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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ea9393cfb9539b84936a5900d6bc79ac5f8d4262 |
# indspeech_news_lvcsr
This is the first Indonesian speech dataset for large vocabulary continuous speech recognition (LVCSR) with more than 40 hours of speech and 400 speakers [Sakti et al., 2008]. R&D Division of PT Telekomunikasi Indonesia (TELKOMRisTI) developed the data in 2005-2006, in collaboration with Advanc... | SEACrowd/indspeech_news_lvcsr | [
"language:ind",
"speech-recognition",
"region:us"
] | 2023-09-26T10:16:08+00:00 | {"language": ["ind"], "tags": ["speech-recognition"]} | 2023-09-26T11:31:52+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #speech-recognition #region-us
|
# indspeech_news_lvcsr
This is the first Indonesian speech dataset for large vocabulary continuous speech recognition (LVCSR) with more than 40 hours of speech and 400 speakers [Sakti et al., 2008]. R&D Division of PT Telekomunikasi Indonesia (TELKOMRisTI) developed the data in 2005-2006, in collaboration with Advanc... | [
"# indspeech_news_lvcsr\n\nThis is the first Indonesian speech dataset for large vocabulary continuous speech recognition (LVCSR) with more than 40 hours of speech and 400 speakers [Sakti et al., 2008]. R&D Division of PT Telekomunikasi Indonesia (TELKOMRisTI) developed the data in 2005-2006, in collaboration with ... | [
"TAGS\n#language-Indonesian #speech-recognition #region-us \n",
"# indspeech_news_lvcsr\n\nThis is the first Indonesian speech dataset for large vocabulary continuous speech recognition (LVCSR) with more than 40 hours of speech and 400 speakers [Sakti et al., 2008]. R&D Division of PT Telekomunikasi Indonesia (TE... | [
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159,
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] | [
"passage: TAGS\n#language-Indonesian #speech-recognition #region-us \n# indspeech_news_lvcsr\n\nThis is the first Indonesian speech dataset for large vocabulary continuous speech recognition (LVCSR) with more than 40 hours of speech and 400 speakers [Sakti et al., 2008]. R&D Division of PT Telekomunikasi Indonesia ... |
f15555dcb6d63ebfa8b9bce0de04a1f241f85944 |
# kopi_nllb
KopI(Korpus Perayapan Indonesia)-NLLB, is Indonesian family language(aceh,bali,banjar,indonesia,jawa,minang,sunda) only extracted from NLLB Dataset, allenai/nllb
each language set also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup
## Dat... | SEACrowd/kopi_nllb | [
"language:ind",
"language:jav",
"language:ace",
"language:ban",
"language:bjn",
"language:min",
"language:sun",
"self-supervised-pretraining",
"arxiv:2205.12654",
"arxiv:2207.04672",
"region:us"
] | 2023-09-26T10:16:12+00:00 | {"language": ["ind", "jav", "ace", "ban", "bjn", "min", "sun"], "tags": ["self-supervised-pretraining"]} | 2023-09-26T11:31:56+00:00 | [
"2205.12654",
"2207.04672"
] | [
"ind",
"jav",
"ace",
"ban",
"bjn",
"min",
"sun"
] | TAGS
#language-Indonesian #language-Javanese #language-Achinese #language-Balinese #language-Banjar #language-Minangkabau #language-Sundanese #self-supervised-pretraining #arxiv-2205.12654 #arxiv-2207.04672 #region-us
|
# kopi_nllb
KopI(Korpus Perayapan Indonesia)-NLLB, is Indonesian family language(aceh,bali,banjar,indonesia,jawa,minang,sunda) only extracted from NLLB Dataset, allenai/nllb
each language set also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup
## Dat... | [
"# kopi_nllb\n\nKopI(Korpus Perayapan Indonesia)-NLLB, is Indonesian family language(aceh,bali,banjar,indonesia,jawa,minang,sunda) only extracted from NLLB Dataset, allenai/nllb\n\n\n\neach language set also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neard... | [
"TAGS\n#language-Indonesian #language-Javanese #language-Achinese #language-Balinese #language-Banjar #language-Minangkabau #language-Sundanese #self-supervised-pretraining #arxiv-2205.12654 #arxiv-2207.04672 #region-us \n",
"# kopi_nllb\n\nKopI(Korpus Perayapan Indonesia)-NLLB, is Indonesian family language(aceh... | [
71,
97,
35,
6,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-Javanese #language-Achinese #language-Balinese #language-Banjar #language-Minangkabau #language-Sundanese #self-supervised-pretraining #arxiv-2205.12654 #arxiv-2207.04672 #region-us \n# kopi_nllb\n\nKopI(Korpus Perayapan Indonesia)-NLLB, is Indonesian family language(a... |
9d32dbd2afdb4f09697dd5babc543b98daaafdec |
# id_panl_bppt
Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and
Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing
Capacity in Asia). The dataset contains about 24K sentences in English... | SEACrowd/id_panl_bppt | [
"language:ind",
"machine-translation",
"region:us"
] | 2023-09-26T10:16:17+00:00 | {"language": ["ind"], "tags": ["machine-translation"]} | 2023-09-26T11:32:02+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #machine-translation #region-us
|
# id_panl_bppt
Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and
Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing
Capacity in Asia). The dataset contains about 24K sentences in English... | [
"# id_panl_bppt\n\nParallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and\n\nApplication of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing\n\nCapacity in Asia). The dataset contains about 24K sentences ... | [
"TAGS\n#language-Indonesian #machine-translation #region-us \n",
"# id_panl_bppt\n\nParallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and\n\nApplication of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Comput... | [
16,
90,
35,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #machine-translation #region-us \n# id_panl_bppt\n\nParallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and\n\nApplication of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Com... |
1abb654f7ff5fb4cb5ad63691e6cb5fb634b8aa9 |
# inset_lexicon
InSet, an Indonesian sentiment lexicon built to identify written opinion and categorize it into positive or negative opinion,
which could be utilized to analyze public sentiment towards particular topic, event, or product. Composed using collection
of words from Indonesian tweet, InSet was construct... | SEACrowd/inset_lexicon | [
"language:ind",
"license:unknown",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:16:19+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["sentiment-analysis"]} | 2023-09-26T11:32:05+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #sentiment-analysis #region-us
|
# inset_lexicon
InSet, an Indonesian sentiment lexicon built to identify written opinion and categorize it into positive or negative opinion,
which could be utilized to analyze public sentiment towards particular topic, event, or product. Composed using collection
of words from Indonesian tweet, InSet was construct... | [
"# inset_lexicon\n\nInSet, an Indonesian sentiment lexicon built to identify written opinion and categorize it into positive or negative opinion,\n\nwhich could be utilized to analyze public sentiment towards particular topic, event, or product. Composed using collection\n\nof words from Indonesian tweet, InSet was... | [
"TAGS\n#language-Indonesian #license-unknown #sentiment-analysis #region-us \n",
"# inset_lexicon\n\nInSet, an Indonesian sentiment lexicon built to identify written opinion and categorize it into positive or negative opinion,\n\nwhich could be utilized to analyze public sentiment towards particular topic, event,... | [
24,
87,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #sentiment-analysis #region-us \n# inset_lexicon\n\nInSet, an Indonesian sentiment lexicon built to identify written opinion and categorize it into positive or negative opinion,\n\nwhich could be utilized to analyze public sentiment towards particular topic, eve... |
c78a163be1a9f22c890166cd35a7b7acb6156bda |
# titml_idn
TITML-IDN (Tokyo Institute of Technology Multilingual - Indonesian) is collected to build a pioneering Indonesian Large Vocabulary Continuous Speech Recognition (LVCSR) System. In order to build an LVCSR system, high accurate acoustic models and large-scale language models are essential. Since Indonesian ... | SEACrowd/titml_idn | [
"language:ind",
"speech-recognition",
"region:us"
] | 2023-09-26T10:16:22+00:00 | {"language": ["ind"], "tags": ["speech-recognition"]} | 2023-09-26T11:32:09+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #speech-recognition #region-us
|
# titml_idn
TITML-IDN (Tokyo Institute of Technology Multilingual - Indonesian) is collected to build a pioneering Indonesian Large Vocabulary Continuous Speech Recognition (LVCSR) System. In order to build an LVCSR system, high accurate acoustic models and large-scale language models are essential. Since Indonesian ... | [
"# titml_idn\n\nTITML-IDN (Tokyo Institute of Technology Multilingual - Indonesian) is collected to build a pioneering Indonesian Large Vocabulary Continuous Speech Recognition (LVCSR) System. In order to build an LVCSR system, high accurate acoustic models and large-scale language models are essential. Since Indon... | [
"TAGS\n#language-Indonesian #speech-recognition #region-us \n",
"# titml_idn\n\nTITML-IDN (Tokyo Institute of Technology Multilingual - Indonesian) is collected to build a pioneering Indonesian Large Vocabulary Continuous Speech Recognition (LVCSR) System. In order to build an LVCSR system, high accurate acoustic... | [
18,
180,
35,
26,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #speech-recognition #region-us \n# titml_idn\n\nTITML-IDN (Tokyo Institute of Technology Multilingual - Indonesian) is collected to build a pioneering Indonesian Large Vocabulary Continuous Speech Recognition (LVCSR) System. In order to build an LVCSR system, high accurate acous... |
b156530275f4a3933c9e68c442c07c1328f72eff |
# posp
POSP is a POS Tagging dataset containing 8400 sentences, collected from Indonesian news website with 26 POS tag classes.
The POS tag labels follow the Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention.
POSP dataset is splitted into 3 sets with 6720 train, 840 validation, and ... | SEACrowd/posp | [
"language:ind",
"pos-tagging",
"region:us"
] | 2023-09-26T10:16:27+00:00 | {"language": ["ind"], "tags": ["pos-tagging"]} | 2023-09-26T11:32:13+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #pos-tagging #region-us
|
# posp
POSP is a POS Tagging dataset containing 8400 sentences, collected from Indonesian news website with 26 POS tag classes.
The POS tag labels follow the Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention.
POSP dataset is splitted into 3 sets with 6720 train, 840 validation, and ... | [
"# posp\n\nPOSP is a POS Tagging dataset containing 8400 sentences, collected from Indonesian news website with 26 POS tag classes.\n\nThe POS tag labels follow the Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention.\n\nPOSP dataset is splitted into 3 sets with 6720 train, 840 valida... | [
"TAGS\n#language-Indonesian #pos-tagging #region-us \n",
"# posp\n\nPOSP is a POS Tagging dataset containing 8400 sentences, collected from Indonesian news website with 26 POS tag classes.\n\nThe POS tag labels follow the Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention.\n\nPOSP ... | [
16,
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35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #pos-tagging #region-us \n# posp\n\nPOSP is a POS Tagging dataset containing 8400 sentences, collected from Indonesian news website with 26 POS tag classes.\n\nThe POS tag labels follow the Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention.\n\nPO... |
eaba0344b80f4f72c5d5e6de0e1a476ad4d58d7c |
# nusax_senti
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-Senti is a 3-labels (positive, neutral, negat... | SEACrowd/nusax_senti | [
"language:ind",
"language:ace",
"language:ban",
"language:bjn",
"language:bbc",
"language:bug",
"language:jav",
"language:mad",
"language:min",
"language:nij",
"language:sun",
"language:eng",
"sentiment-analysis",
"arxiv:2205.15960",
"region:us"
] | 2023-09-26T10:16:31+00:00 | {"language": ["ind", "ace", "ban", "bjn", "bbc", "bug", "jav", "mad", "min", "nij", "sun", "eng"], "tags": ["sentiment-analysis"]} | 2023-09-26T11:32:17+00:00 | [
"2205.15960"
] | [
"ind",
"ace",
"ban",
"bjn",
"bbc",
"bug",
"jav",
"mad",
"min",
"nij",
"sun",
"eng"
] | TAGS
#language-Indonesian #language-Achinese #language-Balinese #language-Banjar #language-Batak Toba #language-Buginese #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-English #sentiment-analysis #arxiv-2205.15960 #region-us
|
# nusax_senti
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-Senti is a 3-labels (positive, neutral, negat... | [
"# nusax_senti\n\nNusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.\n\n\n\nNusaX-Senti is a 3-labels (positive, neut... | [
"TAGS\n#language-Indonesian #language-Achinese #language-Balinese #language-Banjar #language-Batak Toba #language-Buginese #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-English #sentiment-analysis #arxiv-2205.15960 #region-us \n",
"# nusax_senti\n\nNusaX... | [
88,
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35,
10,
3,
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"passage: TAGS\n#language-Indonesian #language-Achinese #language-Balinese #language-Banjar #language-Batak Toba #language-Buginese #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-English #sentiment-analysis #arxiv-2205.15960 #region-us \n# nusax_senti\n\nNu... |
217d1dc5f8c3bde8d2cc56d3f7d344208b6aef2a |
# barasa
The Barasa dataset is an Indonesian SentiWordNet for sentiment analysis.
For each term, the pair (POS,ID) uniquely identifies a WordNet (3.0) synset and there are PosScore and NegScore to show the positivity and negativity of the term.
The objectivity score can be calculated as: ObjScore = 1 - (PosScore + ... | SEACrowd/barasa | [
"language:ind",
"license:mit",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:16:41+00:00 | {"language": ["ind"], "license": "mit", "tags": ["sentiment-analysis"]} | 2023-09-26T11:32:24+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-mit #sentiment-analysis #region-us
|
# barasa
The Barasa dataset is an Indonesian SentiWordNet for sentiment analysis.
For each term, the pair (POS,ID) uniquely identifies a WordNet (3.0) synset and there are PosScore and NegScore to show the positivity and negativity of the term.
The objectivity score can be calculated as: ObjScore = 1 - (PosScore + ... | [
"# barasa\n\nThe Barasa dataset is an Indonesian SentiWordNet for sentiment analysis.\n\nFor each term, the pair (POS,ID) uniquely identifies a WordNet (3.0) synset and there are PosScore and NegScore to show the positivity and negativity of the term.\n\nThe objectivity score can be calculated as: ObjScore = 1 - (P... | [
"TAGS\n#language-Indonesian #license-mit #sentiment-analysis #region-us \n",
"# barasa\n\nThe Barasa dataset is an Indonesian SentiWordNet for sentiment analysis.\n\nFor each term, the pair (POS,ID) uniquely identifies a WordNet (3.0) synset and there are PosScore and NegScore to show the positivity and negativit... | [
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"passage: TAGS\n#language-Indonesian #license-mit #sentiment-analysis #region-us \n# barasa\n\nThe Barasa dataset is an Indonesian SentiWordNet for sentiment analysis.\n\nFor each term, the pair (POS,ID) uniquely identifies a WordNet (3.0) synset and there are PosScore and NegScore to show the positivity and negati... |
5213f19d2ca0a6a0684b79541c951ecfe1aafbde |
# nusatranslation_emot
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. ... | SEACrowd/nusatranslation_emot | [
"language:abs",
"language:btk",
"language:bew",
"language:bug",
"language:jav",
"language:mad",
"language:mak",
"language:min",
"language:mui",
"language:rej",
"language:sun",
"emotion-classification",
"region:us"
] | 2023-09-26T10:16:49+00:00 | {"language": ["abs", "btk", "bew", "bug", "jav", "mad", "mak", "min", "mui", "rej", "sun"], "tags": ["emotion-classification"]} | 2023-09-26T11:32:34+00:00 | [] | [
"abs",
"btk",
"bew",
"bug",
"jav",
"mad",
"mak",
"min",
"mui",
"rej",
"sun"
] | TAGS
#language-Ambonese Malay #language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #emotion-classification #region-us
|
# nusatranslation_emot
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. ... | [
"# nusatranslation_emot\n\nDemocratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document transla... | [
"TAGS\n#language-Ambonese Malay #language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #emotion-classification #region-us \n",
"# nusatranslation_emot\n\nDemocratizing access to natural lan... | [
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35,
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"passage: TAGS\n#language-Ambonese Malay #language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #emotion-classification #region-us \n# nusatranslation_emot\n\nDemocratizing access to natural ... |
c07c51f1cb349c7f57a0d93224fad4acd7093855 |
# indolem_ud_id_pud
1 of 8 sub-datasets of IndoLEM, a comprehensive dataset encompassing 7 NLP tasks (Koto et al., 2020).
This dataset is part of [Parallel Universal Dependencies (PUD)](http://universaldependencies.org/conll17/) project.
This is based on the first corrected version by Alfina et al. (2019), contains... | SEACrowd/indolem_ud_id_pud | [
"language:ind",
"license:cc-by-4.0",
"dependency-parsing",
"arxiv:2011.00677",
"region:us"
] | 2023-09-26T10:17:04+00:00 | {"language": ["ind"], "license": "cc-by-4.0", "tags": ["dependency-parsing"]} | 2023-09-26T11:32:43+00:00 | [
"2011.00677"
] | [
"ind"
] | TAGS
#language-Indonesian #license-cc-by-4.0 #dependency-parsing #arxiv-2011.00677 #region-us
|
# indolem_ud_id_pud
1 of 8 sub-datasets of IndoLEM, a comprehensive dataset encompassing 7 NLP tasks (Koto et al., 2020).
This dataset is part of Parallel Universal Dependencies (PUD) project.
This is based on the first corrected version by Alfina et al. (2019), contains 1,000 sentences.
## Dataset Usage
Run 'pip... | [
"# indolem_ud_id_pud\n\n1 of 8 sub-datasets of IndoLEM, a comprehensive dataset encompassing 7 NLP tasks (Koto et al., 2020).\n\nThis dataset is part of Parallel Universal Dependencies (PUD) project.\n\nThis is based on the first corrected version by Alfina et al. (2019), contains 1,000 sentences.",
"## Dataset U... | [
"TAGS\n#language-Indonesian #license-cc-by-4.0 #dependency-parsing #arxiv-2011.00677 #region-us \n",
"# indolem_ud_id_pud\n\n1 of 8 sub-datasets of IndoLEM, a comprehensive dataset encompassing 7 NLP tasks (Koto et al., 2020).\n\nThis dataset is part of Parallel Universal Dependencies (PUD) project.\n\nThis is ba... | [
35,
85,
35,
6,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-cc-by-4.0 #dependency-parsing #arxiv-2011.00677 #region-us \n# indolem_ud_id_pud\n\n1 of 8 sub-datasets of IndoLEM, a comprehensive dataset encompassing 7 NLP tasks (Koto et al., 2020).\n\nThis dataset is part of Parallel Universal Dependencies (PUD) project.\n\nThis is... |
557f055d9adb271f894b59b22c81195acf4558ba |
# parallel_id_nyo
Dataset that contains Indonesian - Lampung language pairs.
The original data should contains 3000 rows, unfortunately,
not all of the instances in the original data is aligned perfectly.
Thus, this data only have the aligned ones, which only contain 1727 pairs.
## Dataset Usage
Run `pip insta... | SEACrowd/parallel_id_nyo | [
"language:ind",
"language:abl",
"license:unknown",
"machine-translation",
"region:us"
] | 2023-09-26T10:17:11+00:00 | {"language": ["ind", "abl"], "license": "unknown", "tags": ["machine-translation"]} | 2023-09-26T11:32:49+00:00 | [] | [
"ind",
"abl"
] | TAGS
#language-Indonesian #language-Lampung Nyo #license-unknown #machine-translation #region-us
|
# parallel_id_nyo
Dataset that contains Indonesian - Lampung language pairs.
The original data should contains 3000 rows, unfortunately,
not all of the instances in the original data is aligned perfectly.
Thus, this data only have the aligned ones, which only contain 1727 pairs.
## Dataset Usage
Run 'pip insta... | [
"# parallel_id_nyo\n\nDataset that contains Indonesian - Lampung language pairs.\n\n\n\nThe original data should contains 3000 rows, unfortunately,\n\nnot all of the instances in the original data is aligned perfectly.\n\nThus, this data only have the aligned ones, which only contain 1727 pairs.",
"## Dataset Usa... | [
"TAGS\n#language-Indonesian #language-Lampung Nyo #license-unknown #machine-translation #region-us \n",
"# parallel_id_nyo\n\nDataset that contains Indonesian - Lampung language pairs.\n\n\n\nThe original data should contains 3000 rows, unfortunately,\n\nnot all of the instances in the original data is aligned pe... | [
30,
70,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-Lampung Nyo #license-unknown #machine-translation #region-us \n# parallel_id_nyo\n\nDataset that contains Indonesian - Lampung language pairs.\n\n\n\nThe original data should contains 3000 rows, unfortunately,\n\nnot all of the instances in the original data is aligned... |
b097c588182c36af5479bd7970adde4247736c44 |
# bible_en_id
Bible En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the bible. We also add a Bible dataset to the English Indonesian translation task. Specifically, we collect an Indonesian and an English language Bible and generate a verse-aligned parallel corpu... | SEACrowd/bible_en_id | [
"language:ind",
"language:eng",
"machine-translation",
"region:us"
] | 2023-09-26T10:17:14+00:00 | {"language": ["ind", "eng"], "tags": ["machine-translation"]} | 2023-09-26T11:32:53+00:00 | [] | [
"ind",
"eng"
] | TAGS
#language-Indonesian #language-English #machine-translation #region-us
|
# bible_en_id
Bible En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the bible. We also add a Bible dataset to the English Indonesian translation task. Specifically, we collect an Indonesian and an English language Bible and generate a verse-aligned parallel corpu... | [
"# bible_en_id\n\nBible En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the bible. We also add a Bible dataset to the English Indonesian translation task. Specifically, we collect an Indonesian and an English language Bible and generate a verse-aligned parallel... | [
"TAGS\n#language-Indonesian #language-English #machine-translation #region-us \n",
"# bible_en_id\n\nBible En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the bible. We also add a Bible dataset to the English Indonesian translation task. Specifically, we coll... | [
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"passage: TAGS\n#language-Indonesian #language-English #machine-translation #region-us \n# bible_en_id\n\nBible En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the bible. We also add a Bible dataset to the English Indonesian translation task. Specifically, we c... |
ba1d3cbc4dd93acf67ede52abe0d9cdfb3daf441 |
# wikiann
The wikiann dataset contains NER tags with labels from O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4), B-LOC (5), I-LOC (6). The Indonesian subset is used.
WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles
annotated with LOC (location)... | SEACrowd/wikiann | [
"language:ind",
"language:eng",
"language:jav",
"language:min",
"language:sun",
"language:ace",
"language:mly",
"named-entity-recognition",
"region:us"
] | 2023-09-26T10:17:18+00:00 | {"language": ["ind", "eng", "jav", "min", "sun", "ace", "mly"], "tags": ["named-entity-recognition"]} | 2023-09-26T11:32:59+00:00 | [] | [
"ind",
"eng",
"jav",
"min",
"sun",
"ace",
"mly"
] | TAGS
#language-Indonesian #language-English #language-Javanese #language-Minangkabau #language-Sundanese #language-Achinese #language-Malay (individual language) #named-entity-recognition #region-us
|
# wikiann
The wikiann dataset contains NER tags with labels from O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4), B-LOC (5), I-LOC (6). The Indonesian subset is used.
WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles
annotated with LOC (location)... | [
"# wikiann\n\nThe wikiann dataset contains NER tags with labels from O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4), B-LOC (5), I-LOC (6). The Indonesian subset is used.\n\nWikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles\n\n annotated with LOC ... | [
"TAGS\n#language-Indonesian #language-English #language-Javanese #language-Minangkabau #language-Sundanese #language-Achinese #language-Malay (individual language) #named-entity-recognition #region-us \n",
"# wikiann\n\nThe wikiann dataset contains NER tags with labels from O (0), B-PER (1), I-PER (2), B-ORG (3),... | [
58,
202,
35,
7,
3,
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] | [
"passage: TAGS\n#language-Indonesian #language-English #language-Javanese #language-Minangkabau #language-Sundanese #language-Achinese #language-Malay (individual language) #named-entity-recognition #region-us \n# wikiann\n\nThe wikiann dataset contains NER tags with labels from O (0), B-PER (1), I-PER (2), B-ORG (... |
d0218b4ce51e265291b37432c7d26876bc95e164 |
# news_en_id
News En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the news. The news dataset is collected from multiple sources: Pan Asia Networking Localization (PANL), Bilingual BBC news articles, Berita Jakarta, and GlobalVoices. We split the dataset and use 7... | SEACrowd/news_en_id | [
"language:ind",
"language:eng",
"machine-translation",
"region:us"
] | 2023-09-26T10:17:22+00:00 | {"language": ["ind", "eng"], "tags": ["machine-translation"]} | 2023-09-26T11:33:03+00:00 | [] | [
"ind",
"eng"
] | TAGS
#language-Indonesian #language-English #machine-translation #region-us
|
# news_en_id
News En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the news. The news dataset is collected from multiple sources: Pan Asia Networking Localization (PANL), Bilingual BBC news articles, Berita Jakarta, and GlobalVoices. We split the dataset and use 7... | [
"# news_en_id\n\nNews En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the news. The news dataset is collected from multiple sources: Pan Asia Networking Localization (PANL), Bilingual BBC news articles, Berita Jakarta, and GlobalVoices. We split the dataset and... | [
"TAGS\n#language-Indonesian #language-English #machine-translation #region-us \n",
"# news_en_id\n\nNews En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the news. The news dataset is collected from multiple sources: Pan Asia Networking Localization (PANL), Bi... | [
20,
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35,
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3,
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] | [
"passage: TAGS\n#language-Indonesian #language-English #machine-translation #region-us \n# news_en_id\n\nNews En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the news. The news dataset is collected from multiple sources: Pan Asia Networking Localization (PANL),... |
9c3403dedc07197b35f03d26a7960cbc7c8c5335 |
# unimorph_id
The UniMorph project, Indonesian chapter.
Due to sparsity of UniMorph original parsing, raw source is used instead.
Original parsing can be found on https://huggingface.co/datasets/universal_morphologies/blob/2.3.2/universal_morphologies.py
## Dataset Usage
Run `pip install nusacrowd` before loading... | SEACrowd/unimorph_id | [
"language:ind",
"morphological-inflection",
"region:us"
] | 2023-09-26T10:17:31+00:00 | {"language": ["ind"], "tags": ["morphological-inflection"]} | 2023-09-26T11:33:11+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #morphological-inflection #region-us
|
# unimorph_id
The UniMorph project, Indonesian chapter.
Due to sparsity of UniMorph original parsing, raw source is used instead.
Original parsing can be found on URL
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
Creative Commons Attribu... | [
"# unimorph_id\n\nThe UniMorph project, Indonesian chapter.\n\nDue to sparsity of UniMorph original parsing, raw source is used instead.\n\nOriginal parsing can be found on URL",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nCr... | [
"TAGS\n#language-Indonesian #morphological-inflection #region-us \n",
"# unimorph_id\n\nThe UniMorph project, Indonesian chapter.\n\nDue to sparsity of UniMorph original parsing, raw source is used instead.\n\nOriginal parsing can be found on URL",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading... | [
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"passage: TAGS\n#language-Indonesian #morphological-inflection #region-us \n# unimorph_id\n\nThe UniMorph project, Indonesian chapter.\n\nDue to sparsity of UniMorph original parsing, raw source is used instead.\n\nOriginal parsing can be found on URL## Dataset Usage\n\nRun 'pip install nusacrowd' before loading th... |
39c01bd904852b31849c89337217ff8aae19efb8 |
# indspeech_digit_cdsr
INDspeech_DIGIT_CDSR is the first Indonesian speech dataset for connected digit speech recognition (CDSR). The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan and Bandung ... | SEACrowd/indspeech_digit_cdsr | [
"language:ind",
"speech-recognition",
"region:us"
] | 2023-09-26T10:17:36+00:00 | {"language": ["ind"], "tags": ["speech-recognition"]} | 2023-09-26T11:33:16+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #speech-recognition #region-us
|
# indspeech_digit_cdsr
INDspeech_DIGIT_CDSR is the first Indonesian speech dataset for connected digit speech recognition (CDSR). The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan and Bandung ... | [
"# indspeech_digit_cdsr\n\nINDspeech_DIGIT_CDSR is the first Indonesian speech dataset for connected digit speech recognition (CDSR). The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan and Ba... | [
"TAGS\n#language-Indonesian #speech-recognition #region-us \n",
"# indspeech_digit_cdsr\n\nINDspeech_DIGIT_CDSR is the first Indonesian speech dataset for connected digit speech recognition (CDSR). The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced Tel... | [
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"passage: TAGS\n#language-Indonesian #speech-recognition #region-us \n# indspeech_digit_cdsr\n\nINDspeech_DIGIT_CDSR is the first Indonesian speech dataset for connected digit speech recognition (CDSR). The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced ... |
3a88ae129d048e482cdf9b4209ef26ea0d85188a |
# indo_religious_mt_en_id
Indonesian Religious Domain MT En-Id consists of religious manuscripts or articles. These articles are different from news as they are not in a formal, informative style. Instead, they are written to advocate and inspire religious values, often times citing biblical or quranic anecdotes. An ... | SEACrowd/indo_religious_mt_en_id | [
"language:ind",
"language:eng",
"machine-translation",
"region:us"
] | 2023-09-26T10:17:41+00:00 | {"language": ["ind", "eng"], "tags": ["machine-translation"]} | 2023-09-26T11:33:20+00:00 | [] | [
"ind",
"eng"
] | TAGS
#language-Indonesian #language-English #machine-translation #region-us
|
# indo_religious_mt_en_id
Indonesian Religious Domain MT En-Id consists of religious manuscripts or articles. These articles are different from news as they are not in a formal, informative style. Instead, they are written to advocate and inspire religious values, often times citing biblical or quranic anecdotes. An ... | [
"# indo_religious_mt_en_id\n\nIndonesian Religious Domain MT En-Id consists of religious manuscripts or articles. These articles are different from news as they are not in a formal, informative style. Instead, they are written to advocate and inspire religious values, often times citing biblical or quranic anecdote... | [
"TAGS\n#language-Indonesian #language-English #machine-translation #region-us \n",
"# indo_religious_mt_en_id\n\nIndonesian Religious Domain MT En-Id consists of religious manuscripts or articles. These articles are different from news as they are not in a formal, informative style. Instead, they are written to a... | [
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210,
35,
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] | [
"passage: TAGS\n#language-Indonesian #language-English #machine-translation #region-us \n# indo_religious_mt_en_id\n\nIndonesian Religious Domain MT En-Id consists of religious manuscripts or articles. These articles are different from news as they are not in a formal, informative style. Instead, they are written t... |
49003b4e5130b0f21a45f270f1ed9723cd7bfa1d |
# nusaparagraph_topic
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. W... | SEACrowd/nusaparagraph_topic | [
"language:btk",
"language:bew",
"language:bug",
"language:jav",
"language:mad",
"language:mak",
"language:min",
"language:mui",
"language:rej",
"language:sun",
"topic-modeling",
"region:us"
] | 2023-09-26T10:17:48+00:00 | {"language": ["btk", "bew", "bug", "jav", "mad", "mak", "min", "mui", "rej", "sun"], "tags": ["topic-modeling"]} | 2023-09-26T11:33:27+00:00 | [] | [
"btk",
"bew",
"bug",
"jav",
"mad",
"mak",
"min",
"mui",
"rej",
"sun"
] | TAGS
#language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #topic-modeling #region-us
|
# nusaparagraph_topic
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. W... | [
"# nusaparagraph_topic\n\nDemocratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translat... | [
"TAGS\n#language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #topic-modeling #region-us \n",
"# nusaparagraph_topic\n\nDemocratizing access to natural language processing (NLP) technology ... | [
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"passage: TAGS\n#language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #topic-modeling #region-us \n# nusaparagraph_topic\n\nDemocratizing access to natural language processing (NLP) technolo... |
753eb15978a7c0518b611933e21540d98cc2a0c5 |
# id_clickbait
The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news
publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,
Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i)... | SEACrowd/id_clickbait | [
"language:ind",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:17:57+00:00 | {"language": ["ind"], "tags": ["sentiment-analysis"]} | 2023-09-26T11:33:36+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #sentiment-analysis #region-us
|
# id_clickbait
The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news
publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,
Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i)... | [
"# id_clickbait\n\nThe CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news\n\npublishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,\n\nTribunnews, and Wowkeren. This dataset is comprised of mainly two ... | [
"TAGS\n#language-Indonesian #sentiment-analysis #region-us \n",
"# id_clickbait\n\nThe CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news\n\npublishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,\n\n... | [
17,
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"passage: TAGS\n#language-Indonesian #sentiment-analysis #region-us \n# id_clickbait\n\nThe CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news\n\npublishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,\... |
dafe6a4c0e94236e5b98821333042f339c9cc93b |
# facqa
FacQA: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article.
Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the
corresponding short passage. There are six categories of questi... | SEACrowd/facqa | [
"language:ind",
"question-answering",
"region:us"
] | 2023-09-26T10:18:01+00:00 | {"language": ["ind"], "tags": ["question-answering"]} | 2023-09-26T11:33:40+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #question-answering #region-us
|
# facqa
FacQA: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article.
Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the
corresponding short passage. There are six categories of questi... | [
"# facqa\n\nFacQA: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article.\n\nEach row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the\n\ncorresponding short passage. There are six categories... | [
"TAGS\n#language-Indonesian #question-answering #region-us \n",
"# facqa\n\nFacQA: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article.\n\nEach row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found i... | [
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"passage: TAGS\n#language-Indonesian #question-answering #region-us \n# facqa\n\nFacQA: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article.\n\nEach row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be foun... |
70a736e55ab6c805e5bdc2460848a95d80e9a8b9 |
# indolem_tweet_ordering
IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.
This task is based on the sentence order... | SEACrowd/indolem_tweet_ordering | [
"language:ind",
"license:cc-by-4.0",
"sentence-ordering",
"arxiv:2011.00677",
"region:us"
] | 2023-09-26T10:18:05+00:00 | {"language": ["ind"], "license": "cc-by-4.0", "tags": ["sentence-ordering"]} | 2023-09-26T11:34:03+00:00 | [
"2011.00677"
] | [
"ind"
] | TAGS
#language-Indonesian #license-cc-by-4.0 #sentence-ordering #arxiv-2011.00677 #region-us
|
# indolem_tweet_ordering
IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.
This task is based on the sentence order... | [
"# indolem_tweet_ordering\n\nIndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.\n\nThis task is based on the senten... | [
"TAGS\n#language-Indonesian #license-cc-by-4.0 #sentence-ordering #arxiv-2011.00677 #region-us \n",
"# indolem_tweet_ordering\n\nIndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into th... | [
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"passage: TAGS\n#language-Indonesian #license-cc-by-4.0 #sentence-ordering #arxiv-2011.00677 #region-us \n# indolem_tweet_ordering\n\nIndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into... |
6cce9388ba73129b171b67ee977e2e0fc35944cd |
# INDspeech_NEWS_TTS
INDspeech_NEWS_TTS is a speech dataset for developing an Indonesian text-to-speech synthesis system. The data was developed by Advanced Telecommunication Research Institute International (ATR) Japan under the the Asian speech translation advanced research (A-STAR) project [Sakti et al., 2013].
#... | SEACrowd/indspeech_news_tts | [
"language:ind",
"text-to-speech",
"region:us"
] | 2023-09-26T10:18:05+00:00 | {"language": ["ind"], "tags": ["text-to-speech"]} | 2023-09-26T11:34:11+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #text-to-speech #region-us
|
# INDspeech_NEWS_TTS
INDspeech_NEWS_TTS is a speech dataset for developing an Indonesian text-to-speech synthesis system. The data was developed by Advanced Telecommunication Research Institute International (ATR) Japan under the the Asian speech translation advanced research (A-STAR) project [Sakti et al., 2013].
#... | [
"# INDspeech_NEWS_TTS\n\nINDspeech_NEWS_TTS is a speech dataset for developing an Indonesian text-to-speech synthesis system. The data was developed by Advanced Telecommunication Research Institute International (ATR) Japan under the the Asian speech translation advanced research (A-STAR) project [Sakti et al., 201... | [
"TAGS\n#language-Indonesian #text-to-speech #region-us \n",
"# INDspeech_NEWS_TTS\n\nINDspeech_NEWS_TTS is a speech dataset for developing an Indonesian text-to-speech synthesis system. The data was developed by Advanced Telecommunication Research Institute International (ATR) Japan under the the Asian speech tra... | [
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"passage: TAGS\n#language-Indonesian #text-to-speech #region-us \n# INDspeech_NEWS_TTS\n\nINDspeech_NEWS_TTS is a speech dataset for developing an Indonesian text-to-speech synthesis system. The data was developed by Advanced Telecommunication Research Institute International (ATR) Japan under the the Asian speech ... |
e8c93d801b00fc6ace7ce3326495881bdbb8c03f |
# nusax_mt
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-MT is a parallel corpus for training and benchma... | SEACrowd/nusax_mt | [
"language:ind",
"language:ace",
"language:ban",
"language:bjn",
"language:bbc",
"language:bug",
"language:jav",
"language:mad",
"language:min",
"language:nij",
"language:sun",
"language:eng",
"machine-translation",
"arxiv:2205.15960",
"region:us"
] | 2023-09-26T10:18:05+00:00 | {"language": ["ind", "ace", "ban", "bjn", "bbc", "bug", "jav", "mad", "min", "nij", "sun", "eng"], "tags": ["machine-translation"]} | 2023-09-26T11:34:19+00:00 | [
"2205.15960"
] | [
"ind",
"ace",
"ban",
"bjn",
"bbc",
"bug",
"jav",
"mad",
"min",
"nij",
"sun",
"eng"
] | TAGS
#language-Indonesian #language-Achinese #language-Balinese #language-Banjar #language-Batak Toba #language-Buginese #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-English #machine-translation #arxiv-2205.15960 #region-us
|
# nusax_mt
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-MT is a parallel corpus for training and benchma... | [
"# nusax_mt\n\nNusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.\n\n\n\nNusaX-MT is a parallel corpus for training a... | [
"TAGS\n#language-Indonesian #language-Achinese #language-Balinese #language-Banjar #language-Batak Toba #language-Buginese #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-English #machine-translation #arxiv-2205.15960 #region-us \n",
"# nusax_mt\n\nNusaX i... | [
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"passage: TAGS\n#language-Indonesian #language-Achinese #language-Balinese #language-Banjar #language-Batak Toba #language-Buginese #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-English #machine-translation #arxiv-2205.15960 #region-us \n# nusax_mt\n\nNusa... |
c7cbdb4005649662032420d9241cb19137ec40e9 |
# xcopa
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across
languages. The dataset is the translation and reannotation of the English COPA ... | SEACrowd/xcopa | [
"language:ind",
"license:unknown",
"question-answering",
"region:us"
] | 2023-09-26T10:18:06+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["question-answering"]} | 2023-09-26T11:34:53+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #question-answering #region-us
|
# xcopa
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across
languages. The dataset is the translation and reannotation of the English COPA ... | [
"# xcopa\n\nXCOPA: A Multilingual Dataset for Causal Commonsense Reasoning\n\nThe Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across\n\nlanguages. The dataset is the translation and reannotation of the Eng... | [
"TAGS\n#language-Indonesian #license-unknown #question-answering #region-us \n",
"# xcopa\n\nXCOPA: A Multilingual Dataset for Causal Commonsense Reasoning\n\nThe Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reason... | [
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"passage: TAGS\n#language-Indonesian #license-unknown #question-answering #region-us \n# xcopa\n\nXCOPA: A Multilingual Dataset for Causal Commonsense Reasoning\n\nThe Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense rea... |
853f899dad8718f4ef90f45905118113201d1212 |
# nergrit
Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition (NER), Statement Extraction,
and Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT).
The Named Entity Recognition contains 18 entities as follow:
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': ... | SEACrowd/nergrit | [
"language:ind",
"license:mit",
"named-entity-recognition",
"region:us"
] | 2023-09-26T10:18:07+00:00 | {"language": ["ind"], "license": "mit", "tags": ["named-entity-recognition"]} | 2023-09-26T11:35:09+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-mit #named-entity-recognition #region-us
|
# nergrit
Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition (NER), Statement Extraction,
and Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT).
The Named Entity Recognition contains 18 entities as follow:
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': ... | [
"# nergrit\n\nNergrit Corpus is a dataset collection of Indonesian Named Entity Recognition (NER), Statement Extraction,\n\nand Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT).\n\nThe Named Entity Recognition contains 18 entities as follow:\n\n 'CRD': Cardinal\n\n 'DAT': Date\n\n 'EVT': Ev... | [
"TAGS\n#language-Indonesian #license-mit #named-entity-recognition #region-us \n",
"# nergrit\n\nNergrit Corpus is a dataset collection of Indonesian Named Entity Recognition (NER), Statement Extraction,\n\nand Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT).\n\nThe Named Entity Recognition conta... | [
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"passage: TAGS\n#language-Indonesian #license-mit #named-entity-recognition #region-us \n# nergrit\n\nNergrit Corpus is a dataset collection of Indonesian Named Entity Recognition (NER), Statement Extraction,\n\nand Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT).\n\nThe Named Entity Recognition co... |
c8c9db92f9dba4b8d04309d3727f79cce3201532 |
# karonese_sentiment
Karonese sentiment was crawled from Twitter between 1 January 2021 and 31 October 2021.
The first crawling process used several keywords related to the Karonese, such as
"deleng sinabung, Sinabung mountain", "mejuah-juah, greeting welcome", "Gundaling",
and so on. However, due to the insuffici... | SEACrowd/karonese_sentiment | [
"language:btx",
"license:unknown",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:18:07+00:00 | {"language": ["btx"], "license": "unknown", "tags": ["sentiment-analysis"]} | 2023-09-26T11:35:18+00:00 | [] | [
"btx"
] | TAGS
#language-Batak Karo #license-unknown #sentiment-analysis #region-us
|
# karonese_sentiment
Karonese sentiment was crawled from Twitter between 1 January 2021 and 31 October 2021.
The first crawling process used several keywords related to the Karonese, such as
"deleng sinabung, Sinabung mountain", "mejuah-juah, greeting welcome", "Gundaling",
and so on. However, due to the insuffici... | [
"# karonese_sentiment\n\nKaronese sentiment was crawled from Twitter between 1 January 2021 and 31 October 2021.\n\nThe first crawling process used several keywords related to the Karonese, such as\n\n\"deleng sinabung, Sinabung mountain\", \"mejuah-juah, greeting welcome\", \"Gundaling\",\n\nand so on. However, du... | [
"TAGS\n#language-Batak Karo #license-unknown #sentiment-analysis #region-us \n",
"# karonese_sentiment\n\nKaronese sentiment was crawled from Twitter between 1 January 2021 and 31 October 2021.\n\nThe first crawling process used several keywords related to the Karonese, such as\n\n\"deleng sinabung, Sinabung moun... | [
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35,
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"passage: TAGS\n#language-Batak Karo #license-unknown #sentiment-analysis #region-us \n# karonese_sentiment\n\nKaronese sentiment was crawled from Twitter between 1 January 2021 and 31 October 2021.\n\nThe first crawling process used several keywords related to the Karonese, such as\n\n\"deleng sinabung, Sinabung m... |
994beebe4c00b0d503623c00edf963ede3ac24d7 |
# Court of Cassation
[The major decisions of judicial jurisprudence](https://www.data.gouv.fr/en/datasets/cass/); the decisions of the Cour de cassation :
- published in the Bulletin des chambres civiles since 1960
- published in the Bulletin de la chambre criminelle since 1963.
Full text of rulings, supplemented b... | Nicolas-BZRD/CASS_opendata | [
"size_categories:100K<n<1M",
"language:fr",
"license:odc-by",
"legal",
"region:us"
] | 2023-09-26T10:27:18+00:00 | {"language": ["fr"], "license": "odc-by", "size_categories": ["100K<n<1M"], "pretty_name": "Cour de cassation", "tags": ["legal"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype... | 2023-09-28T09:30:20+00:00 | [] | [
"fr"
] | TAGS
#size_categories-100K<n<1M #language-French #license-odc-by #legal #region-us
|
# Court of Cassation
The major decisions of judicial jurisprudence; the decisions of the Cour de cassation :
- published in the Bulletin des chambres civiles since 1960
- published in the Bulletin de la chambre criminelle since 1963.
Full text of rulings, supplemented by columns and summaries written by Court of Ca... | [
"# Court of Cassation\n\nThe major decisions of judicial jurisprudence; the decisions of the Cour de cassation :\n - published in the Bulletin des chambres civiles since 1960\n- published in the Bulletin de la chambre criminelle since 1963.\n\nFull text of rulings, supplemented by columns and summaries written by C... | [
"TAGS\n#size_categories-100K<n<1M #language-French #license-odc-by #legal #region-us \n",
"# Court of Cassation\n\nThe major decisions of judicial jurisprudence; the decisions of the Cour de cassation :\n - published in the Bulletin des chambres civiles since 1960\n- published in the Bulletin de la chambre crimin... | [
34,
76
] | [
"passage: TAGS\n#size_categories-100K<n<1M #language-French #license-odc-by #legal #region-us \n# Court of Cassation\n\nThe major decisions of judicial jurisprudence; the decisions of the Cour de cassation :\n - published in the Bulletin des chambres civiles since 1960\n- published in the Bulletin de la chambre cri... |
be2687ef8d4dbbdbd511566ee588a261aac7b22a |
# smsa
SmSA is a sentence-level sentiment analysis dataset (Purwarianti and Crisdayanti, 2019) is a collection of comments and reviews
in Indonesian obtained from multiple online platforms. The text was crawled and then annotated by several Indonesian linguists
to construct this dataset. There are three possible se... | SEACrowd/smsa | [
"language:ind",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:31:18+00:00 | {"language": ["ind"], "tags": ["sentiment-analysis"]} | 2023-09-26T11:33:48+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #sentiment-analysis #region-us
|
# smsa
SmSA is a sentence-level sentiment analysis dataset (Purwarianti and Crisdayanti, 2019) is a collection of comments and reviews
in Indonesian obtained from multiple online platforms. The text was crawled and then annotated by several Indonesian linguists
to construct this dataset. There are three possible se... | [
"# smsa\n\nSmSA is a sentence-level sentiment analysis dataset (Purwarianti and Crisdayanti, 2019) is a collection of comments and reviews\n\nin Indonesian obtained from multiple online platforms. The text was crawled and then annotated by several Indonesian linguists\n\nto construct this dataset. There are three p... | [
"TAGS\n#language-Indonesian #sentiment-analysis #region-us \n",
"# smsa\n\nSmSA is a sentence-level sentiment analysis dataset (Purwarianti and Crisdayanti, 2019) is a collection of comments and reviews\n\nin Indonesian obtained from multiple online platforms. The text was crawled and then annotated by several In... | [
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] | [
"passage: TAGS\n#language-Indonesian #sentiment-analysis #region-us \n# smsa\n\nSmSA is a sentence-level sentiment analysis dataset (Purwarianti and Crisdayanti, 2019) is a collection of comments and reviews\n\nin Indonesian obtained from multiple online platforms. The text was crawled and then annotated by several... |
f1ef20cb03a78ecd15bd1a126ad14f14dab291a3 |
# indonlu_nergrit
This NER dataset is taken from the Grit-ID repository, and the labels are spans in IOB chunking representation.
The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and
ORGANIZATION (name of organization).
## Dataset Usage
Run `pip install ... | SEACrowd/indonlu_nergrit | [
"language:ind",
"license:mit",
"named-entity-recognition",
"region:us"
] | 2023-09-26T10:31:21+00:00 | {"language": ["ind"], "license": "mit", "tags": ["named-entity-recognition"]} | 2023-09-26T11:35:26+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-mit #named-entity-recognition #region-us
|
# indonlu_nergrit
This NER dataset is taken from the Grit-ID repository, and the labels are spans in IOB chunking representation.
The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and
ORGANIZATION (name of organization).
## Dataset Usage
Run 'pip install ... | [
"# indonlu_nergrit\n\nThis NER dataset is taken from the Grit-ID repository, and the labels are spans in IOB chunking representation.\n\nThe dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and\n\nORGANIZATION (name of organization).",
"## Dataset Usage\n\nR... | [
"TAGS\n#language-Indonesian #license-mit #named-entity-recognition #region-us \n",
"# indonlu_nergrit\n\nThis NER dataset is taken from the Grit-ID repository, and the labels are spans in IOB chunking representation.\n\nThe dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name... | [
26,
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"passage: TAGS\n#language-Indonesian #license-mit #named-entity-recognition #region-us \n# indonlu_nergrit\n\nThis NER dataset is taken from the Grit-ID repository, and the labels are spans in IOB chunking representation.\n\nThe dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (n... |
a6be0d04daf89ffc09cc3af8e08db26ad97a212e |
# id_wiki_parallel
This dataset is designed for machine translation task, specifically jav->ind, min->ind, sun->ind, and vice versa. The data are taken
from sentences in Wikipedia.
(from the publication abstract)
Parallel corpora are necessary for multilingual researches especially in information retrieval (IR) ... | SEACrowd/id_wiki_parallel | [
"language:ind",
"language:jav",
"language:min",
"language:sun",
"license:unknown",
"machine-translation",
"region:us"
] | 2023-09-26T10:31:21+00:00 | {"language": ["ind", "jav", "min", "sun"], "license": "unknown", "tags": ["machine-translation"]} | 2023-09-26T11:35:31+00:00 | [] | [
"ind",
"jav",
"min",
"sun"
] | TAGS
#language-Indonesian #language-Javanese #language-Minangkabau #language-Sundanese #license-unknown #machine-translation #region-us
|
# id_wiki_parallel
This dataset is designed for machine translation task, specifically jav->ind, min->ind, sun->ind, and vice versa. The data are taken
from sentences in Wikipedia.
(from the publication abstract)
Parallel corpora are necessary for multilingual researches especially in information retrieval (IR) ... | [
"# id_wiki_parallel\n\nThis dataset is designed for machine translation task, specifically jav->ind, min->ind, sun->ind, and vice versa. The data are taken\n\nfrom sentences in Wikipedia.\n\n\n\n(from the publication abstract)\n\nParallel corpora are necessary for multilingual researches especially in information r... | [
"TAGS\n#language-Indonesian #language-Javanese #language-Minangkabau #language-Sundanese #license-unknown #machine-translation #region-us \n",
"# id_wiki_parallel\n\nThis dataset is designed for machine translation task, specifically jav->ind, min->ind, sun->ind, and vice versa. The data are taken\n\nfrom sentenc... | [
40,
313,
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"passage: TAGS\n#language-Indonesian #language-Javanese #language-Minangkabau #language-Sundanese #license-unknown #machine-translation #region-us \n# id_wiki_parallel\n\nThis dataset is designed for machine translation task, specifically jav->ind, min->ind, sun->ind, and vice versa. The data are taken\n\nfrom sent... |
5c4326a7e59c42db9fcc7e280628b5b29afc2048 |
# Dataset of Koshigaya Natsumi
This is the dataset of Koshigaya Natsumi, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/d... | CyberHarem/koshigaya_natsumi_nonnonbiyori | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T10:39:33+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2023-09-27T18:37:44+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of Koshigaya Natsumi
============================
This is the dataset of Koshigaya Natsumi, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
| [] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] | [
44
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
e2fddc9f7148ed3b143cf0ef98abb0995546776a |
# id_qqp
Quora Question Pairs (QQP) dataset consists of over 400,000 question pairs,
and each question pair is annotated with a binary value indicating whether
the two questions are paraphrase of each other. This dataset is translated
version of QQP to Indonesian Language.
## Dataset Usage
Run `pip install nu... | SEACrowd/id_qqp | [
"language:ind",
"paraphrasing",
"region:us"
] | 2023-09-26T10:41:38+00:00 | {"language": ["ind"], "tags": ["paraphrasing"]} | 2023-09-26T11:33:52+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #paraphrasing #region-us
|
# id_qqp
Quora Question Pairs (QQP) dataset consists of over 400,000 question pairs,
and each question pair is annotated with a binary value indicating whether
the two questions are paraphrase of each other. This dataset is translated
version of QQP to Indonesian Language.
## Dataset Usage
Run 'pip install nu... | [
"# id_qqp\n\nQuora Question Pairs (QQP) dataset consists of over 400,000 question pairs, \n\nand each question pair is annotated with a binary value indicating whether \n\nthe two questions are paraphrase of each other. This dataset is translated \n\nversion of QQP to Indonesian Language.",
"## Dataset Usage\n\nR... | [
"TAGS\n#language-Indonesian #paraphrasing #region-us \n",
"# id_qqp\n\nQuora Question Pairs (QQP) dataset consists of over 400,000 question pairs, \n\nand each question pair is annotated with a binary value indicating whether \n\nthe two questions are paraphrase of each other. This dataset is translated \n\nversi... | [
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"passage: TAGS\n#language-Indonesian #paraphrasing #region-us \n# id_qqp\n\nQuora Question Pairs (QQP) dataset consists of over 400,000 question pairs, \n\nand each question pair is annotated with a binary value indicating whether \n\nthe two questions are paraphrase of each other. This dataset is translated \n\nve... |
9d1cfc186d94314090e13b944033880b0b7c5b2f |
# nerp
The NERP dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites with five labels
- PER (name of person)
- LOC (name of location)
- IND (name of product or brand)
- EVT (name of the event)
- FNB (name of food and beverage).
NERP makes use of the IOB chunking ... | SEACrowd/nerp | [
"language:ind",
"named-entity-recognition",
"region:us"
] | 2023-09-26T10:41:47+00:00 | {"language": ["ind"], "tags": ["named-entity-recognition"]} | 2023-09-26T11:34:00+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #named-entity-recognition #region-us
|
# nerp
The NERP dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites with five labels
- PER (name of person)
- LOC (name of location)
- IND (name of product or brand)
- EVT (name of the event)
- FNB (name of food and beverage).
NERP makes use of the IOB chunking ... | [
"# nerp\n\nThe NERP dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites with five labels\n\n- PER (name of person)\n\n- LOC (name of location)\n\n- IND (name of product or brand)\n\n- EVT (name of the event)\n\n- FNB (name of food and beverage).\n\nNERP makes use of... | [
"TAGS\n#language-Indonesian #named-entity-recognition #region-us \n",
"# nerp\n\nThe NERP dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites with five labels\n\n- PER (name of person)\n\n- LOC (name of location)\n\n- IND (name of product or brand)\n\n- EVT (name ... | [
21,
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"passage: TAGS\n#language-Indonesian #named-entity-recognition #region-us \n# nerp\n\nThe NERP dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites with five labels\n\n- PER (name of person)\n\n- LOC (name of location)\n\n- IND (name of product or brand)\n\n- EVT (na... |
3746b1573794c87c78d42e37d5a40469036d7dae |
# parallel_su_id
This data contains 3616 lines of Sundanese sentences taken from the online Sundanese language magazine Mangle, West Java Dakwah Council, and Balebat, and translated into Indonesian by several students of the Sundanese language study program UPI Bandung.
## Dataset Usage
Run `pip install nusacrowd` ... | SEACrowd/parallel_su_id | [
"language:ind",
"language:sun",
"machine-translation",
"region:us"
] | 2023-09-26T10:41:55+00:00 | {"language": ["ind", "sun"], "tags": ["machine-translation"]} | 2023-09-26T11:34:07+00:00 | [] | [
"ind",
"sun"
] | TAGS
#language-Indonesian #language-Sundanese #machine-translation #region-us
|
# parallel_su_id
This data contains 3616 lines of Sundanese sentences taken from the online Sundanese language magazine Mangle, West Java Dakwah Council, and Balebat, and translated into Indonesian by several students of the Sundanese language study program UPI Bandung.
## Dataset Usage
Run 'pip install nusacrowd' ... | [
"# parallel_su_id\n\nThis data contains 3616 lines of Sundanese sentences taken from the online Sundanese language magazine Mangle, West Java Dakwah Council, and Balebat, and translated into Indonesian by several students of the Sundanese language study program UPI Bandung.",
"## Dataset Usage\n\nRun 'pip install... | [
"TAGS\n#language-Indonesian #language-Sundanese #machine-translation #region-us \n",
"# parallel_su_id\n\nThis data contains 3616 lines of Sundanese sentences taken from the online Sundanese language magazine Mangle, West Java Dakwah Council, and Balebat, and translated into Indonesian by several students of the ... | [
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"passage: TAGS\n#language-Indonesian #language-Sundanese #machine-translation #region-us \n# parallel_su_id\n\nThis data contains 3616 lines of Sundanese sentences taken from the online Sundanese language magazine Mangle, West Java Dakwah Council, and Balebat, and translated into Indonesian by several students of t... |
fb2bfac663cb923d70a3cb74d759ef15ca4ca71d |
# squad_id
This dataset contains Indonesian SQuAD v2.0 dataset (Google-translated).
The dataset can be used for automatic question generation (AQG),
or machine reading comphrehension(MRC) task.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
#... | SEACrowd/squad_id | [
"language:ind",
"question-answering",
"region:us"
] | 2023-09-26T10:42:04+00:00 | {"language": ["ind"], "tags": ["question-answering"]} | 2023-09-26T11:34:15+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #question-answering #region-us
|
# squad_id
This dataset contains Indonesian SQuAD v2.0 dataset (Google-translated).
The dataset can be used for automatic question generation (AQG),
or machine reading comphrehension(MRC) task.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
#... | [
"# squad_id\n\nThis dataset contains Indonesian SQuAD v2.0 dataset (Google-translated).\n\n The dataset can be used for automatic question generation (AQG),\n\n or machine reading comphrehension(MRC) task.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'l... | [
"TAGS\n#language-Indonesian #question-answering #region-us \n",
"# squad_id\n\nThis dataset contains Indonesian SQuAD v2.0 dataset (Google-translated).\n\n The dataset can be used for automatic question generation (AQG),\n\n or machine reading comphrehension(MRC) task.",
"## Dataset Usage\n\nRun 'pip inst... | [
17,
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35,
4,
3,
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] | [
"passage: TAGS\n#language-Indonesian #question-answering #region-us \n# squad_id\n\nThis dataset contains Indonesian SQuAD v2.0 dataset (Google-translated).\n\n The dataset can be used for automatic question generation (AQG),\n\n or machine reading comphrehension(MRC) task.## Dataset Usage\n\nRun 'pip install... |
511ec34eb24101a27b34964e19c8a64ee7748d1c |
# jv_id_tts
This data set contains high-quality transcribed audio data for Javanese.
The data set consists of wave files, and a TSV file.
The file line_index.tsv contains a filename and the transcription of audio in the file.
Each filename is prepended with a speaker identification number.
The data set has been m... | SEACrowd/jv_id_tts | [
"language:jav",
"text-to-speech",
"region:us"
] | 2023-09-26T10:42:16+00:00 | {"language": ["jav"], "tags": ["text-to-speech"]} | 2023-09-26T11:34:26+00:00 | [] | [
"jav"
] | TAGS
#language-Javanese #text-to-speech #region-us
|
# jv_id_tts
This data set contains high-quality transcribed audio data for Javanese.
The data set consists of wave files, and a TSV file.
The file line_index.tsv contains a filename and the transcription of audio in the file.
Each filename is prepended with a speaker identification number.
The data set has been m... | [
"# jv_id_tts\n\nThis data set contains high-quality transcribed audio data for Javanese.\n\nThe data set consists of wave files, and a TSV file.\n\nThe file line_index.tsv contains a filename and the transcription of audio in the file.\n\nEach filename is prepended with a speaker identification number.\n\nThe data ... | [
"TAGS\n#language-Javanese #text-to-speech #region-us \n",
"# jv_id_tts\n\nThis data set contains high-quality transcribed audio data for Javanese.\n\nThe data set consists of wave files, and a TSV file.\n\nThe file line_index.tsv contains a filename and the transcription of audio in the file.\n\nEach filename is ... | [
18,
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35,
20,
3,
16
] | [
"passage: TAGS\n#language-Javanese #text-to-speech #region-us \n# jv_id_tts\n\nThis data set contains high-quality transcribed audio data for Javanese.\n\nThe data set consists of wave files, and a TSV file.\n\nThe file line_index.tsv contains a filename and the transcription of audio in the file.\n\nEach filename ... |
14ae440e20ef2b0eea6d3e9af9eaa040c7c2139e |
# xpersona_id
XPersona is a multi-lingual extension of Persona-Chat.
XPersona dataset includes persona conversations in six different languages other than English for building and evaluating multilingual personalized agents.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFa... | SEACrowd/xpersona_id | [
"language:ind",
"dialogue-system",
"region:us"
] | 2023-09-26T10:42:21+00:00 | {"language": ["ind"], "tags": ["dialogue-system"]} | 2023-09-26T11:34:30+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #dialogue-system #region-us
|
# xpersona_id
XPersona is a multi-lingual extension of Persona-Chat.
XPersona dataset includes persona conversations in six different languages other than English for building and evaluating multilingual personalized agents.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFa... | [
"# xpersona_id\n\nXPersona is a multi-lingual extension of Persona-Chat. \n\nXPersona dataset includes persona conversations in six different languages other than English for building and evaluating multilingual personalized agents.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset thr... | [
"TAGS\n#language-Indonesian #dialogue-system #region-us \n",
"# xpersona_id\n\nXPersona is a multi-lingual extension of Persona-Chat. \n\nXPersona dataset includes persona conversations in six different languages other than English for building and evaluating multilingual personalized agents.",
"## Dataset Usag... | [
17,
50,
35,
8,
16
] | [
"passage: TAGS\n#language-Indonesian #dialogue-system #region-us \n# xpersona_id\n\nXPersona is a multi-lingual extension of Persona-Chat. \n\nXPersona dataset includes persona conversations in six different languages other than English for building and evaluating multilingual personalized agents.## Dataset Usage\n... |
7ed83c46821f31eb7b49395d798a8d0822584eb0 |
# ud_id_csui
UD Indonesian-CSUI is a conversion from an Indonesian constituency treebank in the Penn Treebank format named Kethu that was also a conversion from a constituency treebank built by Dinakaramani et al. (2015).
This treebank is named after the place where treebanks were built: Faculty of Computer Science ... | SEACrowd/ud_id_csui | [
"language:ind",
"dependency-parsing",
"machine-translation",
"pos-tagging",
"region:us"
] | 2023-09-26T10:42:25+00:00 | {"language": ["ind"], "tags": ["dependency-parsing", "machine-translation", "pos-tagging"]} | 2023-09-26T11:34:34+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #dependency-parsing #machine-translation #pos-tagging #region-us
|
# ud_id_csui
UD Indonesian-CSUI is a conversion from an Indonesian constituency treebank in the Penn Treebank format named Kethu that was also a conversion from a constituency treebank built by Dinakaramani et al. (2015).
This treebank is named after the place where treebanks were built: Faculty of Computer Science ... | [
"# ud_id_csui\n\nUD Indonesian-CSUI is a conversion from an Indonesian constituency treebank in the Penn Treebank format named Kethu that was also a conversion from a constituency treebank built by Dinakaramani et al. (2015).\n\nThis treebank is named after the place where treebanks were built: Faculty of Computer ... | [
"TAGS\n#language-Indonesian #dependency-parsing #machine-translation #pos-tagging #region-us \n",
"# ud_id_csui\n\nUD Indonesian-CSUI is a conversion from an Indonesian constituency treebank in the Penn Treebank format named Kethu that was also a conversion from a constituency treebank built by Dinakaramani et al... | [
28,
150,
35,
7,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #dependency-parsing #machine-translation #pos-tagging #region-us \n# ud_id_csui\n\nUD Indonesian-CSUI is a conversion from an Indonesian constituency treebank in the Penn Treebank format named Kethu that was also a conversion from a constituency treebank built by Dinakaramani et... |
fc7477b2a3c270478ac7367de2846cfaa784d5db |
# IndQNER
IndQNER is a NER dataset created by manually annotating the Indonesian translation of Quran text.
The dataset contains 18 named entity categories as follow:
"Allah": Allah (including synonim of Allah such as Yang maha mengetahui lagi mahabijaksana)
"Throne": Throne of Allah (such as 'Arasy)
... | SEACrowd/indqner | [
"language:ind",
"license:unknown",
"named-entity-recognition",
"region:us"
] | 2023-09-26T10:42:30+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["named-entity-recognition"]} | 2023-09-26T11:34:43+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #named-entity-recognition #region-us
|
# IndQNER
IndQNER is a NER dataset created by manually annotating the Indonesian translation of Quran text.
The dataset contains 18 named entity categories as follow:
"Allah": Allah (including synonim of Allah such as Yang maha mengetahui lagi mahabijaksana)
"Throne": Throne of Allah (such as 'Arasy)
... | [
"# IndQNER\n\nIndQNER is a NER dataset created by manually annotating the Indonesian translation of Quran text.\n\nThe dataset contains 18 named entity categories as follow:\n\n \"Allah\": Allah (including synonim of Allah such as Yang maha mengetahui lagi mahabijaksana)\n\n \"Throne\": Throne of Allah (such ... | [
"TAGS\n#language-Indonesian #license-unknown #named-entity-recognition #region-us \n",
"# IndQNER\n\nIndQNER is a NER dataset created by manually annotating the Indonesian translation of Quran text.\n\nThe dataset contains 18 named entity categories as follow:\n\n \"Allah\": Allah (including synonim of Allah s... | [
28,
328,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #named-entity-recognition #region-us \n# IndQNER\n\nIndQNER is a NER dataset created by manually annotating the Indonesian translation of Quran text.\n\nThe dataset contains 18 named entity categories as follow:\n\n \"Allah\": Allah (including synonim of Alla... |
b871f79852b592e788f5aee353c9290a0c1a4588 |
# indotacos
Predicting the outcome or the probability of winning a legal case has always been highly attractive in legal sciences and practice.
Hardly any dataset has been developed to analyze and accelerate the research of court verdict analysis.
Find out what factor affects the outcome of tax court verdict using ... | SEACrowd/indotacos | [
"language:ind",
"tax-court-verdict",
"region:us"
] | 2023-09-26T10:42:33+00:00 | {"language": ["ind"], "tags": ["tax-court-verdict"]} | 2023-09-26T11:34:47+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #tax-court-verdict #region-us
|
# indotacos
Predicting the outcome or the probability of winning a legal case has always been highly attractive in legal sciences and practice.
Hardly any dataset has been developed to analyze and accelerate the research of court verdict analysis.
Find out what factor affects the outcome of tax court verdict using ... | [
"# indotacos\n\nPredicting the outcome or the probability of winning a legal case has always been highly attractive in legal sciences and practice.\n\nHardly any dataset has been developed to analyze and accelerate the research of court verdict analysis.\n\nFind out what factor affects the outcome of tax court verd... | [
"TAGS\n#language-Indonesian #tax-court-verdict #region-us \n",
"# indotacos\n\nPredicting the outcome or the probability of winning a legal case has always been highly attractive in legal sciences and practice.\n\nHardly any dataset has been developed to analyze and accelerate the research of court verdict analys... | [
18,
73,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #tax-court-verdict #region-us \n# indotacos\n\nPredicting the outcome or the probability of winning a legal case has always been highly attractive in legal sciences and practice.\n\nHardly any dataset has been developed to analyze and accelerate the research of court verdict ana... |
438621c658ab85456f5770224618d01f35e5f564 |
# indo_law
This study presents predictions of first-level judicial decisions by utilizing a collection of Indonesian court decision documents.
We propose using multi-level learning, namely, CNN+attention, using decision document sections as features to predict the category and the length of punishment in Indonesian... | SEACrowd/indo_law | [
"language:ind",
"license:unknown",
"legal-classification",
"region:us"
] | 2023-09-26T10:42:37+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["legal-classification"]} | 2023-09-26T11:34:50+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #legal-classification #region-us
|
# indo_law
This study presents predictions of first-level judicial decisions by utilizing a collection of Indonesian court decision documents.
We propose using multi-level learning, namely, CNN+attention, using decision document sections as features to predict the category and the length of punishment in Indonesian... | [
"# indo_law\n\nThis study presents predictions of first-level judicial decisions by utilizing a collection of Indonesian court decision documents. \n\nWe propose using multi-level learning, namely, CNN+attention, using decision document sections as features to predict the category and the length of punishment in In... | [
"TAGS\n#language-Indonesian #license-unknown #legal-classification #region-us \n",
"# indo_law\n\nThis study presents predictions of first-level judicial decisions by utilizing a collection of Indonesian court decision documents. \n\nWe propose using multi-level learning, namely, CNN+attention, using decision doc... | [
23,
109,
35,
5,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #legal-classification #region-us \n# indo_law\n\nThis study presents predictions of first-level judicial decisions by utilizing a collection of Indonesian court decision documents. \n\nWe propose using multi-level learning, namely, CNN+attention, using decision ... |
dbe0267d6f46d59ec717198e49bf0743f88c2864 |
# keps
The KEPS dataset (Mahfuzh, Soleman and Purwarianti, 2019) consists of text from Twitter
discussing banking products and services and is written in the Indonesian language. A phrase
containing important information is considered a keyphrase. Text may contain one or more
keyphrases since important phrases can... | SEACrowd/keps | [
"language:ind",
"keyword-extraction",
"region:us"
] | 2023-09-26T10:42:43+00:00 | {"language": ["ind"], "tags": ["keyword-extraction"]} | 2023-09-26T11:34:57+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #keyword-extraction #region-us
|
# keps
The KEPS dataset (Mahfuzh, Soleman and Purwarianti, 2019) consists of text from Twitter
discussing banking products and services and is written in the Indonesian language. A phrase
containing important information is considered a keyphrase. Text may contain one or more
keyphrases since important phrases can... | [
"# keps\n\nThe KEPS dataset (Mahfuzh, Soleman and Purwarianti, 2019) consists of text from Twitter\n\ndiscussing banking products and services and is written in the Indonesian language. A phrase\n\ncontaining important information is considered a keyphrase. Text may contain one or more\n\nkeyphrases since important... | [
"TAGS\n#language-Indonesian #keyword-extraction #region-us \n",
"# keps\n\nThe KEPS dataset (Mahfuzh, Soleman and Purwarianti, 2019) consists of text from Twitter\n\ndiscussing banking products and services and is written in the Indonesian language. A phrase\n\ncontaining important information is considered a key... | [
17,
132,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #keyword-extraction #region-us \n# keps\n\nThe KEPS dataset (Mahfuzh, Soleman and Purwarianti, 2019) consists of text from Twitter\n\ndiscussing banking products and services and is written in the Indonesian language. A phrase\n\ncontaining important information is considered a ... |
1e12d514a34f0c580a3bb8bc43294d185fb57778 |
# librivox_indonesia
The LibriVox Indonesia dataset consists of MP3 audio and a corresponding text file we generated from the public domain audiobooks LibriVox.
We collected only languages in Indonesia for this dataset.
The original LibriVox audiobooks or sound files' duration varies from a few minutes to a few h... | SEACrowd/librivox_indonesia | [
"language:min",
"language:bug",
"language:ind",
"language:ban",
"language:ace",
"language:sun",
"language:jav",
"speech-recognition",
"region:us"
] | 2023-09-26T10:42:47+00:00 | {"language": ["min", "bug", "ind", "ban", "ace", "sun", "jav"], "tags": ["speech-recognition"]} | 2023-09-26T11:35:01+00:00 | [] | [
"min",
"bug",
"ind",
"ban",
"ace",
"sun",
"jav"
] | TAGS
#language-Minangkabau #language-Buginese #language-Indonesian #language-Balinese #language-Achinese #language-Sundanese #language-Javanese #speech-recognition #region-us
|
# librivox_indonesia
The LibriVox Indonesia dataset consists of MP3 audio and a corresponding text file we generated from the public domain audiobooks LibriVox.
We collected only languages in Indonesia for this dataset.
The original LibriVox audiobooks or sound files' duration varies from a few minutes to a few h... | [
"# librivox_indonesia\n\nThe LibriVox Indonesia dataset consists of MP3 audio and a corresponding text file we generated from the public domain audiobooks LibriVox. \n\nWe collected only languages in Indonesia for this dataset. \n\nThe original LibriVox audiobooks or sound files' duration varies from a few minutes ... | [
"TAGS\n#language-Minangkabau #language-Buginese #language-Indonesian #language-Balinese #language-Achinese #language-Sundanese #language-Javanese #speech-recognition #region-us \n",
"# librivox_indonesia\n\nThe LibriVox Indonesia dataset consists of MP3 audio and a corresponding text file we generated from the pu... | [
53,
195,
35,
4,
3,
16
] | [
"passage: TAGS\n#language-Minangkabau #language-Buginese #language-Indonesian #language-Balinese #language-Achinese #language-Sundanese #language-Javanese #speech-recognition #region-us \n# librivox_indonesia\n\nThe LibriVox Indonesia dataset consists of MP3 audio and a corresponding text file we generated from the... |
8c6bf40625b51acaed57e7d76a4d219a1859e6af |
# sentiment_nathasa_review
Customer Review (Natasha Skincare) is a customers emotion dataset, with amounted to 19,253 samples with the division for each class is 804 joy, 43 surprise, 154 anger, 61 fear, 287 sad, 167 disgust, and 17736 no-emotions.
## Dataset Usage
Run `pip install nusacrowd` before loading the dat... | SEACrowd/sentiment_nathasa_review | [
"language:ind",
"license:unknown",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:42:52+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["sentiment-analysis"]} | 2023-09-26T11:35:04+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #sentiment-analysis #region-us
|
# sentiment_nathasa_review
Customer Review (Natasha Skincare) is a customers emotion dataset, with amounted to 19,253 samples with the division for each class is 804 joy, 43 surprise, 154 anger, 61 fear, 287 sad, 167 disgust, and 17736 no-emotions.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dat... | [
"# sentiment_nathasa_review\n\nCustomer Review (Natasha Skincare) is a customers emotion dataset, with amounted to 19,253 samples with the division for each class is 804 joy, 43 surprise, 154 anger, 61 fear, 287 sad, 167 disgust, and 17736 no-emotions.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loa... | [
"TAGS\n#language-Indonesian #license-unknown #sentiment-analysis #region-us \n",
"# sentiment_nathasa_review\n\nCustomer Review (Natasha Skincare) is a customers emotion dataset, with amounted to 19,253 samples with the division for each class is 804 joy, 43 surprise, 154 anger, 61 fear, 287 sad, 167 disgust, and... | [
24,
69,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #sentiment-analysis #region-us \n# sentiment_nathasa_review\n\nCustomer Review (Natasha Skincare) is a customers emotion dataset, with amounted to 19,253 samples with the division for each class is 804 joy, 43 surprise, 154 anger, 61 fear, 287 sad, 167 disgust, ... |
33429673cf63a7bced1f3f9f5de4a677d3f7246f |
# Dataset of uehara_himari/上原ひまり (BanG Dream!)
This is the dataset of uehara_himari/上原ひまり (BanG Dream!), containing 325 images and their tags.
The core tags of this character are `bangs, pink_hair, green_eyes, twintails, low_twintails, breasts, medium_hair, long_hair, large_breasts`, which are pruned in this dataset... | CyberHarem/uehara_himari_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T10:47:35+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T18:55:49+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of uehara\_himari/上原ひまり (BanG Dream!)
=============================================
This is the dataset of uehara\_himari/上原ひまり (BanG Dream!), containing 325 images and their tags.
The core tags of this character are 'bangs, pink\_hair, green\_eyes, twintails, low\_twintails, breasts, medium\_hair, long\_ha... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version... | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n... | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\... |
5bdde13a6fbdf68e2fdc92dfd4846c7331bbe0dc |
# Dataset of Koshigaya Komari
This is the dataset of Koshigaya Komari, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/dee... | CyberHarem/koshigaya_komari_nonnonbiyori | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T11:18:31+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2023-09-27T19:18:56+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of Koshigaya Komari
===========================
This is the dataset of Koshigaya Komari, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
| [] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] | [
44
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
f31519f6fa0cba6105ec63b4ca2d8bda817d67ee | # Dataset Card for "zx"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sc3069/zx | [
"region:us"
] | 2023-09-26T11:27:13+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 10329536, "num_examples": 350}], "download_size": 1991265, "dataset_size": 10329536}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "dat... | 2023-09-27T08:47:20+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "zx"
More Information needed | [
"# Dataset Card for \"zx\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"zx\"\n\nMore Information needed"
] | [
6,
12
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"zx\"\n\nMore Information needed"
] |
8ff3a1d869f923c1877ece2bde4734c9aefe683b |
# Dataset of udagawa_ako/宇田川あこ (BanG Dream!)
This is the dataset of udagawa_ako/宇田川あこ (BanG Dream!), containing 237 images and their tags.
The core tags of this character are `purple_hair, bangs, red_eyes, twintails, sidelocks, long_hair, fang, v-shaped_eyebrows, bow`, which are pruned in this dataset.
Images are c... | CyberHarem/udagawa_ako_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T11:33:51+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T17:42:07+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of udagawa\_ako/宇田川あこ (BanG Dream!)
===========================================
This is the dataset of udagawa\_ako/宇田川あこ (BanG Dream!), containing 237 images and their tags.
The core tags of this character are 'purple\_hair, bangs, red\_eyes, twintails, sidelocks, long\_hair, fang, v-shaped\_eyebrows, bow'... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version... | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n... | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\... |
d2466d5e309036740e4b084947621c1717a68f0f | This dataset is a subset of the [JosephusCheung/GuanacoDataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset/viewer/default/train?p=11736) dataset, where only german samples were selected as well as formated with the following template for the chat models:
```<s>[INST] User prompt [/INST] Model answer... | tessiw/German_GuanacoDataset | [
"task_categories:conversational",
"language:de",
"region:us"
] | 2023-09-26T11:34:29+00:00 | {"language": ["de"], "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 77973314, "num_examples": 139476}], "download_size": 40038214, "dataset_size": 77973314}, "configs": [{"config_name": "default", "data_files": [{"spli... | 2023-09-26T11:52:18+00:00 | [] | [
"de"
] | TAGS
#task_categories-conversational #language-German #region-us
| This dataset is a subset of the JosephusCheung/GuanacoDataset dataset, where only german samples were selected as well as formated with the following template for the chat models:
| [] | [
"TAGS\n#task_categories-conversational #language-German #region-us \n"
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d88d5c4c71fd5ed749bd9f2746384b4f109295e0 | # CONSTIT
It includes the references of all the decisions of the [Conseil constitutionnel](https://www.data.gouv.fr/fr/datasets/constit-les-decisions-du-conseil-constitutionnel/) since its creation in 1958 and these same decisions in full text according to the following table:
Contentious standards
Constitutional de... | Nicolas-BZRD/CONSTIT_opendata | [
"size_categories:1K<n<10K",
"language:fr",
"license:odc-by",
"legal",
"region:us"
] | 2023-09-26T11:38:21+00:00 | {"language": ["fr"], "license": "odc-by", "size_categories": ["1K<n<10K"], "pretty_name": "Conseil constitutionnel", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}],... | 2023-09-28T08:49:22+00:00 | [] | [
"fr"
] | TAGS
#size_categories-1K<n<10K #language-French #license-odc-by #legal #region-us
| # CONSTIT
It includes the references of all the decisions of the Conseil constitutionnel since its creation in 1958 and these same decisions in full text according to the following table:
Contentious standards
Constitutional decisions ( DC) since the beginning (1958), Question prioritaire de constitutionnalité ( QPC... | [
"# CONSTIT\n\nIt includes the references of all the decisions of the Conseil constitutionnel since its creation in 1958 and these same decisions in full text according to the following table:\n\nContentious standards \nConstitutional decisions ( DC) since the beginning (1958), Question prioritaire de constitutionna... | [
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a39f02cfea870793b0e99c44036cef44543a169a |
# Bangumi Image Base of Encouragement Of Climb
This is the image base of bangumi Encouragement of Climb, we detected 20 characters, 3066 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manua... | BangumiBase/encouragementofclimb | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-09-26T11:47:09+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-09-29T11:18:25+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Encouragement Of Climb
============================================
This is the image base of bangumi Encouragement of Climb, we detected 20 characters, 3066 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy a... | [] | [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] | [
25
] | [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
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51169c9366fb50411cbaf240fa2b6a4a57521606 |
# Dataset of wakamiya_eve (BanG Dream!)
This is the dataset of wakamiya_eve (BanG Dream!), containing 192 images and their tags.
The core tags of this character are `blue_eyes, bangs, white_hair, long_hair, braid, twin_braids, ribbon, bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. ... | CyberHarem/wakamiya_eve_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T11:54:24+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T19:19:23+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of wakamiya\_eve (BanG Dream!)
======================================
This is the dataset of wakamiya\_eve (BanG Dream!), containing 192 images and their tags.
The core tags of this character are 'blue\_eyes, bangs, white\_hair, long\_hair, braid, twin\_braids, ribbon, bow', which are pruned in this dataset... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
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97ff4f23ebd91da6aa49917cc32fcae9acb0d7c5 |
# Dataset of Kagayama Kaede
This is the dataset of Kagayama Kaede, containing 176 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs... | CyberHarem/kagayama_kaede_nonnonbiyori | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T11:56:57+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2023-09-27T19:45:39+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of Kagayama Kaede
=========================
This is the dataset of Kagayama Kaede, containing 176 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
| [] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] | [
44
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416f594a253e2f9616c6550673a13f278e4e6e67 |
# Coreference Resolution in Question Answering (CRaQAn)
250+ question-answer pairs that require coreference resolution across sentences from selected Wikipedia passages.
## Generation Process
Given the relative complexity of our task (coreference resolution across passages for question-answering), we aimed
to avoi... | Edge-Pyxos/CRaQAn_v1 | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"legal",
"region:us"
] | 2023-09-26T12:11:53+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "task_categories": ["question-answering"], "pretty_name": "craqan_v1", "tags": ["legal"], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "article", "dtype": "string"}, {"name": "article_titles", "sequence": "string"}... | 2023-09-26T15:11:40+00:00 | [] | [
"en"
] | TAGS
#task_categories-question-answering #size_categories-n<1K #language-English #license-cc-by-4.0 #legal #region-us
|
# Coreference Resolution in Question Answering (CRaQAn)
250+ question-answer pairs that require coreference resolution across sentences from selected Wikipedia passages.
## Generation Process
Given the relative complexity of our task (coreference resolution across passages for question-answering), we aimed
to avoi... | [
"# Coreference Resolution in Question Answering (CRaQAn)\n\n250+ question-answer pairs that require coreference resolution across sentences from selected Wikipedia passages.",
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3c3271c66525dbb8e32e62d6ba098bf122517ae2 | # Dataset Card for "Market_Mail_Synthetic_DataSet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | amitraheja82/Market_Mail_Synthetic_DataSet | [
"region:us"
] | 2023-09-26T12:19:33+00:00 | {"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21260, "num_examples": 10}], "download_size": 25244, "dataset_size": 21260}, "configs": [{"config_name": "default"... | 2023-09-26T12:19:35+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "Market_Mail_Synthetic_DataSet"
More Information needed | [
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88e81a61c231f5c510dab51cb23f25e5171b1569 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset ... | anilbhatt1/emlo2s5-sample-flagging-HF-dataset | [
"region:us"
] | 2023-09-26T12:22:22+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-09-26T12:28:36+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Description
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### Dataset Summary
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### Data Instances
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fbdcde385d303bd9c79a45e359a36c87bd5acf14 |
# Dataset Card for CZI DRSM
## Dataset Description
- **Homepage:** https://github.com/chanzuckerberg/DRSM-corpus
- **Pubmed:** False
- **Public:** True
- **Tasks:** TXTCLASS
Research Article document classification dataset based on aspects of disease research. Currently, the dataset consists of three subsets:
(A)... | bigbio/czi_drsm | [
"multilinguality:monolingual",
"language:en",
"license:cc0-1.0",
"region:us"
] | 2023-09-26T12:22:47+00:00 | {"language": ["en"], "license": "cc0-1.0", "multilinguality": "monolingual", "pretty_name": "CZI DRSM", "bigbio_language": ["English"], "bigbio_license_shortname": "CC0_1p0", "homepage": "https://github.com/chanzuckerberg/DRSM-corpus", "bigbio_pubmed": false, "bigbio_public": true, "bigbio_tasks": ["TXTCLASS"]} | 2023-12-06T17:11:15+00:00 | [] | [
"en"
] | TAGS
#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us
|
# Dataset Card for CZI DRSM
## Dataset Description
- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: TXTCLASS
Research Article document classification dataset based on aspects of disease research. Currently, the dataset consists of three subsets:
(A) classifies title/abstracts of papers into most popular su... | [
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70e211010d9084c188408bac231486f37574c5a7 |
# Dataset of Miyauchi Kazuho
This is the dataset of Miyauchi Kazuho, containing 172 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepg... | CyberHarem/miyauchi_kazuho_nonnonbiyori | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T12:25:06+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2023-09-27T20:10:15+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of Miyauchi Kazuho
==========================
This is the dataset of Miyauchi Kazuho, containing 172 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
| [] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
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44
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a37a6bd4aeba21934eebab8d9e87ae8e94632572 | # Dataset Card for "nsql-eng"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ThingsSolver/nsql-eng | [
"region:us"
] | 2023-09-26T12:28:45+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "is_english", "dtype": "bool"}, {"name": "text", "dtype": "string"}], "splits... | 2023-09-28T06:39:58+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "nsql-eng"
More Information needed | [
"# Dataset Card for \"nsql-eng\"\n\nMore Information needed"
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d742e3025346e7cfc517657c4add8b7fc6c05d28 | # Dataset Card for "alpaca-data-gpt4-chinese-zhtw"
This dataset contains Chinese (zh-tw) Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.
The dataset was originaly shared in this repository: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM. This dataset is a translation fr... | erhwenkuo/alpaca-data-gpt4-chinese-zhtw | [
"task_categories:text-generation",
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"language:zh",
"gpt4",
"alpaca",
"instruction-finetuning",
"arxiv:2304.03277",
"region:us"
] | 2023-09-26T12:42:02+00:00 | {"language": ["zh"], "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "conversational", "question-answering"], "pretty_name": " alpaca-data-gpt4-chinese-zhtw", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dty... | 2023-09-26T13:03:00+00:00 | [
"2304.03277"
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#task_categories-text-generation #task_categories-conversational #task_categories-question-answering #size_categories-10K<n<100K #language-Chinese #gpt4 #alpaca #instruction-finetuning #arxiv-2304.03277 #region-us
| # Dataset Card for "alpaca-data-gpt4-chinese-zhtw"
This dataset contains Chinese (zh-tw) Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.
The dataset was originaly shared in this repository: URL This dataset is a translation from English to Chinese.
## Dataset Description
- Homepa... | [
"# Dataset Card for \"alpaca-data-gpt4-chinese-zhtw\"\n\nThis dataset contains Chinese (zh-tw) Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.\n\nThe dataset was originaly shared in this repository: URL This dataset is a translation from English to Chinese.",
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d986520216b5c25de1edbe60fdf5362be27bd1f1 |
# Dataset of seta_kaoru/瀬田薫/세타카오루 (BanG Dream!)
This is the dataset of seta_kaoru/瀬田薫/세타카오루 (BanG Dream!), containing 239 images and their tags.
The core tags of this character are `purple_hair, red_eyes, bangs, long_hair, ponytail, hair_between_eyes, sidelocks`, which are pruned in this dataset.
Images are crawled... | CyberHarem/seta_kaoru_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T12:57:26+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T18:27:55+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of seta\_kaoru/瀬田薫/세타카오루 (BanG Dream!)
==============================================
This is the dataset of seta\_kaoru/瀬田薫/세타카오루 (BanG Dream!), containing 239 images and their tags.
The core tags of this character are 'purple\_hair, red\_eyes, bangs, long\_hair, ponytail, hair\_between\_eyes, sidelocks', ... | [
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f34f8736e4704888a11eaba0e9337879a0a95a76 | # Dataset Card for "pollution-krakow-no2-co"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vitaliy-sharandin/pollution-krakow-no2-co | [
"region:us"
] | 2023-09-26T12:59:04+00:00 | {"dataset_info": {"features": [{"name": "NO2", "dtype": "float64"}, {"name": "CO", "dtype": "float64"}, {"name": "dt", "dtype": "timestamp[ns]"}], "splits": [{"name": "train", "num_bytes": 6816, "num_examples": 284}], "download_size": 9084, "dataset_size": 6816}, "configs": [{"config_name": "default", "data_files": [{"... | 2023-09-26T13:05:28+00:00 | [] | [] | TAGS
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| # Dataset Card for "pollution-krakow-no2-co"
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178fb0d8975d7ccf722acaf4d2b24f658b0e6422 | # Dataset Card for "ppo-seals-Ant-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | HumanCompatibleAI/ppo-seals-Ant-v1 | [
"region:us"
] | 2023-09-26T13:12:32+00:00 | {"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 141011280, "num_... | 2023-09-27T05:56:10+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "ppo-seals-Ant-v1"
More Information needed | [
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e3f352fcc27a445be7f70825f2fee20aa3b5b031 |
# Dataset of kurata_mashiro/倉田ましろ (BanG Dream!)
This is the dataset of kurata_mashiro/倉田ましろ (BanG Dream!), containing 230 images and their tags.
The core tags of this character are `bangs, blue_eyes, hair_between_eyes, short_hair, breasts, white_hair`, which are pruned in this dataset.
Images are crawled from many ... | CyberHarem/kurata_mashiro_bangdream | [
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"size_categories:n<1K",
"license:mit",
"art",
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] | 2023-09-26T13:21:47+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T17:46:15+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of kurata\_mashiro/倉田ましろ (BanG Dream!)
==============================================
This is the dataset of kurata\_mashiro/倉田ましろ (BanG Dream!), containing 230 images and their tags.
The core tags of this character are 'bangs, blue\_eyes, hair\_between\_eyes, short\_hair, breasts, white\_hair', which are p... | [
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6b16e6d3cfc806252c233909ac8840ba44566379 | # Dataset Card for "ppo-seals-HalfCheetah-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | HumanCompatibleAI/ppo-seals-HalfCheetah-v1 | [
"region:us"
] | 2023-09-26T13:41:04+00:00 | {"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 92213656, "num_e... | 2023-09-27T05:57:57+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "ppo-seals-HalfCheetah-v1"
More Information needed | [
"# Dataset Card for \"ppo-seals-HalfCheetah-v1\"\n\nMore Information needed"
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4a2938b10384f6a517c8a85fc36a0b2eeee942c0 |
# Dataset Card for Dataset Name
## Dataset Description
### Dataset Summary
This dataset was created by scrapping the screenplays from the imsdb website and then splitting them into 100 segments.
Each segment has been fed into a emotion classification model and classified into the emotion it evokes and represented ... | hakkam10/screenplay_emotions | [
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] | 2023-09-26T13:41:07+00:00 | {} | 2023-09-26T15:00:45+00:00 | [] | [] | TAGS
#region-us
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# Dataset Card for Dataset Name
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780a2b0e1140a9762947f8b2566d7cd064c2e024 | # Dataset Card for "ppo-seals-Hopper-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | HumanCompatibleAI/ppo-seals-Hopper-v1 | [
"region:us"
] | 2023-09-26T13:42:54+00:00 | {"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 57153894, "num_e... | 2023-09-27T06:06:10+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "ppo-seals-Hopper-v1"
More Information needed | [
"# Dataset Card for \"ppo-seals-Hopper-v1\"\n\nMore Information needed"
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6288c4971f1c03943269147986267d6c3737382e | # Dataset Card for "ppo-seals-Swimmer-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | HumanCompatibleAI/ppo-seals-Swimmer-v1 | [
"region:us"
] | 2023-09-26T13:44:14+00:00 | {"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 131302158, "num_... | 2023-09-27T06:01:55+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "ppo-seals-Swimmer-v1"
More Information needed | [
"# Dataset Card for \"ppo-seals-Swimmer-v1\"\n\nMore Information needed"
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accb242e3831dbc0314a1340c38c3c3ac6cff1d7 | # Dataset Card for "ppo-seals-Walker2d-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | HumanCompatibleAI/ppo-seals-Walker2d-v1 | [
"region:us"
] | 2023-09-26T13:45:14+00:00 | {"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 63405655, "num_e... | 2023-09-27T06:09:25+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "ppo-seals-Walker2d-v1"
More Information needed | [
"# Dataset Card for \"ppo-seals-Walker2d-v1\"\n\nMore Information needed"
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735d163efa01376c70d796c6627638110b0317c0 | # Dataset Card for "squad_baseline_v4_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_baseline_v4_train_10_eval_10 | [
"region:us"
] | 2023-09-26T13:58:45+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "inputs", ... | 2023-09-26T13:58:51+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_baseline_v4_train_10_eval_10"
More Information needed | [
"# Dataset Card for \"squad_baseline_v4_train_10_eval_10\"\n\nMore Information needed"
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4980e6c7ec2d47beccaaf90390c3882018b53d63 | # Dataset Card for "squad_context_v4_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_context_v4_train_10_eval_10 | [
"region:us"
] | 2023-09-26T13:58:51+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "inputs", ... | 2023-09-26T13:58:57+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_context_v4_train_10_eval_10"
More Information needed | [
"# Dataset Card for \"squad_context_v4_train_10_eval_10\"\n\nMore Information needed"
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fe58557f2b3e8f53ec4acaf6eb99a9385ea68762 | # Dataset Card for "squad_title_v4_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_title_v4_train_10_eval_10 | [
"region:us"
] | 2023-09-26T13:58:57+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T13:59:03+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_title_v4_train_10_eval_10"
More Information needed | [
"# Dataset Card for \"squad_title_v4_train_10_eval_10\"\n\nMore Information needed"
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b81d9d801a26743ebf6675f840f59326aa9fb948 | # Dataset Card for "squad_wrong_title_v4_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_wrong_title_v4_train_10_eval_10 | [
"region:us"
] | 2023-09-26T13:59:04+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T13:59:09+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_wrong_title_v4_train_10_eval_10"
More Information needed | [
"# Dataset Card for \"squad_wrong_title_v4_train_10_eval_10\"\n\nMore Information needed"
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0993d80019e2550c517d28af252f870d43312d63 | # Dataset Card for "squad_no_title_v4_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_no_title_v4_train_10_eval_10 | [
"region:us"
] | 2023-09-26T13:59:10+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T13:59:16+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_no_title_v4_train_10_eval_10"
More Information needed | [
"# Dataset Card for \"squad_no_title_v4_train_10_eval_10\"\n\nMore Information needed"
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d65a7be5c468aee419627cfa754a75fa81535153 | # Dataset Card for "squad_no_title_strict_v4_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_no_title_strict_v4_train_10_eval_10 | [
"region:us"
] | 2023-09-26T13:59:16+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T13:59:22+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_no_title_strict_v4_train_10_eval_10"
More Information needed | [
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51b243d0575ac4b4349c6f0feb5daecf2ba46b3c | # Dataset Card for "viettel_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nguyenthanhdo/viettel_v3 | [
"region:us"
] | 2023-09-26T14:02:42+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "translated", "dtype": "bool"}, {"name": "output_len", "dtype": "int64"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 172... | 2023-09-26T14:02:53+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "viettel_v3"
More Information needed | [
"# Dataset Card for \"viettel_v3\"\n\nMore Information needed"
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1371351e51fcd274be466ebabf8ef0b6b95ffeb2 | # Dataset Card for "db197d09"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | result-kand2-sdxl-wuerst-karlo/db197d09 | [
"region:us"
] | 2023-09-26T14:16:30+00:00 | {"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 170, "num_examples": 10}], "download_size": 1327, "dataset_size": 170}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-09-26T14:16:30+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "db197d09"
More Information needed | [
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"passage: TAGS\n#region-us \n# Dataset Card for \"db197d09\"\n\nMore Information needed"
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3a590d29033c305fa1593b427127e1872a2173d1 | # Dataset Card for "3677a860"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | result-muse256-muse512-wuerst-sdv15/3677a860 | [
"region:us"
] | 2023-09-26T14:26:21+00:00 | {"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 240, "num_examples": 10}], "download_size": 1441, "dataset_size": 240}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-09-26T14:26:21+00:00 | [] | [] | TAGS
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b7cabcb38a34d3590eb6e06f4a67c1e78ea6f77c | # Dataset Card for "3b801040"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | result-muse256-muse512-wuerst-sdv15/3b801040 | [
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#region-us
| # Dataset Card for "3b801040"
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066a4a3e06d4eb7805b210273c077fec0410b793 | ## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [... | erhwenkuo/openorca-chinese-zhtw | [
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"task_categories:summarization",
"task_categories:feature-extra... | 2023-09-26T14:36:15+00:00 | {"language": ["zh"], "license": "mit", "size_categories": ["10M<n<100M"], "task_categories": ["conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation"], ... | 2023-09-26T21:30:01+00:00 | [
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- Dataset Summary
- Dataset Attribution
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Dataset Use
- Use Cases
- Usage Caveats
- Getting Started
configs:
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"## Table of Contents\n- Dataset Summary\n- Dataset Attribution\n- Supported Tasks and Leaderboards\n- Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n- Dataset Use\n - Use Cases\n - Usage Caveats\n - Getting Start... | [
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ad8c90107da43fdf7b90b3179fdc67916abb3d1e |
# Dataset of yamato_maya/大和麻弥/야마토마야 (BanG Dream!)
This is the dataset of yamato_maya/大和麻弥/야마토마야 (BanG Dream!), containing 166 images and their tags.
The core tags of this character are `brown_hair, green_eyes, bangs, short_hair, breasts, glasses, bow`, which are pruned in this dataset.
Images are crawled from many ... | CyberHarem/yamato_maya_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
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"art",
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#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of yamato\_maya/大和麻弥/야마토마야 (BanG Dream!)
================================================
This is the dataset of yamato\_maya/大和麻弥/야마토마야 (BanG Dream!), containing 166 images and their tags.
The core tags of this character are 'brown\_hair, green\_eyes, bangs, short\_hair, breasts, glasses, bow', which are p... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
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7acf213dc405f3afb8ede239bcc21707e4c41056 | # Dataset Card for "VietMed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | anonymousQA/VietMedQA | [
"doi:10.57967/hf/1247",
"region:us"
] | 2023-09-26T14:38:43+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "dataset_name", "dtype": "string"}, {"name": "source", "dtyp... | 2023-10-20T11:39:08+00:00 | [] | [] | TAGS
#doi-10.57967/hf/1247 #region-us
| # Dataset Card for "VietMed"
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3e0bfd326e852fac025fd82a66d0998f6a3a821e | # Dataset Card for "dica_v3_283k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DigirentEnterprise/dica_v3_283k | [
"region:us"
] | 2023-09-26T14:41:37+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "dataset_name", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 292501368, "num_examples": 284102}], "download_size": 145095136, "dataset_size": 2925... | 2023-09-26T14:42:12+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "dica_v3_283k"
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4882d5ec74d98d4b8d8ee3fc962de078f23b24f3 | # Dataset Card for "map2sat-central-belt-clarity-old-map20-samples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mespinosami/map2sat-central-belt-clarity-old-map20-samples | [
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#region-us
| # Dataset Card for "map2sat-central-belt-clarity-old-map20-samples"
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68abb13f0fdc4d88e75c1601cdbe8608a47900d3 |
# Dataset of futaba_tsukushi (BanG Dream!)
This is the dataset of futaba_tsukushi (BanG Dream!), containing 130 images and their tags.
The core tags of this character are `long_hair, bangs, black_hair, twintails, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv... | CyberHarem/futaba_tsukushi_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T14:48:05+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T19:11:41+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of futaba\_tsukushi (BanG Dream!)
=========================================
This is the dataset of futaba\_tsukushi (BanG Dream!), containing 130 images and their tags.
The core tags of this character are 'long\_hair, bangs, black\_hair, twintails, brown\_eyes', which are pruned in this dataset.
Images ar... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
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63c2408f0be600c9222e75451d7775f679a7716f |
# Dataset Card for "NFT-70M_transactions"
## Dataset summary
The *NFT-70M_transactions* dataset is the largest and most up-to-date collection of Non-Fungible Tokens (NFT) transactions between 2021 and 2023 sourced from [OpenSea](https://opensea.io), the leading trading platform in the Web3 ecosystem.
With more than 7... | MLNTeam-Unical/NFT-70M_transactions | [
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"task_categories:image-c... | 2023-09-26T14:48:21+00:00 | {"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["10M<n<100M"], "task_categories": ["time-series-forecasting", "text-classification", "feature-extraction", "text-generation", "zero-shot-classification", "text2text-generation", "sentence-similarity", "image-classification", "image-to-text", "text-to-i... | 2023-10-03T06:15:49+00:00 | [] | [
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========================================
Dataset summary
---------------
The *NFT-70M\_transactions* dataset is the largest and most up-to-date collection of Non-Fungible Tokens (NFT) transactions between 2021 and 2023 sourced from OpenSea, the leading trading platform in th... | [] | [
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f177f0106001c200ee671a96dc6388b295ea19c6 |
# Dataset of kitazawa_hagumi (BanG Dream!)
This is the dataset of kitazawa_hagumi (BanG Dream!), containing 93 images and their tags.
The core tags of this character are `short_hair, orange_hair, bangs, orange_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerocha... | CyberHarem/kitazawa_hagumi_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T15:03:44+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T18:57:39+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of kitazawa\_hagumi (BanG Dream!)
=========================================
This is the dataset of kitazawa\_hagumi (BanG Dream!), containing 93 images and their tags.
The core tags of this character are 'short\_hair, orange\_hair, bangs, orange\_eyes', which are pruned in this dataset.
Images are crawled... | [
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31ae39321bb39fbebcb9c95ebaae1e58974484b1 | # Dataset Card for "mlrs-pos-mt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | amitness/mlrs-pos-mt | [
"region:us"
] | 2023-09-26T15:05:32+00:00 | {"dataset_info": {"features": [{"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADV", "2": "COMP", "3": "CONJ_CORD", "4": "CONJ_SUB", "5": "DEF", "6": "FOC", "7": "FUT", "8": "GEN", "9": "GEN_DEF", "10": "GEN_PRON", "11": "HEMM", "12": "INT", "13": "KIEN", "14": "LIL", "15": "LIL_DEF", "16"... | 2023-09-26T15:27:07+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "mlrs-pos-mt"
More Information needed | [
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94308a70f4d72517b8b160c4d9f32409c65a1212 |
# Dataset of tamade_chiyu/珠手ちゆ (BanG Dream!)
This is the dataset of tamade_chiyu/珠手ちゆ (BanG Dream!), containing 107 images and their tags.
The core tags of this character are `long_hair, blue_eyes, bangs, red_hair, ahoge, animal_ears, headphones, fake_animal_ears, animal_ear_headphones, cat_ear_headphones, hair_betw... | CyberHarem/tamade_chiyu_bangdream | [
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#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of tamade\_chiyu/珠手ちゆ (BanG Dream!)
===========================================
This is the dataset of tamade\_chiyu/珠手ちゆ (BanG Dream!), containing 107 images and their tags.
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02b4572c0d6e996a6845c36b3f094e946af2a316 | # Dataset Card for "b6112e1b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | result-kand2-sdxl-wuerst-karlo/b6112e1b | [
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6c22d5df4e21b35c90de1fad09dab283520a0cca | # Dataset Card for "7aa2df49"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | result-kand2-sdxl-wuerst-karlo/7aa2df49 | [
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] | 2023-09-26T15:34:14+00:00 | {"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 172, "num_examples": 10}], "download_size": 1339, "dataset_size": 172}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-09-26T15:34:15+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "7aa2df49"
More Information needed | [
"# Dataset Card for \"7aa2df49\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"7aa2df49\"\n\nMore Information needed"
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544a0a34ef7ad63caebe2e2eb918c297265e903e |
# Dataset of udagawa_tomoe/宇田川巴 (BanG Dream!)
This is the dataset of udagawa_tomoe/宇田川巴 (BanG Dream!), containing 162 images and their tags.
The core tags of this character are `red_hair, long_hair, bangs, blue_eyes, earrings`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pix... | CyberHarem/udagawa_tomoe_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T15:35:48+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T18:34:07+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of udagawa\_tomoe/宇田川巴 (BanG Dream!)
============================================
This is the dataset of udagawa\_tomoe/宇田川巴 (BanG Dream!), containing 162 images and their tags.
The core tags of this character are 'red\_hair, long\_hair, bangs, blue\_eyes, earrings', which are pruned in this dataset.
Imag... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version... | [
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daf1c299f91bf06eda8ce59731cb402961b36fbb | # Dataset Card for "xxt_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuanmei424/xxt_en | [
"region:us"
] | 2023-09-26T15:47:07+00:00 | {"dataset_info": {"features": [{"name": "edit_prompt", "dtype": "string"}, {"name": "input_image", "dtype": "image"}, {"name": "edited_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5329195147.25, "num_examples": 2283951}], "download_size": 526250170, "dataset_size": 5329195147.25}} | 2023-09-26T18:00:06+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "xxt_en"
More Information needed | [
"# Dataset Card for \"xxt_en\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"xxt_en\"\n\nMore Information needed"
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c858cee986bebfecbfabc10996744fd4e906c9fd | # Dataset Card for "1b874213"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | result-kand2-sdxl-wuerst-karlo/1b874213 | [
"region:us"
] | 2023-09-26T15:50:26+00:00 | {"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 161, "num_examples": 10}], "download_size": 1306, "dataset_size": 161}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-09-26T15:50:27+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "1b874213"
More Information needed | [
"# Dataset Card for \"1b874213\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
"# Dataset Card for \"1b874213\"\n\nMore Information needed"
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3befd66c818162734266453dc1c85101fbc5beb3 |
# Dataset of kirigaya_touko/桐ヶ谷透子 (BanG Dream!)
This is the dataset of kirigaya_touko/桐ヶ谷透子 (BanG Dream!), containing 82 images and their tags.
The core tags of this character are `blonde_hair, long_hair, bangs, brown_eyes, breasts, earrings`, which are pruned in this dataset.
Images are crawled from many sites (e.... | CyberHarem/kirigaya_touko_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T16:07:10+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T18:16:59+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of kirigaya\_touko/桐ヶ谷透子 (BanG Dream!)
==============================================
This is the dataset of kirigaya\_touko/桐ヶ谷透子 (BanG Dream!), containing 82 images and their tags.
The core tags of this character are 'blonde\_hair, long\_hair, bangs, brown\_eyes, breasts, earrings', which are pruned in th... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
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7e19fc224b9fca8d95aa4301c58eb220a70ef079 |
# Dataset of yashio_rui/八潮瑠唯 (BanG Dream!)
This is the dataset of yashio_rui/八潮瑠唯 (BanG Dream!), containing 140 images and their tags.
The core tags of this character are `short_hair, bangs, black_hair, hair_between_eyes, breasts, purple_eyes, large_breasts, earrings, pink_eyes`, which are pruned in this dataset.
I... | CyberHarem/yashio_rui_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T16:10:00+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T18:06:17+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of yashio\_rui/八潮瑠唯 (BanG Dream!)
=========================================
This is the dataset of yashio\_rui/八潮瑠唯 (BanG Dream!), containing 140 images and their tags.
The core tags of this character are 'short\_hair, bangs, black\_hair, hair\_between\_eyes, breasts, purple\_eyes, large\_breasts, earrings,... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
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6c933760e5e6dfba752a3ba8f753c76148ae6222 |
# Dataset of nyubara_reona/鳰原令王那 (BanG Dream!)
This is the dataset of nyubara_reona/鳰原令王那 (BanG Dream!), containing 52 images and their tags.
The core tags of this character are `multicolored_hair, bangs, long_hair, twintails, blunt_bangs, two-tone_hair, pink_hair, hair_ornament, blue_hair, sidelocks, red_eyes`, whi... | CyberHarem/nyubara_reona_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T16:32:18+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T18:30:08+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of nyubara\_reona/鳰原令王那 (BanG Dream!)
=============================================
This is the dataset of nyubara\_reona/鳰原令王那 (BanG Dream!), containing 52 images and their tags.
The core tags of this character are 'multicolored\_hair, bangs, long\_hair, twintails, blunt\_bangs, two-tone\_hair, pink\_hair,... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
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0f931df98e45c7790a1565e8719824602db99f8f | https://youtu.be/gn0Z_glYJ90?list=PLXA0IWa3BpHnrfGY39YxPYFvssnwD8awg&t=989 | lunarflu/generative-AI-meets-responsible-AI-practical-challenges-and-opportunities | [
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#region-us
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2f7482dacc8f102de6329851ee7f1c2304092dae | # Dataset Card for "top10_primary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ricardosantoss/top10_primary | [
"region:us"
] | 2023-09-26T16:49:42+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "TEXT", "dtype": "string"}, {"name": "ICD9_CODE", "sequence": "string"}], "splits":... | 2023-09-26T16:50:02+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "top10_primary"
More Information needed | [
"# Dataset Card for \"top10_primary\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"top10_primary\"\n\nMore Information needed"
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9d604e8921b1b242507385f84e10fc45d95e9c87 |
# Dataset of hiromachi_nanami (BanG Dream!)
This is the dataset of hiromachi_nanami (BanG Dream!), containing 104 images and their tags.
The core tags of this character are `bangs, pink_eyes, long_hair, pink_hair, hair_ornament, two_side_up, ribbon`, which are pruned in this dataset.
Images are crawled from many si... | CyberHarem/hiromachi_nanami_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T16:52:12+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T18:59:51+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of hiromachi\_nanami (BanG Dream!)
==========================================
This is the dataset of hiromachi\_nanami (BanG Dream!), containing 104 images and their tags.
The core tags of this character are 'bangs, pink\_eyes, long\_hair, pink\_hair, hair\_ornament, two\_side\_up, ribbon', which are pruned... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
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