id stringlengths 2 115 | private bool 1
class | tags list | description stringlengths 0 5.93k ⌀ | downloads int64 0 1.14M | likes int64 0 1.79k |
|---|---|---|---|---|---|
coarse_discourse | false | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0"
] | dataset contains discourse annotation and relation on threads from reddit during 2016 | 392 | 0 |
codah | false | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown"
] | The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use... | 5,548 | 2 |
code_search_net | false | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
... | CodeSearchNet corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and prep... | 5,750 | 55 |
code_x_glue_cc_clone_detection_big_clone_bench | false | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:code",
"license:c-uda"
] | Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax... | 616 | 2 |
code_x_glue_cc_clone_detection_poj104 | false | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"license:c-uda"
] | Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
We use POJ-104 dataset on this task. | 275 | 0 |
code_x_glue_cc_cloze_testing_all | false | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:code",
"license:c-uda"
... | Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing dataset... | 949 | 0 |
code_x_glue_cc_cloze_testing_maxmin | false | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:code",
"license:c-uda"
... | Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing dataset... | 942 | 1 |
code_x_glue_cc_code_completion_line | false | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:code",
"license:c-uda"
] | Complete the unfinished line given previous context. Models are evaluated by exact match and edit similarity.
We propose line completion task to test model's ability to autocomplete a line. Majority code completion systems behave well in token level completion, but fail in completing an unfinished line like a method ca... | 498 | 1 |
code_x_glue_cc_code_completion_token | false | [
"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",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"l... | Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation... | 413 | 0 |
code_x_glue_cc_code_refinement | false | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"license:c-uda",
"debugging",
"arxiv:1812.08693"
] | We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. | 1,336 | 0 |
code_x_glue_cc_code_to_code_trans | false | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"license:c-uda",
"code-to-code"
] | The dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/).
We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplica... | 871 | 1 |
code_x_glue_cc_defect_detection | false | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"license:c-uda"
] | Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
The dataset we use comes from the... | 568 | 5 |
code_x_glue_ct_code_to_text | false | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda",
"code-to-text"
] | The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove ex... | 1,621 | 21 |
code_x_glue_tc_nl_code_search_adv | false | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda"
] | The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove ex... | 297 | 0 |
code_x_glue_tc_text_to_code | false | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda",
"text-to-code"
] | We use concode dataset which is a widely used code generation dataset from Iyer's EMNLP 2018 paper Mapping Language to Code in Programmatic Context. See paper for details. | 1,155 | 7 |
code_x_glue_tt_text_to_text | false | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:da",
"language:en",
"language:lv",
"language:nb",
"language:zh",
"license:c-uda",
"code-documentation-tr... | The dataset we use is crawled and filtered from Microsoft Documentation, whose document located at https://github.com/MicrosoftDocs/. | 732 | 1 |
com_qa | false | [
"language:en"
] | ComQA is a dataset of 11,214 questions, which were collected from WikiAnswers, a community question answering website.
By collecting questions from such a site we ensure that the information needs are ones of interest to actual users.
Moreover, questions posed there are often cannot be answered by commercial search eng... | 328 | 1 |
common_gen | false | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"concepts-to-text",
"arxiv:1911.03705"
... | CommonGen is a constrained text generation task, associated with a benchmark dataset,
to explicitly test machines for the ability of generative commonsense reasoning. Given
a set of common concepts; the task is to generate a coherent sentence describing an
everyday scenario using these concepts.
CommonGen is challengi... | 31,531 | 8 |
common_language | false | [
"task_categories:audio-classification",
"task_ids:speaker-identification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|common_voice",
"language:ar",
"language:br",
"language:ca",
"la... | This dataset is composed of speech recordings from languages that were carefully selected from the CommonVoice database.
The total duration of audio recordings is 45.1 hours (i.e., 1 hour of material for each language).
The dataset has been extracted from CommonVoice to train language-id systems. | 657 | 6 |
common_voice | false | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:extended|common_voice... | Common Voice is Mozilla's initiative to help teach machines how real people speak.
The dataset currently consists of 7,335 validated hours of speech in 60 languages, but we’re always adding more voices and languages. | 14,311 | 71 |
commonsense_qa | false | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1811.00937"
] | CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random... | 7,568 | 13 |
competition_math | false | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"explanation-generation",
"arxiv:2103.03874"
] | The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems
from mathematics competitions, including the AMC 10, AMC 12, AIME, and more.
Each problem in MATH has a full step-by-step solution, which can be used to teach
models to generate answer derivations and explanations. | 5,099 | 6 |
compguesswhat | false | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-guesswhat",
"language:en",
"license:unknown"
] | CompGuessWhat?! is an instance of a multi-task framework for evaluating the quality of learned neural representations,
in particular concerning attribute grounding. Use this dataset if you want to use the set of games whose reference
scene is an image in VisualGenome. Visit the website for more details:... | 404 | 1 |
conceptnet5 | false | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10M<n<100M",
"size_categories:1M<n<10M",
"sou... | This dataset is designed to provide training data
for common sense relationships pulls together from various sources.
The dataset is multi-lingual. See langauge codes and language info
here: https://github.com/commonsense/conceptnet5/wiki/Languages
This dataset provides an interface for the conceptnet5 csv fi... | 626 | 9 |
conll2000 | false | [
"language:en"
] | Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence
He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:
[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP onl... | 373 | 2 |
conll2002 | false | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:es",
"language:nl",
"license... | Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
The shared task of CoNLL-2002 concerns language-indep... | 932 | 0 |
conll2003 | false | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-reuters-corpus",
"language:en",
"lice... | The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
not belong to the previous three groups.
The CoNLL-2003 shared task data files contain four columns se... | 28,853 | 42 |
conllpp | false | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|conll2003",
"language:en",
"license:unknown"
] | CoNLLpp is a corrected version of the CoNLL2003 NER dataset where labels of 5.38% of the sentences in the test set
have been manually corrected. The training set and development set are included for completeness.
For more details see https://www.aclweb.org/anthology/D19-1519/ and https://github.com/ZihanWangKi/CrossWei... | 1,806 | 2 |
consumer-finance-complaints | false | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0"
] | null | 312 | 4 |
conv_ai | false | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"evalu... | ConvAI is a dataset of human-to-bot conversations labelled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains the information on the quality of utterances and entire dialogues, that can guide a dialogue sy... | 1,231 | 2 |
conv_ai_2 | false | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"evalu... | ConvAI is a dataset of human-to-bot conversations labelled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains the information on the quality of utterances and entire dialogues, that can guide a dialogue sy... | 1,642 | 9 |
conv_ai_3 | false | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"eva... | The Conv AI 3 challenge is organized as part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In Information Retrieval (IR) settings such... | 1,297 | 10 |
conv_questions | false | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:open-domain-qa",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source... | ConvQuestions is the first realistic benchmark for conversational question answering over knowledge graphs.
It contains 11,200 conversations which can be evaluated over Wikidata. The questions feature a variety of complex
question phenomena like comparisons, aggregations, compositionality, and temporal reasoning. | 273 | 1 |
coqa | false | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|race",
"source_datasets:extended|cnn_dailymail",
"source_datasets:extended|wikipedia",
... | CoQA: A Conversational Question Answering Challenge | 910 | 9 |
allenai/cord19 | false | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-nd-4.0",
"license:cc-by-sa-4.0",
"license:other",
"arxiv:2004.07180"
] | The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19 and related
historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information
retrieval systems over its rich collection of metadata and structured full text papers. Since ... | 702 | 1 |
cornell_movie_dialog | false | [
"language:en"
] | This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts:
- 220,579 conversational exchanges between 10,292 pairs of movie characters
- involves 9,035 characters from 617 movies
- in total 304,713 utterances
- movie metadata included:
- genres
- release y... | 647 | 5 |
cos_e | false | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|commonsense_qa",
"language:en",
"license:unknown",
"arxiv:1906.02361"
] | Common Sense Explanations (CoS-E) allows for training language models to
automatically generate explanations that can be used during training and
inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. | 16,365 | 3 |
cosmos_qa | false | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1909.00277"
] | Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events tha... | 15,522 | 4 |
counter | false | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
... | The COrpus of Urdu News TExt Reuse (COUNTER) corpus contains 1200 documents with real examples of text reuse from the field of journalism. It has been manually annotated at document level with three levels of reuse: wholly derived, partially derived and non derived. | 274 | 0 |
covid_qa_castorini | false | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2004.1... | CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. | 1,183 | 0 |
covid_qa_deepset | false | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0"
] | COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. | 713 | 1 |
covid_qa_ucsd | false | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"languag... | null | 411 | 1 |
covid_tweets_japanese | false | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ja",
"license:cc-by-nd-4.0"
] | 53,640 Japanese tweets with annotation if a tweet is related to COVID-19 or not. The annotation is by majority decision by 5 - 10 crowd workers. Target tweets include "COVID" or "コロナ". The period of the tweets is from around January 2020 to around June 2020. The original tweets are not contained. Please use Twitter API... | 271 | 0 |
covost2 | false | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-common-voice",
"language:ar",
"language:ca",
... | CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English and from English into 15 languages. The dataset is created using Mozilla’s open source Common Voice database of crowdsourced voice recordings.
Note that in order to limit the required storage for prepari... | 5,550 | 4 |
cppe-5 | false | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"medical-personal-protective-equipment-detection",
"arxiv:2112.09569"
] | CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
to allow the study of subordinate categorization of medical personal protective equipments,
which is not possible with other popular data sets that focus on broad level categories. | 1,179 | 3 |
craigslist_bargains | false | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxi... | We study negotiation dialogues where two agents, a buyer and a seller,
negotiate over the price of an time for sale. We collected a dataset of more
than 6K negotiation dialogues over multiple categories of products scraped from Craigslist.
Our goal is to develop an agent that negotiates with humans through such convers... | 1,222 | 2 |
crawl_domain | false | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-Common-Crawl",
"source_datasets:original",... | Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries (e.g. "commoncrawl" to "common crawl"). Breaking domain names such as "openresearch" into component words "open" and "research" is important for applications such as Text-to-Speech synthesis and web search. Common Crawl is an... | 278 | 0 |
crd3 | false | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:... | Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.
Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.
The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398... | 404 | 6 |
crime_and_punish | false | [
"language:en"
] | \ | 1,325 | 1 |
crows_pairs | false | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"bias-evaluation"
] | CrowS-Pairs, a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models (MLMs). | 5,241 | 1 |
cryptonite | false | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",... | Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language
Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite,
a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each
example in... | 273 | 2 |
cs_restaurants | false | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:machine-gene... | This is a dataset for NLG in task-oriented spoken dialogue systems with Czech as the target language. It originated as
a translation of the English San Francisco Restaurants dataset by Wen et al. (2015). | 270 | 0 |
cuad | false | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:210... | Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions. | 1,086 | 18 |
curiosity_dialogs | false | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"co... | This dataset contains 14K dialogs (181K utterances) where users and assistants converse about geographic topics like
geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog
acts, grounding to Wikipedia, and user reactions to messages. | 295 | 3 |
daily_dialog | false | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"emotion-classificati... | We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way
and cover various topics about our daily life. We also manually label the developed dataset with commun... | 3,829 | 41 |
dane | false | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-Danish-Universal-Dependencies-treebank"... | The DaNE dataset has been annotated with Named Entities for PER, ORG and LOC
by the Alexandra Institute.
It is a reannotation of the UD-DDT (Universal Dependency - Danish Dependency Treebank)
which has annotations for dependency parsing and part-of-speech (POS) tagging.
The Danish UD treebank (Johannsen et al., 2015, U... | 323 | 3 |
danish_political_comments | false | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:da",
"license:unknown"
] | The dataset consists of 9008 sentences that are labelled with fine-grained polarity in the range from -2 to 2 (negative to postive). The quality of the fine-grained is not cross validated and is therefore subject to uncertainties; however, the simple polarity has been cross validated and therefore is considered to be m... | 269 | 0 |
dart | false | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wi... | DART is a large and open-domain structured DAta Record to Text generation corpus with high-quality
sentence annotations with each input being a set of entity-relation triples following a tree-structured ontology.
It consists of 82191 examples across different domains with each input being a semantic RDF triple set deri... | 306 | 3 |
datacommons_factcheck | false | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0"
] | A dataset of fact checked claims by news media maintained by datacommons.org | 406 | 0 |
dbpedia_14 | false | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0"
] | The DBpedia ontology classification dataset is constructed by picking 14 non-overlapping classes
from DBpedia 2014. They are listed in classes.txt. From each of thse 14 ontology classes, we
randomly choose 40,000 training samples and 5,000 testing samples. Therefore, the total size
of the training dataset is 560,000 an... | 11,214 | 6 |
dbrd | false | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"si... | The Dutch Book Review Dataset (DBRD) contains over 110k book reviews of which 22k have associated binary sentiment polarity labels. It is intended as a benchmark for sentiment classification in Dutch and created due to a lack of annotated datasets in Dutch that are suitable for this task. | 305 | 2 |
deal_or_no_dialog | false | [
"task_categories:conversational",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1706.05125"
] | A large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach anagreement (o a deal) via natural language dialogue. | 411 | 2 |
definite_pronoun_resolution | false | [
"task_categories:token-classification",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown"
] | Composed by 30 students from one of the author's undergraduate classes. These
sentence pairs cover topics ranging from real events (e.g., Iran's plan to
attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
Batman) and purely imaginary situations, largely reflecting the pop culture as
perceived... | 314 | 3 |
dengue_filipino | false | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:tl",
"lice... | Benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets. | 270 | 0 |
dialog_re | false | [
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en"... | DialogRE is the first human-annotated dialogue based relation extraction (RE) dataset aiming
to support the prediction of relation(s) between two arguments that appear in a dialogue.
The dataset annotates all occurrences of 36 possible relation types that exist between pairs
of arguments in the 1,788 dialogues originat... | 300 | 6 |
diplomacy_detection | false | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown"
] | null | 273 | 0 |
disaster_response_messages | false | [
"task_categories:text2text-generation",
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:sentiment-classification",
"task_ids:text-simplification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categ... | This dataset contains 30,000 messages drawn from events including an earthquake in Haiti in 2010, an earthquake in Chile in 2010, floods in Pakistan in 2010, super-storm Sandy in the U.S.A. in 2012, and news articles spanning a large number of years and 100s of different disasters.
The data has been encoded with 36 dif... | 288 | 2 |
discofuse | false | [
"task_categories:text2text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"sentence-fusion",
"arxiv:1902.10526"
] | DISCOFUSE is a large scale dataset for discourse-based sentence fusion. | 1,419 | 0 |
discovery | false | [
"task_categories:text-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"discourse-marker-prediction"
] | null | 1,354 | 4 |
disfl_qa | false | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2106.... | Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting,
namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2018)
dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as
a sou... | 277 | 0 |
doc2dial | false | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0"
] | Doc2dial is dataset of goal-oriented dialogues that are grounded in the associated documents. It includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets this dataset covers a variety of di... | 570 | 1 |
docred | false | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1906.06127"
] | Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, ... | 1,488 | 3 |
doqa | false | [
"language:en",
"arxiv:2005.01328"
] | DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues
(10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also
Community Question Answering sites, as... | 536 | 0 |
dream | false | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown"
] | DREAM is a multiple-choice Dialogue-based REAding comprehension exaMination dataset. In contrast to existing reading comprehension datasets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding. | 7,745 | 3 |
drop | false | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:extractive-qa",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"langua... | DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counti... | 1,491 | 4 |
duorc | false | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"sourc... | DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie. | 30,033 | 14 |
dutch_social | false | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-label-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"la... | The dataset contains around 271,342 tweets. The tweets are filtered via the official Twitter API to
contain tweets in Dutch language or by users who have specified their location information within Netherlands
geographical boundaries. Using natural language processing we have classified the tweets for their HISCO codes... | 435 | 4 |
dyk | false | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:bsd-3-clause"
] | The Did You Know (pol. Czy wiesz?) dataset consists of human-annotated question-answer pairs. The task is to predict if the answer is correct. We chose the negatives which have the largest token overlap with a question. | 269 | 0 |
e2e_nlg | false | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"meaning-representation-to-text",
"arxiv:1706.09254",
"ar... | The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
The E2E dataset poses new challenges:
(1) its human reference texts show more lexical richness and syntactic variatio... | 663 | 2 |
e2e_nlg_cleaned | false | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"meaning-representation-to-text",
"arxiv:1706.09254",
"ar... | An update release of E2E NLG Challenge data with cleaned MRs and scripts, accompanying the following paper:
Ondřej Dušek, David M. Howcroft, and Verena Rieser (2019): Semantic Noise Matters for Neural Natural Language Generation. In INLG, Tokyo, Japan. | 1,161 | 2 |
ecb | false | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"languag... | Original source: Website and documentatuion from the European Central Bank, compiled and made available by Alberto Simoes (thank you very much!)
19 languages, 170 bitexts
total number of files: 340
total number of tokens: 757.37M
total number of sentence fragments: 30.55M | 796 | 0 |
ecthr_cases | false | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-... | The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases. | 2,294 | 8 |
eduge | false | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:mn",
"license:unknown"
] | Eduge news classification dataset is provided by Bolorsoft LLC. It is used for training the Eduge.mn production news classifier
75K news articles in 9 categories: урлаг соёл, эдийн засаг, эрүүл мэнд, хууль, улс төр, спорт, технологи, боловсрол and байгал орчин | 269 | 3 |
ehealth_kd | false | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:es",
"license:cc-by-nc-sa-4.0",
"relation-pre... | Dataset of the eHealth Knowledge Discovery Challenge at IberLEF 2020. It is designed for
the identification of semantic entities and relations in Spanish health documents. | 267 | 1 |
eitb_parcc | false | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:es",
"language:eu",
"license:unknown"
] | EiTB-ParCC: Parallel Corpus of Comparable News. A Basque-Spanish parallel corpus provided by Vicomtech (https://www.vicomtech.org), extracted from comparable news produced by the Basque public broadcasting group Euskal Irrati Telebista. | 269 | 1 |
electricity_load_diagrams | false | [
"task_categories:time-series-forecasting",
"task_ids:univariate-time-series-forecasting",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"license:unknown"
] | This new dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014. | 405 | 2 |
eli5 | false | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"ar... | Explain Like I'm 5 long form QA dataset | 6,833 | 23 |
eli5_category | false | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|eli5",
"language:en",
"license:unknown"
] | The ELI5-Category dataset is a smaller but newer and categorized version of the original ELI5 dataset. After 2017, a tagging system was introduced to this subreddit so that the questions can be categorized into different topics according to their tags. Since the training and validation set is built by questions in diff... | 349 | 2 |
emea | false | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language... | This is a parallel corpus made out of PDF documents from the European Medicines Agency. All files are automatically converted from PDF to plain text using pdftotext with the command line arguments -layout -nopgbrk -eol unix. There are some known problems with tables and multi-column layouts - some of them are fixed in ... | 795 | 0 |
emo | false | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown"
] | In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. | 2,302 | 1 |
dair-ai/emotion | false | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"emotion-classific... | Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. | 2,587 | 79 |
emotone_ar | false | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:unknown"
] | Dataset of 10065 tweets in Arabic for Emotion detection in Arabic text | 272 | 5 |
empathetic_dialogues | false | [
"task_categories:conversational",
"task_categories:question-answering",
"task_ids:dialogue-generation",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"langua... | PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset | 904 | 22 |
enriched_web_nlg | false | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-web-nlg",
"language:de",
"language:en",
"license:cc-by-sa-4.0"
] | WebNLG is a valuable resource and benchmark for the Natural Language Generation (NLG) community. However, as other NLG benchmarks, it only consists of a collection of parallel raw representations and their corresponding textual realizations. This work aimed to provide intermediate representations of the data for the de... | 1,383 | 1 |
eraser_multi_rc | false | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other"
] | Eraser Multi RC is a dataset for queries over multi-line passages, along with
answers and a rationalte. Each example in this dataset has the following 5 parts
1. A Mutli-line Passage
2. A Query about the passage
3. An Answer to the query
4. A Classification as to whether the answer is right or wrong
5. An Explanation j... | 310 | 0 |
esnli | false | [
"language:en"
] | The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
include human-annotated natural language explanations of the entailment
relations. | 2,951 | 6 |
eth_py150_open | false | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"contextual-embeddings"
] | A redistributable subset of the ETH Py150 corpus, introduced in the ICML 2020 paper 'Learning and Evaluating Contextual Embedding of Source Code' | 273 | 0 |
ethos | false | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K"... | ETHOS: onlinE haTe speecH detectiOn dataSet. This repository contains a dataset for hate speech
detection on social media platforms, called Ethos. There are two variations of the dataset:
Ethos_Dataset_Binary: contains 998 comments in the dataset alongside with a label
about hate speech presence or absence. 565 of the... | 3,200 | 7 |
eu_regulatory_ir | false | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"document-to-document-retrieval",
"arxiv:21... | EURegIR: Regulatory Compliance IR (EU/UK) | 400 | 1 |
eurlex | false | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"legal-topic-classification"
] | EURLEX57K contains 57k legislative documents in English from EUR-Lex portal, annotated with EUROVOC concepts. | 655 | 3 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.