Datasets:
nkasmanoff
/
huggingface-datasets

Dataset card Data Studio Files Community
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illuin/fr_corpora_parliament_processed
false
[ "language:fr" ]
null
0
0
marinone94/nst_no
false
[]
This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Norwegian. In this version, the organization of the data have been altered to improve the usefulness of the database. In the original version of the material, the files were organized in a specific folder structure where the folder names were meaningful. However, the file names were not meaningful, and there were also cases of files with identical names in different folders. This proved to be impractical, since users had to keep the original folder structure in order to use the data. The files have been renamed, such that the file names are unique and meaningful regardless of the folder structure. The original metadata files were in spl format. These have been converted to JSON format. The converted metadata files are also anonymized and the text encoding has been converted from ANSI to UTF-8. See the documentation file for a full description of the data and the changes made to the database.
0
0
marinone94/nst_sv
false
[]
This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish. In this updated version, the organization of the data have been altered to improve the usefulness of the database. In the original version of the material, the files were organized in a specific folder structure where the folder names were meaningful. However, the file names were not meaningful, and there were also cases of files with identical names in different folders. This proved to be impractical, since users had to keep the original folder structure in order to use the data. The files have been renamed, such that the file names are unique and meaningful regardless of the folder structure. The original metadata files were in spl format. These have been converted to JSON format. The converted metadata files are also anonymized and the text encoding has been converted from ANSI to UTF-8. See the documentation file for a full description of the data and the changes made to the database.
2
0
mariosasko/test_multi_dir_dataset
false
[]
null
0
0
markscrivo/OddsOn
false
[]
null
0
0
martodaniel/terere
false
[]
null
0
0
masked-neuron/amazon
false
[]
0
0
masked-neuron/ccd
false
[]
The consumer compaint data set is derived from the consumer complaint database for the purpose of benchmarking quantification / label shift algorithms. The data set consists of records of compaints about consumer financial products and services that the Consumer Financial Protection Bureau sent to companies for response. Each record has a corresponding product / sub product field which can be used as labels for text classification.
0
0
masked-neuron/qb
false
[]
0
0
mattchurgin/sv_corpora_parliament_processed
false
[]
null
0
0
matteopilotto/github-issues
false
[]
null
0
0
maximedb/mcqa_light
false
[]
MQA is a multilingual corpus of questions and answers parsed from the Common Crawl. Questions are divided between Frequently Asked Questions (FAQ) pages and Community Question Answering (CQA) pages.
0
0
maximedb/mfaq_light
false
[]
MQA is a multilingual corpus of questions and answers parsed from the Common Crawl. Questions are divided between Frequently Asked Questions (FAQ) pages and Community Question Answering (CQA) pages.
0
0
maximedb/paws-x-all
false
[]
PAWS-X, a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages. This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. English language is available by default. All translated pairs are sourced from examples in PAWS-Wiki. For further details, see the accompanying paper: PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification (https://arxiv.org/abs/1908.11828) NOTE: There might be some missing or wrong labels in the dataset and we have replaced them with -1.
0
0
maximedb/vaccinchat
false
[]
null
0
0
maximedb/vaccinchat_retrieval
false
[]
null
0
0
maximedb/wow
false
[]
In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. The most popular sequence to sequence models typically "generate and hope" generic utterances that can be memorized in the weights of the model when mapping from input utterance(s) to output, rather than employing recalled knowledge as context. Use of knowledge has so far proved difficult, in part because of the lack of a supervised learning benchmark task which exhibits knowledgeable open dialogue with clear grounding. To that end we collect and release a large dataset with conversations directly grounded with knowledge retrieved from Wikipedia. We then design architectures capable of retrieving knowledge, reading and conditioning on it, and finally generating natural responses. Our best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while our new benchmark allows for measuring further improvements in this important research direction.
8
0
maxmoynan/SemEval2017-Task4aEnglish
false
[]
null
7
0
maydogan/TRSAv1
false
[]
null
0
1
mbateman/github-issues
false
[ "arxiv:2005.00614" ]
null
0
0
medzaf/test
false
[]
null
0
0
meghanabhange/chaii
false
[]
null
0
0
meghanabhange/hilm141021
false
[ "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:hi", "license:other" ]
null
0
0
meghanabhange/hitalm141021
false
[ "annotations_creators:other", "language_creators:other", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:original", "language:hi", "language:ta", "license:other" ]
null
0
0
meghanabhange/hitalmsandbox
false
[]
null
0
0
meghanabhange/talm141021
false
[ "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:ta", "license:other" ]
null
0
0
merve/coco
false
[]
null
2
2
merve/folk-mythology-tales
false
[]
null
0
1
merve/poetry
false
[]
null
3
6
merve/qqp
false
[]
null
7
0
metaeval/blimp_classification
false
[ "task_categories:text-classification", "task_ids:acceptability-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "cola" ]
Acceptable/non acceptable sentences (recasted as a classification task)
20
1
metaeval/crowdflower
false
[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "language:en" ]
Collection of crowdflower classification datasets
130
0
metaeval/ethics
false
[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "language:en" ]
Probing for ethics understanding
595
3
metaeval/linguisticprobing
false
[ "task_categories:text-classification", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "language:en" ]
10 probing tasks designed to capture simple linguistic features of sentences,
120
0
metaeval/recast
false
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:apache-2.0", "nli", "natural-language-inference" ]
A diverse collection of tasks recasted as natural language inference tasks.
112
0
metalearning/kaggale-nlp-tutorial
false
[]
null
0
0
metamong1/summarization_optimization
false
[]
Aihub Document summarization data
1
1
michaelbenayoun/wikipedia-bert-128
false
[]
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
0
0
microsoft/codexglue_method_generation
false
[]
null
23
2
midas/citeulike180
false
[]
\
0
0
midas/cstr
false
[]
\
0
0
midas/duc2001
false
[]
\
260
1
midas/inspec
false
[ "arxiv:1910.08840" ]
Benchmark dataset for automatic identification of keyphrases from text published with the work - Improved automatic keyword extraction given more linguistic knowledge. Anette Hulth. In Proceedings of EMNLP 2003. p. 216-223.
158
3
midas/inspec_ke_tagged
false
[]
null
0
0
midas/kdd
false
[]
\
0
0
midas/kp20k
false
[]
\
10
2
midas/kpcrowd
false
[]
\
23
1
midas/kptimes
false
[]
\
0
0
midas/krapivin
false
[]
\
0
0
midas/ldke3k_medium
false
[]
null
0
0
midas/ldke3k_small
false
[]
null
0
0
midas/ldkp10k
false
[]
This new dataset is designed to solve kp NLP task and is crafted with a lot of care.
0
2
midas/ldkp3k
false
[]
This new dataset is designed to solve kp NLP task and is crafted with a lot of care.
0
2
midas/ldkp3k_small
false
[]
null
0
0
midas/nus
false
[]
\
0
0
midas/oagkx
false
[]
\
3
0
midas/openkp
false
[]
\
253
2
midas/pubmed
false
[]
\
0
0
midas/semeval2010
false
[ "arxiv:1910.08840" ]
\
17
0
midas/semeval2010_ke_tagged
false
[]
null
0
0
midas/semeval2017
false
[ "arxiv:1704.02853", "arxiv:1910.08840" ]
\
46
1
midas/semeval2017_ke_tagged
false
[]
null
1
0
midas/test_ldkp
false
[]
This new dataset is designed to solve kp NLP task and is crafted with a lot of care.
0
0
midas/www
false
[]
\
0
0
mideind/icelandic-common-crawl-corpus-IC3
false
[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:is", "license:unknown" ]
null
0
0
mideind/icelandic-error-corpus-IceEC
false
[ "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:is", "license:cc-by-4.0" ]
The Icelandic Error Corpus (IceEC) is a collection of texts in modern Icelandic annotated for mistakes related to spelling, grammar, and other issues. The texts are organized by genre. The current version includes sentences from student essays, online news texts and Wikipedia articles. Sentences within texts in the student essays had to be shuffled due to the license which they were originally published under, but neither the online news texts nor the Wikipedia articles needed to be shuffled.
0
1
miesnerjacob/github-issues
false
[]
null
0
0
mikeee/model-z
false
[]
null
0
0
mirari/sv_corpora_parliament_processed
false
[]
null
0
0
mishig/sample_images
false
[]
null
0
0
mksaad/Arabic_news
false
[]
null
0
0
ml6team/cnn_dailymail_nl
false
[ "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:https://github.com/huggingface/datasets/tree/master/datasets/cnn_dailymail", "language:nl", "license:mit" ]
This dataset is the CNN/Dailymail dataset translated to Dutch. This is the original dataset: ``` load_dataset("cnn_dailymail", '3.0.0') ``` And this is the HuggingFace translation pipeline: ``` pipeline( task='translation_en_to_nl', model='Helsinki-NLP/opus-mt-en-nl', tokenizer='Helsinki-NLP/opus-mt-en-nl') ```
36
12
ml6team/xsum_nl
false
[ "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|xsum", "language:nl", "license:unknown" ]
null
0
2
mldmm/glass_alloy_composition
false
[]
This is an alloy composition dataset
0
0
mmcquade11-test/reuters-for-summarization-two
false
[]
null
0
0
mmm-da/rutracker_anime_torrent_titles
false
[]
null
0
0
mnaylor/evaluating-student-writing
false
[]
null
0
0
mnemlaghi/widdd
false
[ "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "arxiv:1810.09164" ]
WiDDD stands for WIkiData Disambig with Descriptions. The former dataset comes from [Cetoli & al](https://arxiv.org/pdf/1810.09164.pdf) paper, and is aimed at solving Named Entity Disambiguation. This datasets tries to extract relevant information from entities descriptions only, instead of working with graphs. In order to do so, we mapped every Wikidata id (correct id and wrong id) in the original paper with its WikiData description. If not found, row is discarded for this version.
2
1
mohamed-illiyas/wav2vec-malayalam-data
false
[]
null
0
0
mohamed-illiyas/wav2vec-malayalam-new-data
false
[]
null
0
0
mohamed-illiyas/wav2vec2-base-lj-demo-colab
false
[]
null
0
0
morganchen1007/1215
false
[]
null
0
0
morganchen1007/1216
false
[]
null
0
0
morganchen1007/1216_00
false
[]
null
0
0
morganchen1007/test_1213_00
false
[]
null
0
0
mostol/wiktionary-ipa
false
[]
null
0
2
moumeneb1/French_arpa_lm
false
[]
null
0
0
moumeneb1/filtered
false
[]
null
0
0
moumeneb1/filtered_300
false
[]
null
0
0
moumeneb1/fr_lm_dataset
false
[]
null
0
0
moumeneb1/large_vocabulary_dataset
false
[]
null
0
0
moumeneb1/osc_processed_lm
false
[]
null
0
0
moumeneb1/testing
false
[]
null
0
0
moxi43/github-issues
false
[]
null
0
0
mpierrau/sv_corpora_parliament_processed
false
[]
null
0
0
mr-robot/ec
false
[]
null
0
0
mrm8488/fake-news
false
[]
null
0
0
mrm8488/goemotions
false
[ "arxiv:2005.00547" ]
null
6
5
mrojas/abbreviation
false
[]
\
0
0
mrojas/body
false
[]
\
0
0