Datasets:
nkasmanoff
/
huggingface-datasets

Dataset card Data Studio Files Community
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5.93k
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filwsyl/video_tags
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[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-nist", "language:enx", "license:mit" ]
The MNIST dataset consists of 70,000 28x28 black-and-white images in 10 classes (one for each digits), with 7,000 images per class. There are 60,000 training images and 10,000 test images.
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taln-ls2n/kptimes
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[ "task_categories:text-generation", "annotations_creators:unknown", "language_creators:unknown", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "license:cc-by-4.0" ]
KPTimes benchmark dataset for keyphrase extraction an generation.
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swcrazyfan/net-kjv
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crabz/boolq_sk
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vehnem/DBpedia
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[ "license:cc-by-sa-3.0" ]
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MauriceV2021/AuroraSDGsDataset
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[ "license:cc-by-4.0" ]
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filwsyl/ascend
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[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "language:zh", "license:cc-by-sa-4.0", "arxiv:2112.06223" ]
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ai4bharat/Aksharantar
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[ "task_categories:text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "language_creators:machine-generated", "language_creators:found", "language_creators:other", "multilinguality:multilingual", "source_datasets:original", "language:asm", "language:ben", "language:brx", "language:guj", "language:hin", "language:kan", "language:kas", "language:kok", "language:mai", "language:mal", "language:mar", "language:mni", "language:nep", "language:ori", "language:pan", "language:san", "language:sid", "language:tam", "language:tel", "language:urd", "license:cc-by-nc-4.0", "arxiv:2205.03018" ]
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cradle-bio/FLIP_clusters
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cradle-bio/FLIP_biggest_cluster_Q0PD48
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polinaeterna/vox_lingua
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[ "license:cc-by-4.0" ]
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
1
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pie/german_legal_entity_recognition
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pie/conll2002
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pie/germeval_14
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pie/conllpp
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pie/germaner
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pie/ncbi_disease
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pie/wnut_17
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pie/wikiann
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Rizwan125/AIByRizwan
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[ "license:apache-2.0" ]
null
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strombergnlp/twitter_pos
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[ "task_categories:token-classification", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0" ]
Part-of-speech information is basic NLP task. However, Twitter text is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style. This dataset contains two datasets for English PoS tagging for tweets: * Ritter, with train/dev/test * Foster, with dev/test Splits defined in the Derczynski paper, but the data is from Ritter and Foster. For more details see: * https://gate.ac.uk/wiki/twitter-postagger.html * https://aclanthology.org/D11-1141.pdf * https://www.aaai.org/ocs/index.php/ws/aaaiw11/paper/download/3912/4191
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kimcando/KOR-RE-natures-and-environments
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[ "license:apache-2.0" ]
null
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avacaondata/randomq
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nateraw/background-remover-files
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[ "license:apache-2.0" ]
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d0r1h/customer_churn
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[ "license:apache-2.0" ]
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NLPC-UOM/Student_feedback_analysis_dataset
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Bingsu/arcalive_220506
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[ "task_categories:fill-mask", "task_categories:text-generation", "task_ids:masked-language-modeling", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ko", "license:cc0-1.0" ]
[์•„์นด๋ผ์ด๋ธŒ ๋ฒ ์ŠคํŠธ ๋ผ์ด๋ธŒ ์ฑ„๋„](https://arca.live/b/live)์˜ 2021๋…„ 8์›” 16์ผ๋ถ€ํ„ฐ 2022๋…„ 5์›” 6์ผ๊นŒ์ง€์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ, ๋Œ“๊ธ€๋งŒ ๊ณจ๋ผ๋‚ธ ๋ฐ์ดํ„ฐ์ž…๋‹ˆ๋‹ค.
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zhiguoxu/test_data
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null
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deydebasmita91/Tweet
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[ "license:afl-3.0" ]
null
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manirai91/ebiquity-v2
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[]
Ebiquity V2 (non-stemmed) dataset for Nepali NER task. The dataset is tagged with BIO scheme.
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