9078feda251111aac4874318 |
Dataset Card for BBC Hindi NLI Dataset
Dataset Summary
Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
Context and Hypothesis is written in Hindi while Entailment_Label is in English.
Entailment_label is of 2 types - entailed and not-entailed.
Dataset can be used to train models for Natural Language Inference… See the full description on the dataset page: https://huggingface.co/datasets/midas/bbc_hindi_nli. | 14 | null | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|bbc__hindi_news_classification",
"language:hi",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d85 | spyysalo/bc2gm_corpus | spyysalo | false | False | 2024-01-10T10:03:04.000Z | 6 | false | dc0640510665bb3de7c88416ede4708cf6481b61 |
Dataset Card for bc2gm_corpus
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
id: Sentence identifier.
tokens: Array of tokens composing a sentence.
ner_tags: Array of tags, where 0 indicates no disease mentioned, 1 signals the… See the full description on the dataset page: https://huggingface.co/datasets/spyysalo/bc2gm_corpus. | 104 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d86 | AI-Lab-Makerere/beans | AI-Lab-Makerere | false | False | 2024-01-03T12:06:51.000Z | 30 | false | 27aa014ce09b193e1a6f58112d4a66e0eddb69c5 |
Dataset Card for Beans
Dataset Summary
Beans leaf dataset with images of diseased and health leaves.
Supported Tasks and Leaderboards
image-classification: Based on a leaf image, the goal of this task is to predict the disease type (Angular Leaf Spot and Bean Rust), if any.
Languages
English
Dataset Structure
Data Instances
A sample from the training set is provided below:
{
'image_file_path':… See the full description on the dataset page: https://huggingface.co/datasets/AI-Lab-Makerere/beans. | 1,107 | null | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d87 | nectec/best2009 | nectec | false | False | 2024-01-10T10:08:29.000Z | 0 | false | 685fffc4105dda00888f127d586c378bf6fa995e |
Dataset Card for best2009
Dataset Summary
best2009 is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by NECTEC (148,995/2,252 lines of train/test). It was created for BEST 2010: Word Tokenization Competition. The test set answers are not provided publicly.
Supported Tasks and Leaderboards
word tokenization
Languages
Thai
Dataset Structure
Data Instances
{'char': ['?', 'ภ'… See the full description on the dataset page: https://huggingface.co/datasets/nectec/best2009. | 43 | null | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:th",
"license:cc-by-nc-sa-3.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"word-tokenization"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d88 | Helsinki-NLP/bianet | Helsinki-NLP | false | False | 2024-02-23T15:06:25.000Z | 1 | false | 48a45ed77f0604997882ea2f8202b765f2d0e8b1 |
Dataset Card for Bianet
Dataset Summary
A new open-source parallel corpus consisting of news articles collected from the Bianet magazine, an online newspaper that
publishes Turkish news, often along with their translations in English and Kurdish.
A parallel news corpus in Turkish, Kurdish and English;
Bianet collects 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper.
Bianet's Numbers:
Languages: 3… See the full description on the dataset page: https://huggingface.co/datasets/Helsinki-NLP/bianet. | 25 | bianet | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"source_datasets:original",
"language:en",
"language:ku",
"language:tr",
"license:cc-by-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1805.05095",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d89 | Helsinki-NLP/bible_para | Helsinki-NLP | false | False | 2024-01-18T11:01:58.000Z | 14 | false | 0a2c121b0224b552e05f281fc71c55e3180b3d00 | This is a multilingual parallel corpus created from translations of the Bible compiled by Christos Christodoulopoulos and Mark Steedman.
102 languages, 5,148 bitexts
total number of files: 107
total number of tokens: 56.43M
total number of sentence fragments: 2.84M | 33 | null | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:acu",
"language:af",
"language:agr",
"language:ake",
"language:am",
"language:amu",
"language:ar",
"language:bg",
"language:bsn",
"language:cak",
"language:ceb",
"language:ch",
"language:chq",
"language:chr",
"language:cjp",
"language:cni",
"language:cop",
"language:crp",
"language:cs",
"language:da",
"language:de",
"language:dik",
"language:dje",
"language:djk",
"language:dop",
"language:ee",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fi",
"language:fr",
"language:gbi",
"language:gd",
"language:gu",
"language:gv",
"language:he",
"language:hi",
"language:hr",
"language:hu",
"language:hy",
"language:id",
"language:is",
"language:it",
"language:ja",
"language:jak",
"language:jiv",
"language:kab",
"language:kbh",
"language:kek",
"language:kn",
"language:ko",
"language:la",
"language:lt",
"language:lv",
"language:mam",
"language:mi",
"language:ml",
"language:mr",
"language:my",
"language:ne",
"language:nhg",
"language:nl",
"language:no",
"language:ojb",
"language:pck",
"language:pes",
"language:pl",
"language:plt",
"language:pot",
"language:ppk",
"language:pt",
"language:quc",
"language:quw",
"language:ro",
"language:rom",
"language:ru",
"language:shi",
"language:sk",
"language:sl",
"language:sn",
"language:so",
"language:sq",
"language:sr",
"language:ss",
"language:sv",
"language:syr",
"language:te",
"language:th",
"language:tl",
"language:tmh",
"language:tr",
"language:uk",
"language:usp",
"language:vi",
"language:wal",
"language:wo",
"language:xh",
"language:zh",
"language:zu",
"license:cc0-1.0",
"size_categories:10K<n<100K",
"region:us"
] | 2022-03-02T23:29:22.000Z | OPUS and A massively parallel corpus: the Bible in 100 languages, Christos Christodoulopoulos and Mark Steedman, *Language Resources and Evaluation*, 49 (2) |
|
621ffdd236468d709f181d8a | NortheasternUniversity/big_patent | NortheasternUniversity | false | False | 2024-01-18T11:01:59.000Z | 54 | false | e807b1d5492aa5f4fac08f3f6c7c85c72887ca12 | BIGPATENT, consisting of 1.3 million records of U.S. patent documents
along with human written abstractive summaries.
Each US patent application is filed under a Cooperative Patent Classification
(CPC) code. There are nine such classification categories:
A (Human Necessities), B (Performing Operations; Transporting),
C (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),
F (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),
G (Physics), H (Electricity), and
Y (General tagging of new or cross-sectional technology)
There are two features:
- description: detailed description of patent.
- abstract: Patent abastract. | 89 | bigpatent | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"arxiv:1906.03741",
"region:us",
"patent-summarization"
] | 2022-03-02T23:29:22.000Z | @misc{sharma2019bigpatent,
title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},
author={Eva Sharma and Chen Li and Lu Wang},
year={2019},
eprint={1906.03741},
archivePrefix={arXiv},
primaryClass={cs.CL}
} |
|
621ffdd236468d709f181d8b | FiscalNote/billsum | FiscalNote | false | False | 2024-03-27T16:01:38.000Z | 38 | false | 3d8510441c06a3d9dfb32eb0d7f80151730bcc4f |
Dataset Card for "billsum"
Dataset Summary
BillSum, summarization of US Congressional and California state bills.
There are several features:
text: bill text.
summary: summary of the bills.
title: title of the bills.
features for us bills. ca bills does not have.
text_len: number of chars in text.
sum_len: number of chars in summary.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed… See the full description on the dataset page: https://huggingface.co/datasets/FiscalNote/billsum. | 351 | billsum | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1910.00523",
"region:us",
"bills-summarization"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d8c | microsoft/bing_coronavirus_query_set | microsoft | false | False | 2024-01-10T10:17:05.000Z | 0 | false | 77f70b572508c4571927c95e3b9bec64e4275d39 |
Dataset Card for BingCoronavirusQuerySet
Dataset Summary
Please note that you can specify the start and end date of the data. You can get start and end dates from here: https://github.com/microsoft/BingCoronavirusQuerySet/tree/master/data/2020
example:
load_dataset("bing_coronavirus_query_set", queries_by="state", start_date="2020-09-01", end_date="2020-09-30")
You can also load the data by country by using queries_by="country".
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/bing_coronavirus_query_set. | 14 | null | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d8d | nlpaueb/biomrc | nlpaueb | false | False | 2024-01-18T11:02:01.000Z | 4 | false | 5bb4def0bfa1570a933f18af2d8c13c22c2e2b94 | We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard. | 26 | biomrc | [
"language:en",
"region:us"
] | 2022-03-02T23:29:22.000Z | @inproceedings{pappas-etal-2020-biomrc,
title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
author = "Pappas, Dimitris and
Stavropoulos, Petros and
Androutsopoulos, Ion and
McDonald, Ryan",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.bionlp-1.15",
pages = "140--149",
abstract = "We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.",
} |
|
621ffdd236468d709f181d8e | tabilab/biosses | tabilab | false | False | 2024-01-10T10:20:02.000Z | 5 | false | 2394a2eda8dae34a30f68f0770775fd5c2e863bd |
Dataset Card for BIOSSES
Dataset Summary
BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset containing articles from the biomedical domain. The sentence pairs in BIOSSES were selected from citing sentences, i.e. sentences that have a citation to a reference article.
The… See the full description on the dataset page: https://huggingface.co/datasets/tabilab/biosses. | 14 | biosses | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d8f | TheBritishLibrary/blbooks | TheBritishLibrary | false | False | 2024-08-08T06:15:12.000Z | 14 | false | de11fed4c2a3bfb17a750347db93da52d9fa58c4 | A dataset comprising of text created by OCR from the 49,455 digitised books, equating to 65,227 volumes (25+ million pages), published between c. 1510 - c. 1900.
The books cover a wide range of subject areas including philosophy, history, poetry and literature. | 69 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:other",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"license:cc0-1.0",
"size_categories:100K<n<1M",
"region:us",
"digital-humanities-research"
] | 2022-03-02T23:29:22.000Z | @misc{BritishLibraryBooks2021,
author = {British Library Labs},
title = {Digitised Books. c. 1510 - c. 1900. JSONL (OCR derived text + metadata)},
year = {2021},
publisher = {British Library},
howpublished={https://doi.org/10.23636/r7w6-zy15} |
|
621ffdd236468d709f181d90 | TheBritishLibrary/blbooksgenre | TheBritishLibrary | false | False | 2023-06-01T14:59:51.000Z | 4 | false | de087348b4ef8c44c2978f8ff819e9e3862089e6 | This dataset contains metadata for resources belonging to the British Library’s digitised printed books (18th-19th century) collection (bl.uk/collection-guides/digitised-printed-books).
This metadata has been extracted from British Library catalogue records.
The metadata held within our main catalogue is updated regularly.
This metadata dataset should be considered a snapshot of this metadata. | 31 | null | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:topic-classification",
"task_ids:multi-label-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:de",
"language:en",
"language:fr",
"language:nl",
"license:cc0-1.0",
"size_categories:10K<n<100K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2022-03-02T23:29:22.000Z | @misc{british library_genre,
title={ 19th Century Books - metadata with additional crowdsourced annotations},
url={https://doi.org/10.23636/BKHQ-0312},
author={{British Library} and Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annelies and Wollner, Ildi},
year={2021}} |
|
621ffdd236468d709f181d91 | ParlAI/blended_skill_talk | ParlAI | false | False | 2024-01-10T10:22:26.000Z | 65 | false | d7b0093243439fa5f0cd9663125cc47575ced2ea |
Dataset Card for "blended_skill_talk"
Dataset Summary
A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
default
Size of downloaded dataset files: 38.11 MB
Size… See the full description on the dataset page: https://huggingface.co/datasets/ParlAI/blended_skill_talk. | 391 | blended-skill-talk | [
"task_ids:dialogue-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2004.08449",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d92 | nyu-mll/blimp | nyu-mll | false | False | 2024-01-23T09:58:08.000Z | 33 | false | 877fba0801ffb7cbd8c39c1ff314a46f053f6036 |
Dataset Card for "blimp"
Dataset Summary
BLiMP is a challenge set for evaluating what language models (LMs) know about
major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each
containing 1000 minimal pairs isolating specific contrasts in syntax,
morphology, or semantics. The data is automatically generated according to
expert-crafted grammars.
Supported Tasks and Leaderboards
More Information Needed
Languages… See the full description on the dataset page: https://huggingface.co/datasets/nyu-mll/blimp. | 599 | blimp | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1912.00582",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d93 | barilan/blog_authorship_corpus | barilan | false | False | 2023-06-06T16:16:13.000Z | 11 | false | 728947f6c98ade87aa396004440cb3b58f173cb8 | The Blog Authorship Corpus consists of the collected posts of 19,320 bloggers gathered from blogger.com in August 2004. The corpus incorporates a total of 681,288 posts and over 140 million words - or approximately 35 posts and 7250 words per person.
Each blog is presented as a separate file, the name of which indicates a blogger id# and the blogger’s self-provided gender, age, industry and astrological sign. (All are labeled for gender and age but for many, industry and/or sign is marked as unknown.)
All bloggers included in the corpus fall into one of three age groups:
- 8240 "10s" blogs (ages 13-17),
- 8086 "20s" blogs (ages 23-27),
- 2994 "30s" blogs (ages 33-47).
For each age group there are an equal number of male and female bloggers.
Each blog in the corpus includes at least 200 occurrences of common English words. All formatting has been stripped with two exceptions. Individual posts within a single blogger are separated by the date of the following post and links within a post are denoted by the label urllink.
The corpus may be freely used for non-commercial research purposes. | 776 | blog-authorship-corpus | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"region:us"
] | 2022-03-02T23:29:22.000Z | @inproceedings{schler2006effects,
title={Effects of age and gender on blogging.},
author={Schler, Jonathan and Koppel, Moshe and Argamon, Shlomo and Pennebaker, James W},
booktitle={AAAI spring symposium: Computational approaches to analyzing weblogs},
volume={6},
pages={199--205},
year={2006}
} |
|
621ffdd236468d709f181d94 | rezacsedu/bn_hate_speech | rezacsedu | false | False | 2024-01-10T10:29:39.000Z | 1 | false | 99612296bc093f0720cac7d7cbfcb67eecf1ca2f |
Dataset Card for Bengali Hate Speech Dataset
Dataset Summary
The Bengali Hate Speech Dataset is a Bengali-language dataset of news articles collected from various Bengali media sources and categorized based on the type of hate in the text. The dataset was created to provide greater support for under-resourced languages like Bengali on NLP tasks, and serves as a benchmark for multiple types of classification tasks.
Supported Tasks and Leaderboards… See the full description on the dataset page: https://huggingface.co/datasets/rezacsedu/bn_hate_speech. | 45 | bengali-hate-speech | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:bn",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2004.07807",
"region:us",
"hate-speech-topic-classification"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d95 | bnl-data/bnl_newspapers | bnl-data | false | False | 2024-01-24T16:24:00.000Z | 2 | false | fd671e637acfbe911650fa398ec203f4205d128c |
Dataset Card for BnL Historical Newspapers
Dataset Summary
The BnL has digitised over 800.000 pages of Luxembourg newspapers. This dataset currently has one configuration covering a subset of these newspapers, which sit under the "Processed Datasets" collection. The BNL:
processed all newspapers and monographs that are in the public domain and extracted the full text and associated meta data of every single article, section, advertisement… The result is a large… See the full description on the dataset page: https://huggingface.co/datasets/bnl-data/bnl_newspapers. | 9 | 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:multilingual",
"source_datasets:original",
"language:ar",
"language:da",
"language:de",
"language:fi",
"language:fr",
"language:lb",
"language:nl",
"language:pt",
"license:cc0-1.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d96 | bookcorpus/bookcorpus | bookcorpus | false | False | 2024-05-03T13:48:33.000Z | 256 | false | d917559bbe9cf49c638fc331c37c4bf239e3b637 | Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanations for visual content that go semantically farbeyond the captions available in current datasets. \ | 2,796 | bookcorpus | [
"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",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10M<n<100M",
"arxiv:2105.05241",
"region:us"
] | 2022-03-02T23:29:22.000Z | @InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
} |
|
621ffdd236468d709f181d97 | defunct-datasets/bookcorpusopen | defunct-datasets | false | False | 2023-11-24T14:42:08.000Z | 33 | false | 817f291474dcb4fa865ed7c8298e709cd8a20266 | Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.
This version of bookcorpus has 17868 dataset items (books). Each item contains two fields: title and text. The title is the name of the book (just the file name) while text contains unprocessed book text. The bookcorpus has been prepared by Shawn Presser and is generously hosted by The-Eye. The-Eye is a non-profit, community driven platform dedicated to the archiving and long-term preservation of any and all data including but by no means limited to... websites, books, games, software, video, audio, other digital-obscura and ideas. | 22 | bookcorpus | [
"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",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"arxiv:2105.05241",
"region:us"
] | 2022-03-02T23:29:22.000Z | @InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
} |
|
621ffdd236468d709f181d98 | google/boolq | google | false | False | 2024-01-22T09:16:26.000Z | 58 | false | 35b264d03638db9f4ce671b711558bf7ff0f80d5 |
Dataset Card for Boolq
Dataset Summary
BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair classification setup is similar to existing natural language inference tasks.
Supported Tasks… See the full description on the dataset page: https://huggingface.co/datasets/google/boolq. | 8,805 | boolq | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1905.10044",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d99 | clarin-pl/bprec | clarin-pl | false | False | 2024-01-18T11:02:04.000Z | 0 | false | 45f1ac8242a87d96645e04bd6c1c645c85bf61ed | Dataset consisting of Polish language texts annotated to recognize brand-product relations. | 11 | null | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:pl",
"license:unknown",
"size_categories:1K<n<10K",
"region:us"
] | 2022-03-02T23:29:22.000Z | @inproceedings{inproceedings,
author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka},
year = {2020},
month = {05},
pages = {},
title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations}
} |
|
621ffdd236468d709f181d9a | allenai/break_data | allenai | false | False | 2024-01-11T07:39:12.000Z | 1 | false | 42d29b59a18aec2be0986d24469bf67b6291cb27 |
Dataset Card for "break_data"
Dataset Summary
Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations
(QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases.
This repository contains the Break dataset along with information on the exact data format.
Supported Tasks and Leaderboards
More Information Needed… See the full description on the dataset page: https://huggingface.co/datasets/allenai/break_data. | 20 | break | [
"task_categories:text2text-generation",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d9b | UFRGS/brwac | UFRGS | false | False | 2024-01-18T11:02:06.000Z | 17 | false | 3475bc217e5241f9a5c833b2f8ae9b74a2d7e44d | The BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework,
which was made public for research purposes. The current corpus version, released in January 2017, is composed by
3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource is available
solely for academic research purposes, and you agreed not to use it for any commercial applications.
Manually download at https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC | 30 | brwac | [
"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",
"source_datasets:original",
"language:pt",
"license:unknown",
"size_categories:1M<n<10M",
"region:us"
] | 2022-03-02T23:29:22.000Z | @inproceedings{wagner2018brwac,
title={The brwac corpus: A new open resource for brazilian portuguese},
author={Wagner Filho, Jorge A and Wilkens, Rodrigo and Idiart, Marco and Villavicencio, Aline},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
} |
|
621ffdd236468d709f181d9c | ryo0634/bsd_ja_en | ryo0634 | false | False | 2024-01-11T07:36:44.000Z | 9 | false | ed6539dc16c18c481ff3574376b79d7a83a57fb2 |
Dataset Card for Business Scene Dialogue
Dataset Summary
This is the Business Scene Dialogue (BSD) dataset,
a Japanese-English parallel corpus containing written conversations
in various business scenarios.
The dataset was constructed in 3 steps:
selecting business scenes,
writing monolingual conversation scenarios according to the selected scenes, and
translating the scenarios into the other language.
Half of the monolingual scenarios were written in… See the full description on the dataset page: https://huggingface.co/datasets/ryo0634/bsd_ja_en. | 270 | business-scene-dialogue | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"source_datasets:original",
"language:en",
"language:ja",
"license:cc-by-nc-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"business-conversations-translation"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d9d | community-datasets/bswac | community-datasets | false | False | 2024-01-11T12:54:46.000Z | 0 | false | 1dbdabb101d60471e705f84ae821cdb804399dd7 | The Bosnian web corpus bsWaC was built by crawling the .ba top-level domain in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Bosnian vs. Croatian vs. Serbian).
Version 1.0 of this corpus is described in http://www.aclweb.org/anthology/W14-0405. Version 1.1 contains newer and better linguistic annotations. | 22 | 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",
"source_datasets:original",
"language:bs",
"license:cc-by-sa-3.0",
"size_categories:100M<n<1B",
"region:us"
] | 2022-03-02T23:29:22.000Z | @misc{11356/1062,
title = {Bosnian web corpus {bsWaC} 1.1},
author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
url = {http://hdl.handle.net/11356/1062},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
year = {2016} } |
|
621ffdd236468d709f181d9e | dataset-org/c3 | dataset-org | false | False | 2024-01-11T08:12:46.000Z | 10 | false | 28e91a21a22b95987a90a46cb6d7741c7aad8158 |
Dataset Card for C3
Dataset Summary
Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.… See the full description on the dataset page: https://huggingface.co/datasets/dataset-org/c3. | 40 | c3 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:zh",
"license:other",
"size_categories:10K<n<100K",
"format:parquet",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1904.09679",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181d9f | legacy-datasets/c4 | legacy-datasets | false | False | 2024-03-05T08:44:26.000Z | 229 | false | 21e98d7063e4037e836a0299d7fbb7efd484e6c3 | A colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's C4 dataset by AllenAI. | 59 | c4 | [
"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:multilingual",
"source_datasets:original",
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
"arxiv:1910.10683",
"region:us"
] | 2022-03-02T23:29:22.000Z | @article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
} |
|
621ffdd236468d709f181da0 | china-ai-law-challenge/cail2018 | china-ai-law-challenge | false | False | 2024-01-16T15:08:12.000Z | 13 | false | 775098da3ba75f033781f8061900b62503e9bea0 |
Dataset Card for CAIL 2018
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… See the full description on the dataset page: https://huggingface.co/datasets/china-ai-law-challenge/cail2018. | 24 | chinese-ai-and-law-cail-2018 | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:zh",
"license:unknown",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:1807.02478",
"region:us",
"judgement-prediction"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181da1 | community-datasets/caner | community-datasets | false | False | 2024-01-16T13:38:20.000Z | 1 | false | 4749e1d6950c2377b62a2e424147e68406cca9dd |
Dataset Card for CANER
Dataset Summary
The Classical Arabic Named Entity Recognition corpus is a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.
Supported Tasks and Leaderboards
Named Entity Recognition
Languages
Classical Arabic
Dataset Structure
Data Instances
An example from the dataset:
{'ner_tag': 1, 'token': 'الجامع'}
Where 1 stands… See the full description on the dataset page: https://huggingface.co/datasets/community-datasets/caner. | 11 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:ar",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181da2 | soarescmsa/capes | soarescmsa | false | False | 2024-01-16T10:30:24.000Z | 2 | false | 42c1ec984cc5461418a24fec2cd9ab8c8d4aa99c |
Dataset Card for CAPES
Dataset Summary
A parallel corpus of theses and dissertations abstracts in English and Portuguese were collected from the
CAPES website (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) - Brazil.
The corpus is sentence aligned for all language pairs. Approximately 240,000 documents were
collected and aligned using the Hunalign algorithm.
Supported Tasks and Leaderboards
The underlying task is machine translation.… See the full description on the dataset page: https://huggingface.co/datasets/soarescmsa/capes. | 12 | capes | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:en",
"language:pt",
"license:unknown",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1905.01715",
"region:us",
"dissertation-abstracts-translation",
"theses-translation"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181da3 | kchawla123/casino | kchawla123 | false | False | 2024-01-16T13:53:39.000Z | 4 | false | 290898d2d08b6591db17005504e40ce00ac1028e |
Dataset Card for Casino
Dataset Summary
We provide a novel dataset (referred to as CaSiNo) of 1030 negotiation dialogues. Two participants take the role of campsite neighbors and negotiate for Food, Water, and Firewood packages, based on their individual preferences and requirements. This design keeps the task tractable, while still facilitating linguistically rich and personal conversations. This helps to overcome the limitations of prior negotiation datasets… See the full description on the dataset page: https://huggingface.co/datasets/kchawla123/casino. | 23 | casino | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181da4 | community-datasets/catalonia_independence | community-datasets | false | False | 2024-01-16T13:54:09.000Z | 3 | false | cf24d44e517efa534f048e5fc5981f399ed25bee |
Dataset Card for Catalonia Independence Corpus
Dataset Summary
This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter messages for automatic stance detection. The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia.
Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express… See the full description on the dataset page: https://huggingface.co/datasets/community-datasets/catalonia_independence. | 58 | cic | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:ca",
"language:es",
"license:cc-by-nc-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"stance-detection"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181da5 | microsoft/cats_vs_dogs | microsoft | false | False | 2024-08-08T05:35:11.000Z | 26 | false | b5ae3589204019bc2cc97e99e4914a54589333ef |
Dataset Card for Cats Vs. Dogs
Dataset Summary
A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset.
From the competition page:
The Asirra data set
Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/cats_vs_dogs. | 953 | cats-vs-dogs | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181da6 | community-datasets/cawac | community-datasets | false | False | 2024-01-16T15:50:41.000Z | 0 | false | 7dc6be007333a09f1b5d2474508c43d18551859d |
Dataset Card for caWaC
Dataset Summary
caWaC is a 780-million-token web corpus of Catalan built from the .cat top-level-domain in late 2013.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
Dataset is monolingual in Catalan language.
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
[More Information Needed]
Data Splits
[More… See the full description on the dataset page: https://huggingface.co/datasets/community-datasets/cawac. | 8 | cawac | [
"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",
"source_datasets:original",
"language:ca",
"license:cc-by-sa-3.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181da7 | cam-cst/cbt | cam-cst | false | False | 2024-01-16T16:01:16.000Z | 12 | false | 72b5c46b1248e3316360f0f2f0b2c39e773b68e4 |
Dataset Card for CBT
Dataset Summary
The Children’s Book Test (CBT) is designed to measure directly how well language models can exploit wider linguistic context. The CBT is built from books that are freely available.
This dataset contains four different configurations:
V: where the answers to the questions are verbs.
P: where the answers to the questions are pronouns.
NE: where the answers to the questions are named entities.
CN: where the answers to the… See the full description on the dataset page: https://huggingface.co/datasets/cam-cst/cbt. | 72 | cbt | [
"task_categories:other",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:gfdl",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:1511.02301",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181da8 | statmt/cc100 | statmt | false | False | 2024-03-05T12:15:34.000Z | 66 | false | 8c658c983d32eab9170d77d416252cfaa0c23e96 | This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository. No claims of intellectual property are made on the work of preparation of the corpus. | 406 | cc100 | [
"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:multilingual",
"source_datasets:original",
"language:af",
"language:am",
"language:ar",
"language:as",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:br",
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"language:ca",
"language:cs",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:ff",
"language:fi",
"language:fr",
"language:fy",
"language:ga",
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"language:gl",
"language:gn",
"language:gu",
"language:ha",
"language:he",
"language:hi",
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"language:ht",
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"language:km",
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"language:ko",
"language:ku",
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"language:li",
"language:ln",
"language:lo",
"language:lt",
"language:lv",
"language:mg",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:ms",
"language:my",
"language:ne",
"language:nl",
"language:no",
"language:ns",
"language:om",
"language:or",
"language:pa",
"language:pl",
"language:ps",
"language:pt",
"language:qu",
"language:rm",
"language:ro",
"language:ru",
"language:sa",
"language:sc",
"language:sd",
"language:si",
"language:sk",
"language:sl",
"language:so",
"language:sq",
"language:sr",
"language:ss",
"language:su",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:th",
"language:tl",
"language:tn",
"language:tr",
"language:ug",
"language:uk",
"language:ur",
"language:uz",
"language:vi",
"language:wo",
"language:xh",
"language:yi",
"language:yo",
"language:zh",
"language:zu",
"license:unknown",
"size_categories:10M<n<100M",
"arxiv:1911.02116",
"arxiv:1911.00359",
"region:us"
] | 2022-03-02T23:29:22.000Z | @inproceedings{conneau-etal-2020-unsupervised,
title = "Unsupervised Cross-lingual Representation Learning at Scale",
author = "Conneau, Alexis and
Khandelwal, Kartikay and
Goyal, Naman and
Chaudhary, Vishrav and
Wenzek, Guillaume and
Guzm{\\'a}n, Francisco and
Grave, Edouard and
Ott, Myle and
Zettlemoyer, Luke and
Stoyanov, Veselin",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.747",
doi = "10.18653/v1/2020.acl-main.747",
pages = "8440--8451",
}
@inproceedings{wenzek-etal-2020-ccnet,
title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data",
author = "Wenzek, Guillaume and
Lachaux, Marie-Anne and
Conneau, Alexis and
Chaudhary, Vishrav and
Guzm{\\'a}n, Francisco and
Joulin, Armand and
Grave, Edouard",
editor = "Calzolari, Nicoletta and
B{\\'e}chet, Fr{\\'e}d{\\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\\'e}l{\\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.494",
pages = "4003--4012",
language = "English",
ISBN = "979-10-95546-34-4",
} |
|
621ffdd236468d709f181da9 | vblagoje/cc_news | vblagoje | false | False | 2024-01-04T06:45:02.000Z | 45 | false | 81eb2ce0d2a9dad6ad16b68ef750ec290880fa36 |
Dataset Card for CC-News
Dataset Summary
CC-News dataset contains news articles from news sites all over the world. The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/.
This version of the dataset has been prepared using news-please - an integrated web crawler and information extractor for news.It contains 708241 English language news articles published between Jan 2017 and December 2019.
It represents a small portion of the… See the full description on the dataset page: https://huggingface.co/datasets/vblagoje/cc_news. | 814 | cc-news | [
"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",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181daa | ahelk/ccaligned_multilingual | ahelk | false | False | 2024-01-18T11:02:11.000Z | 5 | false | 732e0c60b22e16ea2fddcf7b10e4eeff64f88caa | CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). | 11 | ccaligned | [
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"language:uz",
"language:ve",
"language:vi",
"language:war",
"language:wo",
"language:xh",
"language:yi",
"language:yo",
"language:zgh",
"language:zh",
"language:zu",
"language:zza",
"license:unknown",
"size_categories:n<1K",
"region:us"
] | 2022-03-02T23:29:22.000Z | @inproceedings{elkishky_ccaligned_2020,
author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
month = {November},
title = {{CCAligned}: A Massive Collection of Cross-lingual Web-Document Pairs},
year = {2020}
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.480",
doi = "10.18653/v1/2020.emnlp-main.480",
pages = "5960--5969"
} |
|
621ffdd236468d709f181dab | community-datasets/cdsc | community-datasets | false | False | 2024-01-18T08:46:51.000Z | 0 | false | b54010592d87b35ea7e007a1de9e6a3ed7d35f8b |
Dataset Card for [Dataset Name]
Dataset Summary
Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. The dataset was presented at ACL 2017. Please refer to the Wróblewska and Krasnowska-Kieraś (2017) for a detailed description of the resource.
Supported Tasks and Leaderboards… See the full description on the dataset page: https://huggingface.co/datasets/community-datasets/cdsc. | 11 | polish-cdscorpus | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:pl",
"license:cc-by-nc-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"sentences entailment and relatedness"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181dac | ptaszynski/cdt | ptaszynski | false | False | 2024-01-18T14:08:18.000Z | 0 | false | 6c872f54a00a2bd65b1e502b5221dd1161d30789 |
Dataset Card for [Dataset Name]
Dataset Summary
The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
Polish
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
sentence: an… See the full description on the dataset page: https://huggingface.co/datasets/ptaszynski/cdt. | 10 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:pl",
"license:bsd-3-clause",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181dad | sagteam/cedr_v1 | sagteam | false | False | 2024-01-18T14:11:21.000Z | 6 | false | abafbe63cf92c33791b217e8f4f3460f816f1d96 |
Dataset Card for [cedr]
Dataset Summary
The Corpus for Emotions Detecting in Russian-language text sentences of different social sources (CEDR) contains 9410 comments labeled for 5 emotion categories (joy, sadness, surprise, fear, and anger).
Here are 2 dataset configurations:
"main" - contains "text", "labels", and "source" features;
"enriched" - includes all "main" features and "sentences".
Dataset with predefined train/test splits.
Supported… See the full description on the dataset page: https://huggingface.co/datasets/sagteam/cedr_v1. | 16 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ru",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"emotion-classification"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181dae | google-research-datasets/cfq | google-research-datasets | false | False | 2024-01-18T14:16:34.000Z | 3 | false | 6627f9390245fe11ef09f349b82f6c89f577aabf |
Dataset Card for "cfq"
Dataset Summary
The Compositional Freebase Questions (CFQ) is a dataset that is specifically designed to measure compositional
generalization. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also
provides for each question a corresponding SPARQL query against the Freebase knowledge base. This means that CFQ can
also be used for semantic parsing.
Supported Tasks and Leaderboards
More… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/cfq. | 14 | cfq | [
"task_categories:question-answering",
"task_categories:other",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1912.09713",
"region:us",
"compositionality"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181daf | shiyue/chr_en | shiyue | false | False | 2024-01-18T14:19:36.000Z | 3 | false | 1b111eca2b6f2c08ff347b916a3b9cf05642a135 |
Dataset Card for ChrEn
Dataset Summary
ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English.
ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation.
ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning.
Supported Tasks and Leaderboards
The dataset is intended to use… See the full description on the dataset page: https://huggingface.co/datasets/shiyue/chr_en. | 8 | chren | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_categories:translation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"annotations_creators:found",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:multilingual",
"multilinguality:translation",
"source_datasets:original",
"language:chr",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2010.04791",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181db0 | uoft-cs/cifar10 | uoft-cs | false | False | 2024-01-04T06:53:11.000Z | 53 | false | 0b2714987fa478483af9968de7c934580d0bb9a2 |
Dataset Card for CIFAR-10
Dataset Summary
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may… See the full description on the dataset page: https://huggingface.co/datasets/uoft-cs/cifar10. | 20,119 | cifar-10 | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|other-80-Million-Tiny-Images",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181db1 | uoft-cs/cifar100 | uoft-cs | false | False | 2024-01-04T06:57:47.000Z | 31 | false | aadb3af77e9048adbea6b47c21a81e47dd092ae5 |
Dataset Card for CIFAR-100
Dataset Summary
The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (actual class) and coarse label (superclass).
Supported Tasks and Leaderboards… See the full description on the dataset page: https://huggingface.co/datasets/uoft-cs/cifar100. | 2,303 | cifar-100 | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|other-80-Million-Tiny-Images",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181db2 | google-research-datasets/circa | google-research-datasets | false | False | 2024-01-18T14:21:12.000Z | 3 | false | faa1b5a78dd926a899bcd4da289c2e3abe8061a9 |
Dataset Card for CIRCA
Dataset Summary
The Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions.
The dataset contains pairs of yes/no questions and indirect answers, together with annotations for the interpretation of the answer. The data is collected in 10 different social conversational situations (eg. food preferences of a friend).
The following are the situational… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/circa. | 9 | circa | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2010.03450",
"region:us",
"question-answer-pair-classification"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181db3 | google/civil_comments | google | false | False | 2024-01-25T08:23:15.000Z | 10 | false | f2970eb3a55777454c94069077cc8d9b5866312d |
Dataset Card for "civil_comments"
Dataset Summary
The comments in this dataset come from an archive of the Civil Comments
platform, a commenting plugin for independent news sites. These public comments
were created from 2015 - 2017 and appeared on approximately 50 English-language
news sites across the world. When Civil Comments shut down in 2017, they chose
to make the public comments available in a lasting open archive to enable future
research. The original… See the full description on the dataset page: https://huggingface.co/datasets/google/civil_comments. | 1,479 | civil-comments | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"language:en",
"license:cc0-1.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:1903.04561",
"region:us",
"toxic-comment-classification"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181db4 | community-datasets/clickbait_news_bg | community-datasets | false | False | 2024-01-18T14:25:02.000Z | 1 | false | 116216daae4af666df84c0c3296c92d2ff9bcb29 |
Dataset Card for Clickbait/Fake News in Bulgarian
Dataset Summary
This is a corpus of Bulgarian news over a fixed period of time, whose factuality had been questioned.
The news come from 377 different sources from various domains, including politics, interesting facts and tips&tricks.
The dataset was prepared for the Hack the
Fake News hackathon. It was provided by the
Bulgarian Association of PR Agencies and is
available in Gitlab.
The corpus was automatically… See the full description on the dataset page: https://huggingface.co/datasets/community-datasets/clickbait_news_bg. | 11 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:bg",
"license:unknown",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181db5 | tdiggelm/climate_fever | tdiggelm | false | False | 2024-01-18T14:28:07.000Z | 18 | false | ae61ccb9320a78109a246414139ff3a2bd677b8b |
Dataset Card for ClimateFever
Dataset Summary
A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple… See the full description on the dataset page: https://huggingface.co/datasets/tdiggelm/climate_fever. | 131 | climate-fever | [
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_ids:text-scoring",
"task_ids:fact-checking",
"task_ids:fact-checking-retrieval",
"task_ids:semantic-similarity-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|wikipedia",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2012.00614",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181db6 | clinc/clinc_oos | clinc | false | False | 2024-01-18T14:33:10.000Z | 11 | false | 155b9c710419136e17307b80d0a13e68cd46b4ec |
Dataset Card for CLINC150
Dataset Summary
Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope (OOS), i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at… See the full description on the dataset page: https://huggingface.co/datasets/clinc/clinc_oos. | 161 | clinc150 | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181db7 | clue/clue | clue | false | False | 2024-01-17T07:48:08.000Z | 40 | false | 28178267a609dd08bdc703dd6c931dfc2c2f4431 |
Dataset Card for "clue"
Dataset Summary
CLUE, A Chinese Language Understanding Evaluation Benchmark
(https://www.cluebenchmarks.com/) is a collection of resources for training,
evaluating, and analyzing Chinese language understanding systems.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
afqmc
Size of downloaded… See the full description on the dataset page: https://huggingface.co/datasets/clue/clue. | 4,896 | clue | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_ids:topic-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:natural-language-inference",
"task_ids:multiple-choice-qa",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:zh",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2004.05986",
"region:us",
"coreference-nli",
"qa-nli"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181db8 | hfl/cmrc2018 | hfl | false | False | 2024-08-08T06:11:44.000Z | 20 | false | 137f2c45a24275fb68f6961c4d357f46288886aa |
Dataset Card for "cmrc2018"
Dataset Summary
A Span-Extraction dataset for Chinese machine reading comprehension to add language
diversities in this area. The dataset is composed by near 20,000 real questions annotated
on Wikipedia paragraphs by human experts. We also annotated a challenge set which
contains the questions that need comprehensive understanding and multi-sentence
inference throughout the context.
Supported Tasks and Leaderboards
More… See the full description on the dataset page: https://huggingface.co/datasets/hfl/cmrc2018. | 139 | cmrc-2018 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:zh",
"license:cc-by-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181db9 | festvox/cmu_hinglish_dog | festvox | false | False | 2024-01-18T14:36:48.000Z | 7 | false | 19796c6fb32020154cb2745d48704fa73e29b17d |
Dataset Card for CMU Document Grounded Conversations
Dataset Summary
This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English versions. Can be used for Translating between the two. The dataset has been provided by Prof. Alan Black's group from CMU.
Supported Tasks and Leaderboards
abstractive-mt
Languages
Dataset Structure
Data Instances… See the full description on the dataset page: https://huggingface.co/datasets/festvox/cmu_hinglish_dog. | 11 | null | [
"task_categories:translation",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"multilinguality:translation",
"source_datasets:original",
"language:en",
"language:hi",
"license:cc-by-sa-3.0",
"license:gfdl",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1809.07358",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181dba | abisee/cnn_dailymail | abisee | false | False | 2024-01-18T15:31:34.000Z | 203 | false | 96df5e686bee6baa90b8bee7c28b81fa3fa6223d |
Dataset Card for CNN Dailymail Dataset
Dataset Summary
The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.
Supported Tasks and Leaderboards… See the full description on the dataset page: https://huggingface.co/datasets/abisee/cnn_dailymail. | 8,394 | cnn-daily-mail-1 | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22.000Z | null |
|
621ffdd236468d709f181dbb | google-research-datasets/coached_conv_pref | google-research-datasets | false | False | 2024-01-18T09:16:22.000Z | 2 | false | c5a050c88eea9927dc6b914184b1c2b2d031cd07 | A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing
movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers,
where one worker plays the role of an 'assistant', while the other plays the role of a 'user'. The 'assistant' elicits
the 'user’s' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The
assistant asks questions designed to minimize the bias in the terminology the 'user' employs to convey his or her
preferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with
entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of
entities. | 20 | coached-conversational-preference-elicitation | [
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:token-classification",
"task_ids:dialogue-modeling",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:n<1K",
"region:us",
"Conversational Recommendation"
] | 2022-03-02T23:29:22.000Z | @inproceedings{48414,
title = {Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences},
author = {Filip Radlinski and Krisztian Balog and Bill Byrne and Karthik Krishnamoorthi},
year = {2019},
booktitle = {Proceedings of the Annual SIGdial Meeting on Discourse and Dialogue}
} |
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