Datasets:
nkasmanoff
/
huggingface-datasets

Dataset card Data Studio Files Community
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mrojas/disease
false
[]
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0
0
mrojas/family
false
[]
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0
0
mrojas/finding
false
[]
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0
0
mrojas/medication
false
[]
\
0
0
mrojas/procedure
false
[]
\
0
0
mrp/Thai-Semantic-Textual-Similarity-Benchmark
false
[]
null
0
0
msarmi9/korean-english-multitarget-ted-talks-task
false
[ "annotations_creators:expert-generated", "language_creators:other", "multilinguality:translation", "multilinguality:multilingual", "language:en", "language:ko", "license:cc-by-nc-nd-4.0" ]
null
21
0
msivanes/github-issues
false
[]
null
0
0
mswedrowski/multiwiki_90k
false
[]
null
0
0
mtfelix/datasetdemo
false
[]
null
0
0
mtlew/0001_Angry_test
false
[]
null
0
0
muhtasham/autonlp-data-Doctor_DE
false
[ "task_categories:text-classification", "task_ids:text-scoring", "language:de" ]
null
0
0
mulcyber/europarl-mono
false
[]
Europarl Monolingual Dataset. The Europarl parallel corpus is extracted from the proceedings of the European Parliament (from 2000 to 2011). It includes versions in 21 European languages: Romanic (French, Italian, Spanish, Portuguese, Romanian), Germanic (English, Dutch, German, Danish, Swedish), Slavik (Bulgarian, Czech, Polish, Slovak, Slovene), Finni-Ugric (Finnish, Hungarian, Estonian), Baltic (Latvian, Lithuanian), and Greek. Upstream url: https://www.statmt.org/europarl/
3
0
indonesian-nlp/mc4-id
false
[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended", "language:id", "license:odc-by", "arxiv:1910.10683" ]
A thoroughly cleaned version of the Italian portion of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning detailed in the repository README file.
0
1
mustafa12/db_ee
false
[]
null
0
0
mustafa12/edaaaas
false
[]
null
0
0
mustafa12/thors
false
[]
null
0
0
mvarma/medwiki
false
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|wikipedia", "language:en-US", "language:en", "license:cc-by-4.0", "arxiv:2110.08228" ]
MedWiki is a large-scale sentence dataset collected from Wikipedia with medical entity (UMLS) annotations. This dataset is intended for pretraining.
21
1
mvip/tr_corpora_parliament_processed
false
[]
null
0
0
mvip/tr_corpora_parliament_processed_non_hatted
false
[]
null
0
0
nateraw/auto-cats-and-dogs
false
[ "task_categories:other", "auto-generated", "image-classification" ]
null
2
0
nateraw/auto-exp-2
false
[ "task_categories:other", "auto-generated", "image-classification" ]
null
0
0
nateraw/beans
false
[ "task_categories:other", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:mit" ]
Beans is a dataset of images of beans taken in the field using smartphone cameras. It consists of 3 classes: 2 disease classes and the healthy class. Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated by experts from the National Crops Resources Research Institute (NaCRRI) in Uganda and collected by the Makerere AI research lab.
0
0
nateraw/beans_old
false
[]
null
0
0
nateraw/blahblah
false
[]
null
0
0
nateraw/bulk-dummy
false
[]
null
0
0
nateraw/cats-and-dogs
false
[]
null
8
0
nateraw/cats_vs_dogs
false
[ "task_categories:other", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown" ]
null
0
0
nateraw/dummy-csv-dataset
false
[]
null
0
0
nateraw/filings-10k
false
[]
null
0
0
nateraw/food101
false
[ "task_categories:other", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-foodspotting", "language:en", "license:unknown" ]
null
4
1
nateraw/food101_old
false
[ "task_categories:other", "annotations_creators:crowdsourced", "size_categories:10K<n<100K", "source_datasets:extended|other-foodspotting", "license:unknown" ]
null
0
0
nateraw/huggingpics-data-2
false
[]
null
0
0
nateraw/huggingpics-data
false
[]
null
0
0
nateraw/image-folder
false
[]
null
30
0
nateraw/imagefolder
false
[]
null
4
1
nateraw/imagenette
false
[]
Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time. This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants: * Full size * 320 px * 160 px Note: The v2 config correspond to the new 70/30 train/valid split (released in Dec 6 2019).
0
2
nateraw/img-demo
false
[]
null
0
0
nateraw/punks
false
[]
null
0
0
nateraw/rock_paper_scissors
false
[]
null
0
0
nateraw/sync_food101
false
[ "task_categories:other", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-foodspotting", "language:en", "license:unknown" ]
null
0
0
nateraw/test
false
[]
null
0
0
nateraw/wit
false
[]
Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
0
0
nathanlsl/news
false
[]
null
0
0
naver-clova-conversation/klue-tc-dev-tsv
false
[]
null
0
0
naver-clova-conversation/klue-tc-tsv
false
[]
null
2
0
navjordj/nak_nb
false
[]
null
0
0
ncats/EpiSet4BinaryClassification
false
[ "annotations_creators:unknown", "language_creators:unknown", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:unknown", "language:en", "license:cc-by-4.0" ]
INSERT DESCRIPTION
3
0
ncats/EpiSet4NER-v1
false
[ "task_ids:named-entity-recognition", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "license:other" ]
**REWRITE* EpiSet4NER is a dataset generated from 620 rare disease abstracts labeled using statistical and rule-base methods. The test set was then manually corrected by a rare disease expert. For more details see *INSERT PAPER* and https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard
0
1
ncats/GARD_EpiSet4TextClassification
false
[]
INSERT DESCRIPTION
0
0
ncduy/github-issues
false
[]
null
0
0
ncduy/mt-en-vi
false
[ "annotations_creators:found", "language_creators:found", "multilinguality:translation", "size_categories:1M<n<10M", "source_datasets:own", "source_datasets:open_subtitles", "source_datasets:tatoeba", "source_datasets:opus_tedtalks", "source_datasets:qed_amara", "source_datasets:opus_wikipedia", "language:en", "language:vi", "license:mit" ]
null
23
1
ncoop57/athena_data
false
[]
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
0
0
ncoop57/csnc_human_judgement
false
[]
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
0
0
ncoop57/rico_captions
false
[]
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
0
1
neelalex/raft-predictions
false
[ "benchmark:raft" ]
\\nThis dataset contains a corpus of AI papers. The first task is to determine\\n whether or not a datapoint is an AI safety paper. The second task is to\\n determine what type of paper it is.
0
1
nferruz/UR50_2021_04
false
[ "size_categories:unknown" ]
null
3
1
ngdiana/hu_severity
false
[]
null
1
0
ngdiana/uaspeech
false
[]
null
1
0
ngdiana/uaspeech_severity
false
[]
null
0
0
ngdiana/uaspeech_severity_high
false
[]
null
1
0
ngdiana/uaspeech_severity_low
false
[]
null
0
0
nickmuchi/financial-classification
false
[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "size_categories:1K<n<10K", "language:en", "finance" ]
null
20
4
nickmuchi/trade-the-event-finance
false
[]
null
2
1
nid989/FNC-1
false
[]
null
39
2
nielsr/FUNSD_layoutlmv2
false
[ "language:en", "arxiv:1905.13538" ]
https://guillaumejaume.github.io/FUNSD/
408
3
nielsr/XFUN
false
[]
null
113
3
nielsr/funsd
false
[]
https://guillaumejaume.github.io/FUNSD/
20,953
5
nlpconnect/dpr-nq-reader-v2
false
[]
null
0
0
nlpconnect/dpr-nq-reader
false
[]
null
0
0
nlpconnect/ms_marco_subset_v2.1
false
[]
null
0
0
nlpufg/brwac-pt
false
[]
null
0
0
nlpufg/brwac
false
[]
null
0
0
nlpufg/oscar-pt
false
[]
null
0
0
nlpyeditepe/tr-qnli
false
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|glue", "language:tr-TR", "license:mit" ]
null
0
0
nlpyeditepe/tr_rte
false
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|glue", "language:tr-TR", "license:mit" ]
null
0
0
nntadotzip/iuQAchatbot
false
[]
null
0
0
notional/notional-python
false
[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:py", "license:unknown" ]
null
5
1
nouamanetazi/ar_common_voice_processed
false
[]
null
0
0
nouamanetazi/ar_opus100_processed
false
[]
null
0
0
ntagg/data1
false
[]
null
0
0
nthngdy/bananas
false
[]
null
1
0
nthngdy/ccnews_split
false
[]
CC-News containing 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 708241 articles. It represents a small portion of English language subset of the CC-News dataset created using news-please(Hamborg et al.,2017) to collect and extract English language portion of CC-News.
3
0
nthngdy/openwebtext_split
false
[]
An open-source replication of the WebText dataset from OpenAI.
0
0
ntutexas/amazon
false
[]
null
0
0
nucklehead/ht-voice-dataset
false
[]
null
0
0
nykodmar/cs_corpora_parliament_processed
false
[]
null
0
0
oelkrise/CRT
false
[]
null
0
0
omar-sharif/BAD-Bengali-Aggressive-Text-Dataset
false
[]
null
0
1
openclimatefix/eumetsat_uk_hrv
false
[]
The EUMETSAT Spinning Enhanced Visible and InfraRed Imager (SEVIRI) rapid scanning service (RSS) takes an image of the northern third of the Meteosat disc every five minutes (see the EUMETSAT website for more information on SEVIRI RSS ). The original EUMETSAT dataset contains data from 2008 to the present day from 12 channels, and for a wide geographical extent covering North Africa, Saudi Arabia, all of Europe, and Western Russia. In contrast, this dataset on Google Cloud is a small subset of the entire SEVIRI RSS dataset: This Google Cloud dataset is from a single channel: the "high resolution visible" (HRV) channel; and contains data from January 2020 to November 2021. The geographical extent of this dataset on Google Cloud is a small subset of the total SEVIRI RSS extent: This Google Cloud dataset includes data over the United Kingdom and over North Western Europe. This dataset is slightly transformed: It does not contain the original numerical values. The original data is copyright EUMETSAT. EUMETSAT has given permission to redistribute this transformed data. The data was transformed by Open Climate Fix using satip. This public dataset is hosted in Google Cloud Storage and available free to use.
3
1
openclimatefix/gfs
false
[ "license:mit" ]
null
2
0
openclimatefix/goes-l2
false
[ "license:mit" ]
null
2
0
openclimatefix/goes-mrms
false
[]
null
14
0
openclimatefix/goes
false
[ "license:mit" ]
The National Oceanic and Atmospheric Administration (NOAA) operates a constellation of Geostationary Operational Environmental Satellites (GOES) to provide continuous weather imagery and monitoring of meteorological and space environment data for the protection of life and property across the United States. GOES satellites provide critical atmospheric, oceanic, climatic and space weather products supporting weather forecasting and warnings, climatologic analysis and prediction, ecosystems management, safe and efficient public and private transportation, and other national priorities. The satellites provide advanced imaging with increased spatial resolution, 16 spectral channels, and up to 1 minute scan frequency for more accurate forecasts and timely warnings. The real-time feed and full historical archive of original resolution Advanced Baseline Imager (ABI) radiance data (Level 1b) and full resolution Cloud and Moisture Imager (CMI) products (Level 2) are freely available on Amazon S3 for anyone to use.
2
2
openclimatefix/hrrr
false
[ "license:mit" ]
null
2
0
orisuchy/Descriptive_Sentences_He
false
[ "license:afl-3.0" ]
null
0
2
osanseviero/codeparrot-train
false
[]
null
0
0
osanseviero/llama_test
false
[]
null
1
0
osanseviero/test
false
[]
null
0
0
ought/raft-submission
false
[]
null
0
3