id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
swedish_ner_corpus | 2023-01-25T14:45:21.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
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"size_categories:1K<n<10K",
"source_datasets:original",
"language:sv",
"license:cc-by-4.0",
"region:us"
] | null | Webbnyheter 2012 from Spraakbanken, semi-manually annotated and adapted for CoreNLP Swedish NER. Semi-manually defined in this case as: Bootstrapped from Swedish Gazetters then manually correcte/reviewed by two independent native speaking swedish annotators. No annotator agreement calculated. | null | 1 | 91 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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- sv
license:
- cc-by-4.0
multilinguality:
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size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Swedish NER Corpus
dataset_info:
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AlekseyKorshuk/horror-scripts | 2022-02-10T18:26:41.000Z | [
"region:us"
] | AlekseyKorshuk | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 1 | 91 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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Nexdata/accented_english | 2023-08-31T02:59:20.000Z | [
"region:us"
] | Nexdata | null | null | 5 | 91 | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for accented-english
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supporte... | 3,309 | [
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TristanBehrens/js-fakes-4bars | 2022-01-03T15:53:23.000Z | [
"region:us"
] | TristanBehrens | null | null | 9 | 91 | 2022-03-02T23:29:22 | # JSFakes (Dr. Tristan Behrens).
This is a tokenized version of the JS-Fakes dataset by Omar Peracha. The original dataset can be found here:
[js-fakes.git](https://github.com/omarperacha/js-fakes.git)
The representation is four tracks with four bars per track.
## Purpose.
This dataset is a good starting point for ... | 635 | [
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allegro/klej-psc | 2022-10-26T09:01:54.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
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"source_datasets:original",
"language:pl",
"license:cc-by-sa-3.0",
"paraphrase-classification",
"region:us"
... | allegro | null | null | 0 | 91 | 2022-03-02T23:29:22 | ---
annotations_creators:
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license:
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multilinguality:
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size_categories:
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- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Polish Summaries Corpus
tags:
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animesh/autonlp-data-peptides | 2021-10-12T09:08:03.000Z | [
"region:us"
] | animesh | null | null | 0 | 91 | 2022-03-02T23:29:22 | [Deep learning the collisional cross sections of the peptide universe from a million experimental values](https://www.nature.com/articles/s41467-021-21352-8)
[Data](http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD017703) generated from [MaxQuant](http://coxdocs.org/doku.php?id=maxquant:start) output
``... | 642 | [
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anuragshas/ha_opus100_processed | 2022-02-03T22:29:36.000Z | [
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anuragshas/hi_opus100_processed | 2022-01-30T15:14:09.000Z | [
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anuragshas/mr_cc100_processed | 2022-02-06T14:46:02.000Z | [
"region:us"
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anuragshas/mt_opus100_processed | 2022-02-02T08:14:25.000Z | [
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anuragshas/sk_opus100_processed | 2022-02-05T08:22:48.000Z | [
"region:us"
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anuragshas/sl_opus100_processed | 2022-02-02T13:54:18.000Z | [
"region:us"
] | anuragshas | null | null | 0 | 91 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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anzorq/kbd-ru-jsonl-tmp | 2022-01-14T01:19:45.000Z | [
"region:us"
] | anzorq | null | null | 0 | 91 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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davanstrien/embellishments-sample | 2022-02-24T15:44:29.000Z | [
"region:us"
] | davanstrien | null | null | 0 | 91 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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DFKI-SLT/cross_ner | 2023-01-19T09:17:38.000Z | [
"task_categories:token-classification",
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"annotations_creators:expert-generated",
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"source_datasets:extended|conll2003",
"language:en",
"cross domain",
"ai",
"news",
"musi... | DFKI-SLT | CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains
(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for
different domains. Additionally, CrossNER also includes unlabeled domain-related corpora fo... | @article{liu2020crossner,
title={CrossNER: Evaluating Cross-Domain Named Entity Recognition},
author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung},
year={2020},
eprint={2012.04373},
archivePrefix={arXiv},
... | 0 | 91 | 2023-01-19T09:17:08 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
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license: []
multilinguality:
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pretty_name: CrossNER is a cross-domain dataset for named entity recognition
size_categories:
- 10K<n<100K
source_datasets:
- extended|conll2003
tags:
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- ai
- news
- music
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0-hero/OIG-small-chip2 | 2023-03-16T20:10:19.000Z | [
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"region:us"
] | 0-hero | null | null | 6 | 91 | 2023-03-16T17:59:26 | ---
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task_categories:
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language:
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---
# Dataset Card for "O... | 480 | [
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zhengyun21/PMC-Patients | 2023-07-13T06:11:59.000Z | [
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"patient summary",
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] | zhengyun21 | null | null | 60 | 91 | 2023-04-25T13:20:16 | ---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- patient summary
- medical
- biology
size_categories:
- 100K<n<1M
---
# Dataset Card for PMC-Patients
## Dataset Description
- **Homepage:** https://github.com/pmc-patients/pmc-patients
- **Repository:** https://github.com/pmc-patients/pmc-patients
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sukantan/nyaya-acts | 2023-06-08T06:56:32.000Z | [
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sukantan/nyaya-ae-all-mpnet-base-v2-ftlegal-v3 | 2023-06-25T12:23:15.000Z | [
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] | sukantan | null | null | 0 | 91 | 2023-06-25T10:20:41 | ---
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MAPS-research/GEMRec-Metadata | 2023-08-07T04:42:05.000Z | [
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tanvinsharma/books100 | 2023-09-27T10:38:26.000Z | [
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hippocrates/DDI2013_train | 2023-10-12T19:18:48.000Z | [
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] | hippocrates | null | null | 0 | 91 | 2023-10-12T19:18:42 | ---
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AmanK1202/CNNOVEng_train | 2023-10-19T15:59:14.000Z | [
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MemGPT/example_short_stories | 2023-10-19T02:04:57.000Z | [
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bswac | 2022-11-03T16:15:55.000Z | [
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"language:bs",... | null | The Bosnian web corpus bsWaC was built by crawling the .ba top-level domain in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and lang... | @misc{11356/1062,
title = {Bosnian web corpus {bsWaC} 1.1},
author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
url = {http://hdl.handle.net/11356/1062},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{... | 0 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- bs
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: n... | 3,965 | [
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ehealth_kd | 2023-01-25T14:29:46.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
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"size_categories:1K<n<10K",
"source_datasets:original",
"language:es",
"license:cc-by-nc-sa-4.0",
"relation-pre... | null | Dataset of the eHealth Knowledge Discovery Challenge at IberLEF 2020. It is designed for
the identification of semantic entities and relations in Spanish health documents. | @inproceedings{overview_ehealthkd2020,
author = {Piad{-}Morffis, Alejandro and
Guti{\'{e}}rrez, Yoan and
Cañizares-Diaz, Hian and
Estevez{-}Velarde, Suilan and
Almeida{-}Cruz, Yudivi{\'{a}}n and
Muñoz, Rafael and
Montoyo, And... | 1 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
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- expert-generated
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- es
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- cc-by-nc-sa-4.0
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- monolingual
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- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: eHealth-KD
tags:
-... | 12,488 | [
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igbo_english_machine_translation | 2022-11-18T20:08:55.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:ig",
"license:unknown",
"arxiv:2004.00648",
"region:us"
] | null | Parallel Igbo-English Dataset | @misc{ezeani2020igboenglish,
title={Igbo-English Machine Translation: An Evaluation Benchmark},
author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple},
year={2020},
eprint={2004.00648},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://a... | 1 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- ig
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: igbonlp-datasets
pretty_name: IgboNLP Datasets
dataset_info:
feat... | 3,374 | [
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ms_terms | 2022-11-03T16:08:00.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:af",
"language:am",
"language:ar",
"language:as",
"la... | null | The Microsoft Terminology Collection can be used to develop localized versions of applications that integrate with Microsoft products.
It can also be used to integrate Microsoft terminology into other terminology collections or serve as a base IT glossary
for language development in the nearly 100 languages available. ... | null | 3 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
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- expert-generated
language:
- af
- am
- ar
- as
- az
- be
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- bn
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- ca
- chr
- cs
- cy
- da
- de
- el
- en
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- et
- eu
- fa
- fi
- fil
- fr
- ga
- gd
- gl
- gu
- guc
- ha
- he
- hi
- hr
- hu
- hy
- id
- ig
- is
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- iu
- ja
- ka
- kk
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- kn... | 4,200 | [
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msr_genomics_kbcomp | 2023-01-25T14:40:48.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"genomics-knowledge-base-bompletion",
"region:us"
] | null | The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowle... | @inproceedings{toutanova-etal-2016-compositional,
title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text",
author = "Toutanova, Kristina and
Lin, Victoria and
Yih, Wen-tau and
Poon, Hoifung and
Quirk, Chris",
booktitle = "Proceedings of the 54... | 0 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: MsrGenomicsKbcomp
tags:
- genomics-knowledge-base-bompletion
data... | 6,312 | [
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mutual_friends | 2022-11-18T21:31:53.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1... | null | Our goal is to build systems that collaborate with people by exchanging
information through natural language and reasoning over structured knowledge
base. In the MutualFriend task, two agents, A and B, each have a private
knowledge base, which contains a list of friends with multiple attributes
(e.g., name, school, maj... | @inproceedings{he-etal-2017-learning,
title = "Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings",
author = "He, He and
Balakrishnan, Anusha and
Eric, Mihail and
Liang, Percy",
booktitle = "Proceedings of the 55th Annual Meeting of the Association ... | 2 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
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paperswithcode_id: mutualfriends
pretty_name:... | 11,255 | [
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nkjp-ner | 2023-01-25T14:41:28.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:gpl-3.0",
"region:us"
] | null | The NKJP-NER is based on a human-annotated part of National Corpus of Polish (NKJP). We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity. | @book{przepiorkowski2012narodowy,
title={Narodowy korpus jezyka polskiego},
author={Przepi{\'o}rkowski, Adam},
year={2012},
publisher={Naukowe PWN}
} | 1 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: NJKP NER
dataset_info:
features:
... | 4,446 | [
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wmt20_mlqe_task3 | 2023-01-25T15:02:49.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|amazon_us_reviews",
"language:en",
"language:fr",
"license:unknown",
"... | null | This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced thi... | Not available. | 0 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
language:
- en
- fr
license:
- unknown
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|amazon_us_reviews
task_categories:
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task_ids: []
pretty_name: WMT20 - MultiLingual Qualit... | 14,790 | [
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GEM/cochrane-simplification | 2022-10-24T15:30:10.000Z | [
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | GEM | This dataset measures the ability for a model to simplify paragraphs of medical text through the omission non-salient information and simplification of medical jargon. | @inproceedings{devaraj-etal-2021-paragraph,
title = "Paragraph-level Simplification of Medical Texts",
author = "Devaraj, Ashwin and
Marshall, Iain and
Wallace, Byron and
Li, Junyi Jessy",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association f... | 3 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: cochrane-simplification
---
# Dataset Card for GEM/coc... | 16,372 | [
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GEM/wiki_auto_asset_turk | 2022-10-24T15:31:10.000Z | [
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:crowd-sourced",
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"size_categories:unknown",
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"... | GEM | WikiAuto provides a set of aligned sentences from English Wikipedia and Simple
English Wikipedia as a resource to train sentence simplification systems.
The authors first crowd-sourced a set of manual alignments between sentences in
a subset of the Simple English Wikipedia and their corresponding versions in
English W... | @inproceedings{jiang-etal-2020-neural,
title = "Neural {CRF} Model for Sentence Alignment in Text Simplification",
author = "Jiang, Chao and
Maddela, Mounica and
Lan, Wuwei and
Zhong, Yang and
Xu, Wei",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Co... | 3 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- en
license:
- other
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: wiki_auto_asset_turk
---
# Dataset Card for GEM/w... | 31,669 | [
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0.0... |
Llamacha/monolingual-quechua-iic | 2022-10-02T14:40:00.000Z | [
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
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"multilinguality:monolingual",
"size_categories:1M<n<5M",
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"language:qu",
"license:apache-2.0",
"regi... | Llamacha | null | null | 0 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- qu
license:
- apache-2.0
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
---
# Dataset Card for Monolingual... | 4,676 | [
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caltonji/harrypotter_squad_v2 | 2021-12-31T19:11:29.000Z | [
"region:us"
] | caltonji | null | null | 0 | 90 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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caltonji/harrypotter_squad_v2_2 | 2021-12-31T20:01:23.000Z | [
"region:us"
] | caltonji | null | null | 0 | 90 | 2022-03-02T23:29:22 | ## Dataset Summary
Contains 15 Harry Potter trivia questions in Squadv2 format, 3 of which are unanswerable.
## Model Performance
[Test Notebook](https://colab.research.google.com/drive/1VFUJKV7eun68XgQDAHSHsbvoM_CGHzWA?usp=sharing)
| Model | exact | f1 |
| ----------- | ----------- | ----------- |
| Albert Ba... | 580 | [
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cointegrated/ru-paraphrase-NMT-Leipzig | 2022-10-23T12:23:15.000Z | [
"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:extended|other",
"language:ru",
"license:cc-by-4.0",
"conditional-text-generation",
"paraphrase-generation",
... | cointegrated | null | null | 4 | 90 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language:
- ru
license:
- cc-by-4.0
multilinguality:
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size_categories:
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source_datasets:
- extended|other
task_categories:
- text-generation
pretty_name: ru-paraphrase-NMT-Leipzig
tags:
- conditional-text-generatio... | 11,995 | [
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TheBritishLibrary/BookGenreSnorkelAnnotated | 2021-12-17T11:37:52.000Z | [
"region:us"
] | TheBritishLibrary | null | null | 0 | 90 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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enelpol/czywiesz | 2022-10-25T09:07:45.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:unknown",
"region:us"
] | enelpol | null | null | 2 | 90 | 2022-03-02T23:29:22 | ---
language:
- pl
license:
- unknown
multilinguality:
- monolingual
pretty_name: Czywiesz
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
---
This is an extract of the original [Czywiesz](https://clarin-pl.eu/dspace/handle/11321/39) dataset. I... | 475 | [
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fuliucansheng/minicoco | 2021-08-03T07:19:48.000Z | [
"region:us"
] | fuliucansheng | MINICOCO2017 | MINICOCO2017 | 0 | 90 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.03793334... |
bigbio/bionlp_st_2013_pc | 2022-12-22T15:44:03.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | bigbio | the Pathway Curation (PC) task is a main event extraction task of the BioNLP shared task (ST) 2013.
The PC task concerns the automatic extraction of biomolecular reactions from text.
The task setting, representation and semantics are defined with respect to pathway
model standards and ontologies (SBML, BioPAX, SBO) and... | @inproceedings{ohta-etal-2013-overview,
title = "Overview of the Pathway Curation ({PC}) task of {B}io{NLP} Shared Task 2013",
author = "Ohta, Tomoko and
Pyysalo, Sampo and
Rak, Rafal and
Rowley, Andrew and
Chun, Hong-Woo and
Jung, Sung-Jae and
Choi, Sung-Pil and
... | 0 | 90 | 2022-11-13T22:07:14 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: GENIA_PROJECT_LICENSE
pretty_name: BioNLP 2013 PC
homepage: https://github.com/openbiocorpora/bionlp-st-2013-pc
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- EVENT_EXTRACTION
- NAMED_ENTITY_... | 1,823 | [
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... |
HiTZ/alpaca_mt | 2023-04-07T15:15:55.000Z | [
"task_categories:text-generation",
"task_ids:dialogue-modeling",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:tatsu-lab/alpaca",
"language:en",
"language:pt... | HiTZ | Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. This dataset also includes machine-translated data for 6 Iberian languag... | @misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howp... | 7 | 90 | 2023-03-22T15:27:30 | ---
annotations_creators:
- no-annotation
language:
- en
- pt
- es
- ca
- eu
- gl
- at
language_creators:
- machine-generated
license: cc-by-nc-4.0
multilinguality:
- multilingual
- translation
pretty_name: Alpaca MT
size_categories:
- 10K<n<100K
source_datasets:
- tatsu-lab/alpaca
tags:
- instruction-finetuning
task_c... | 10,371 | [
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Francesco/construction-safety-gsnvb | 2023-03-30T09:11:51.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
] | Francesco | null | null | 2 | 90 | 2023-03-30T09:11:17 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
lengt... | 3,561 | [
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0.0276641845703125,
-0.042083740234375,
-0.08026123046875,
-0.04779052734375,
... |
BelleGroup/multiturn_chat_0.8M | 2023-04-02T09:15:32.000Z | [
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:gpl-3.0",
"region:us"
] | BelleGroup | null | null | 77 | 90 | 2023-04-02T08:55:44 | ---
license: gpl-3.0
task_categories:
- text2text-generation
language:
- zh
size_categories:
- 100K<n<1M
---
# Multiturn Chat 0.8M
## 内容
包含约80万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的*用户*与*助手*的多轮对话。
注意：此数据集是由ChatGPT产生的，未经过严格校验，内容可能包含错误。使用过程中请注意这一点。
**instruction**中包含多轮对话的上文内容，以*Human:*和*Assistant:*区分，**... | 3,334 | [
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0.00406646728... |
clarin-knext/nfcorpus-pl | 2023-06-07T08:11:26.000Z | [
"language:pl",
"arxiv:2305.19840",
"region:us"
] | clarin-knext | null | null | 0 | 90 | 2023-06-06T22:41:34 | ---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: konrad.wojtasik@pwr.edu.pl | 201 | [
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Vipitis/Shadertoys-FunctionGeneration-dev | 2023-10-15T23:00:48.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"language:code",
"license:cc-by-nc-sa-3.0",
"shader",
"region:us"
] | Vipitis | null | null | 2 | 90 | 2023-08-14T21:41:28 | ---
language:
- en
- code
license: cc-by-nc-sa-3.0
size_categories:
- 10K<n<100K
task_categories:
- text-generation
pretty_name: 'Shadertoys: shadereval task 2: Function Generation (development build)'
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/t... | 1,172 | [
[
-0.0290985107421875,
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0.04180908203125,
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0.037353515625,
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-0.041534423828125,
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... |
morpheuslord/cve-llm-training | 2023-08-30T06:26:48.000Z | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] | morpheuslord | null | null | 6 | 90 | 2023-08-15T06:15:00 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: CVE-LLm-Dataset
data_source: Custom data collected from the CVE database
data_formats: JSONL
---
# CVE-llm_dataset
This dataset is intended to train an LLM model for an utterly CVE-focused input and output.
## Data extr... | 2,135 | [
[
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-0.0... |
yongsun-yoon/open-ner-english | 2023-08-19T03:41:49.000Z | [
"region:us"
] | yongsun-yoon | null | null | 0 | 90 | 2023-08-19T03:40:23 | ---
dataset_info:
features:
- name: text
dtype: string
- name: entities
list:
- name: entity_mentions
sequence: string
- name: entity_type
dtype: string
splits:
- name: train
num_bytes: 51881074.954063065
num_examples: 36711
- name: validation
num_bytes: 12970622.0459... | 553 | [
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0.... |
asyafiqe/orca_mini_v1_indonesia | 2023-08-27T10:54:58.000Z | [
"license:apache-2.0",
"region:us"
] | asyafiqe | null | null | 1 | 90 | 2023-08-27T10:53:05 | ---
license: apache-2.0
---
This is dataset is a modified version of psmathur's [orca_mini_v1](https://huggingface.co/datasets/psmathur/orca_mini_v1_dataset) dataset translated into Bahasa Indonesia by Google Translate. | 220 | [
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0.... |
yujiepan/wikitext-tiny | 2023-08-31T18:05:09.000Z | [
"region:us"
] | yujiepan | null | null | 0 | 90 | 2023-08-31T18:01:07 | This dataset is sampled from `wikitext/wikitext-2-v1/train`.
Codes to generate this dataset:
```python
import datasets
dataset = datasets.load_dataset('wikitext', 'wikitext-2-v1')
selected = []
i = -1
while len(selected) < 24:
i += 1
text = dataset['train'][i]['text']
if 8 < len(text.split(' ')) <= 16 an... | 427 | [
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-0.01715087... |
yzhuang/autotree_pmlb_100000_spambase_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T19:42:03.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 90 | 2023-09-07T19:41:40 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... | 848 | [
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0... |
yzhuang/autotree_automl_100000_covertype_sgosdt_l256_dim10_d3_sd0 | 2023-09-08T02:06:34.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 90 | 2023-09-08T02:06:02 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... | 851 | [
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0.... |
warshakhan/donut_vqa_ISynHMP | 2023-09-15T07:12:51.000Z | [
"task_categories:visual-question-answering",
"language:en",
"license:unknown",
"medical",
" prescriptions",
"region:us"
] | warshakhan | null | null | 0 | 90 | 2023-09-14T11:10:50 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 578804498
... | 806 | [
[
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0.0711669921875,
0.036651611328125,
-0.0635986328125,
-0.04949951171875,
-0.041229248046875,... |
ostapeno/qa-openai_batched_icl5_clen512_maxD-1_maxC2500_0_cleaned | 2023-10-25T19:00:26.000Z | [
"region:us"
] | ostapeno | null | null | 0 | 90 | 2023-10-25T16:41:26 | Config:
{
"type": "QATransformConfig",
"model_setting": "openai_batched",
"icl_examples": 0,
"icl_dataset": "lukaemon/mmlu",
"icl_split": "validation",
"icl_use_options": true,
"num_iterations": 1,
"max_context_length": 512,
"max_tokens_instruction": 2048,
"max_tokens_response":... | 429 | [
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... |
finer | 2023-01-25T14:30:30.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fi",
"license:mit",
"arxiv:1908.04212",
"region:us"
... | null | The directory data contains a corpus of Finnish technology related news articles with a manually prepared
named entity annotation (digitoday.2014.csv). The text material was extracted from the archives of Digitoday,
a Finnish online technology news source (www.digitoday.fi). The corpus consists of 953 articles
(193,742... | @article{ruokolainen2019finnish,
title={A finnish news corpus for named entity recognition},
author={Ruokolainen, Teemu and Kauppinen, Pekka and Silfverberg, Miikka and Lind{\'e}n, Krister},
journal={Language Resources and Evaluation},
pages={1--26},
year={2019},
publisher={Springer}
} | 1 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- fi
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: finer
pretty_name: Finnish News C... | 4,621 | [
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-0.086181640625,
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0.0186157... |
isixhosa_ner_corpus | 2023-01-25T14:33:10.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:xh",
"license:other",
"region:us"
] | null | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | @inproceedings{isixhosa_ner_corpus,
author = {K. Podile and
Roald Eiselen},
title = {NCHLT isiXhosa Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluat... | 0 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- xh
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: IsixhosaNerCorpus
license_de... | 5,560 | [
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... |
journalists_questions | 2023-01-25T14:33:26.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:unknown",
"question-identification",
"region:us"
] | null | \
The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic
tweets manually labeled for question identification over Arabic tweets posted by journalists. | \
@inproceedings{hasanain2016questions,
title={What Questions Do Journalists Ask on Twitter?},
author={Hasanain, Maram and Bagdouri, Mossaab and Elsayed, Tamer and Oard, Douglas W},
booktitle={Tenth International AAAI Conference on Web and Social Media},
year={2016}
} | 0 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: JournalistsQuestions
tags:
- question-identification
dataset_inf... | 4,853 | [
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0.016525... |
multi_nli_mismatch | 2023-04-05T10:10:18.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:ori... | null | The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre gener... | @InProceedings{N18-1101,
author = {Williams, Adina
and Nangia, Nikita
and Bowman, Samuel},
title = {A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference},
booktitle = {Proceedings of the 2018 Conference of
the North American Chapter of th... | 1 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-3.0
- cc-by-sa-3.0
- mit
- other
license_details: Open Portion of the American National Corpus
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- tex... | 6,985 | [
[
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myanmar_news | 2023-01-25T14:41:11.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:my",
"license:gpl-3.0",
"region:us"
] | null | The Myanmar news dataset contains article snippets in four categories:
Business, Entertainment, Politics, and Sport.
These were collected in October 2017 by Aye Hninn Khine | null | 1 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- my
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: MyanmarNews
dataset_info:
features:
- name: text
... | 1,790 | [
[
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narrativeqa_manual | 2022-11-18T21:32:14.000Z | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1712.07040",
"region:us"
] | null | The Narrative QA Manual dataset is a reading comprehension dataset, in which the reader must answer questions about stories by reading entire books or movie scripts. The QA tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pa... | @article{kovcisky2018narrativeqa,
title={The narrativeqa reading comprehension challenge},
author={Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}} and Schwarz, Jonathan and Blunsom, Phil and Dyer, Chris and Hermann, Karl Moritz and Melis, G{\'a}bor and Grefenstette, Edward},
journal={Transactions of the Association for Comput... | 0 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
paperswithcode_id: narrativeqa
pretty_name: NarrativeQA
dat... | 9,716 | [
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ro_sent | 2023-01-25T14:43:14.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ro",
"license:unknown",
"arxiv:2009.08712",
"region:us"
] | null | This dataset is a Romanian Sentiment Analysis dataset.
It is present in a processed form, as used by the authors of `Romanian Transformers`
in their examples and based on the original data present in
`https://github.com/katakonst/sentiment-analysis-tensorflow`. The original dataset is collected
from product and movie r... | @article{dumitrescu2020birth,
title={The birth of Romanian BERT},
author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius and Pyysalo, Sampo},
journal={arXiv preprint arXiv:2009.08712},
year={2020}
} | 0 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ro
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: RoSent
dataset_info:
features:
- name: orig... | 5,360 | [
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tsac | 2023-01-25T14:54:29.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:aeb",
"license:lgpl-3.0",
"region:us"
] | null | Tunisian Sentiment Analysis Corpus.
About 17k user comments manually annotated to positive and negative polarities. This corpus is collected from Facebook users comments written on official pages of Tunisian radios and TV channels namely Mosaique FM, JawhraFM, Shemes FM, HiwarElttounsi TV and Nessma TV. The corpus is ... | @inproceedings{medhaffar-etal-2017-sentiment,
title = "Sentiment Analysis of {T}unisian Dialects: Linguistic Ressources and Experiments",
author = "Medhaffar, Salima and
Bougares, Fethi and
Est{`e}ve, Yannick and
Hadrich-Belguith, Lamia",
booktitle = "Proceedings of the Third {A}rabic N... | 0 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- aeb
license:
- lgpl-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: tsac
pretty_name: Tunisian S... | 3,436 | [
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0.003622... |
turku_ner_corpus | 2023-01-25T14:54:48.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fi",
"license:cc-by-nc-sa-4.0",
"region:us"... | null | An open, broad-coverage corpus for Finnish named entity recognition presented in Luoma et al. (2020) A Broad-coverage Corpus for Finnish Named Entity Recognition. | @inproceedings{luoma-etal-2020-broad,
title = "A Broad-coverage Corpus for {F}innish Named Entity Recognition",
author = {Luoma, Jouni and Oinonen, Miika and Pyyk{\"o}nen, Maria and Laippala, Veronika and Pyysalo, Sampo},
booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
year = "2020",... | 0 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- fi
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Turku NER corpus... | 3,770 | [
[
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... |
ARKseal/YFCC14M_subset_webdataset | 2021-11-27T22:47:47.000Z | [
"region:us"
] | ARKseal | null | null | 0 | 89 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
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AlexZapolskii/zapolskii-amazon | 2021-12-22T22:13:57.000Z | [
"region:us"
] | AlexZapolskii | null | null | 0 | 89 | 2022-03-02T23:29:22 | dataset from kaggle https://www.kaggle.com/c/amazon-pet-product-reviews-classification | 86 | [
[
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Baybars/parla_text_corpus | 2022-10-21T15:29:15.000Z | [
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:various",
"multilinguality:monolingual",
"size_categories:100k<n<1M",
"source_datasets:found",
"language:ca",
"license:cc-by-4.0",
"robust-speech-event",
"region:us"
] | Baybars | null | null | 0 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- various
language:
- ca
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: ParlaTextCorpus
size_categories:
- 100k<n<1M
source_datasets:
- found
task_categories:
- sequence-modeling
task_ids:
- language-modeling
tags:
- robust-speech-event
---
... | 446 | [
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0.... |
Nexdata/accented_mandarin | 2023-08-31T03:09:30.000Z | [
"region:us"
] | Nexdata | null | null | 3 | 89 | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for accented_mandarin
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Support... | 3,326 | [
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0.0... |
Nexdata/mandarin_chinese | 2023-08-31T03:06:29.000Z | [
"region:us"
] | Nexdata | null | null | 5 | 89 | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for mandarin_chinese
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supporte... | 3,208 | [
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Nexdata/mixed_speech_chinese_english | 2023-08-31T03:12:08.000Z | [
"region:us"
] | Nexdata | null | null | 4 | 89 | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for mixed_speech_chinese_english
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
... | 3,341 | [
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Nexdata/multi_language | 2023-08-31T03:07:33.000Z | [
"region:us"
] | Nexdata | null | null | 3 | 89 | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for multi_language
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported ... | 3,513 | [
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Davlan/conll2003_de_noMISC | 2021-10-05T09:06:35.000Z | [
"region:us"
] | Davlan | null | null | 0 | 89 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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GEM/cs_restaurants | 2022-10-24T15:30:14.000Z | [
"task_categories:conversational",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:cs",
"license:cc-by-sa-4.0",
"dialog-response-generation",
"region:us"
] | GEM | The task is generating responses in the context of a (hypothetical) dialogue
system that provides information about restaurants. The input is a basic
intent/dialogue act type and a list of slots (attributes) and their values.
The output is a natural language sentence. | @inproceedings{cs_restaurants,
address = {Tokyo, Japan},
title = {Neural {Generation} for {Czech}: {Data} and {Baselines}},
shorttitle = {Neural {Generation} for {Czech}},
url = {https://www.aclweb.org/anthology/W19-8670/},
urldate = {2019-10-18},
booktitle = {Proceedings of the 12th {International} {Conference} ... | 1 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- cs
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: cs_restaurants
tags:
- dialog-response-generation
---
# Dataset Card for GEM... | 20,221 | [
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GEM/squad_v2 | 2022-10-24T15:30:29.000Z | [
"task_categories:other",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"question-generation",
"arxiv:1806.03822",
"region:us"
] | GEM | SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
also determine when no answer is supported by the paragraph and absta... | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250}... | 0 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: squad_v2
tags:
- question-generation
---
# Dataset Card for GEM/squad_v2
##... | 17,828 | [
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Graphcore/gqa-lxmert | 2023-01-09T11:28:07.000Z | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | Graphcore | GQA is a new dataset for real-world visual reasoning and compositional question answering,
seeking to address key shortcomings of previous visual question answering (VQA) datasets. | @inproceedings{hudson2019gqa,
title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
author={Hudson, Drew A and Manning, Christopher D},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={6700--6709},
year={2019}
} | 0 | 89 | 2022-03-02T23:29:22 | ---
language:
- en
license:
- cc-by-4.0
---
| 45 | [
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khondoker/SentNoB | 2023-04-23T10:32:36.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:bn",
"region:us"
] | khondoker | null | null | 0 | 89 | 2022-03-02T23:29:22 | ---
language:
- bn
task_categories:
- text-classification
pretty_name: SentNoB
task_ids:
- sentiment-classification
annotations_creators:
- expert-generated
language_creators:
- expert-generated
paperswithcode_id: sentnob
---
# Dataset Card for "SentNoB"
### Dataset Summary
Social Media User Comments' Sentiment Anal... | 790 | [
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Lucylulu/imdb | 2021-12-08T20:20:37.000Z | [
"region:us"
] | Lucylulu | null | null | 0 | 89 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
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allegro/klej-polemo2-in | 2022-08-30T06:57:28.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-4.0",
"region:us"
] | allegro | null | null | 0 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: 'PolEmo2.0-IN'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
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---
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allegro/klej-polemo2-out | 2022-08-30T06:57:07.000Z | [
"task_categories:text-classification",
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"region:us"
] | allegro | null | null | 0 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
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license:
- cc-by-sa-4.0
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pretty_name: 'PolEmo2.0-OUT'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
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---
# ... | 6,244 | [
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MLCommons/ml_spoken_words | 2022-12-06T11:11:02.000Z | [
"task_categories:audio-classification",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:extended|common_voice",
"language:ar",
"language:as",
"language:br",
"language:ca",
"language:cnh",
"langu... | MLCommons | Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken
words in 50 languages collectively spoken by over 5 billion people, for academic
research and commercial applications in keyword spotting and spoken term search,
licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords,
tot... | @inproceedings{mazumder2021multilingual,
title={Multilingual Spoken Words Corpus},
author={Mazumder, Mark and Chitlangia, Sharad and Banbury, Colby and Kang, Yiping and Ciro, Juan Manuel and Achorn, Keith and Galvez, Daniel and Sabini, Mark and Mattson, Peter and Kanter, David and others},
booktitle={Thirty-fifth... | 16 | 89 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
- ar
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Yaxin/SemEval2016Task5Raw | 2022-08-15T08:19:35.000Z | [
"region:us"
] | Yaxin | A collection of SemEval2016 specifically designed to aid research in multilingual Aspect Based Sentiment Analysis. | @inproceedings{pontiki2016semeval,
title={Semeval-2016 task 5: Aspect based sentiment analysis},
author={Pontiki, Maria and Galanis, Dimitrios and Papageorgiou, Haris and Androutsopoulos, Ion and Manandhar, Suresh and Al-Smadi, Mohammad and Al-Ayyoub, Mahmoud and Zhao, Yanyan and Qin, Bing and De Clercq, Orph{\'e}e... | 2 | 89 | 2022-04-20T14:39:38 | Entry not found | 15 | [
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bigbio/scai_chemical | 2022-12-22T15:46:32.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | SCAI Chemical is a corpus of MEDLINE abstracts that has been annotated
to give an overview of the different chemical name classes
found in MEDLINE text. | @inproceedings{kolarik:lrec-ws08,
author = {Kol{\'a}{\vr}ik, Corinna and Klinger, Roman and Friedrich, Christoph M and Hofmann-Apitius, Martin and Fluck, Juliane},
title = {Chemical Names: {T}erminological Resources and Corpora Annotation},
booktitle = {LREC Workshop on Building and Evaluating Resources fo... | 2 | 89 | 2022-11-13T22:11:56 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: SCAI Chemical
homepage: https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/corpora-for-chemical-entity-recognition.html
bigbio_pubmed: True
bigbio... | 1,200 | [
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gonzalobenegas/clinvar | 2023-02-09T23:32:45.000Z | [
"region:us"
] | gonzalobenegas | null | null | 0 | 89 | 2023-02-09T23:32:39 | Entry not found | 15 | [
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IlyaGusev/ru_news | 2023-03-20T23:05:08.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:ru",
"region:us"
] | IlyaGusev | null | null | 3 | 89 | 2023-03-12T20:56:14 | ---
dataset_info:
features:
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num_bytes: 12858731888
num_examples: 4137525
download_size: 3669747077
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sarus-tech/phee | 2023-06-21T19:36:26.000Z | [
"arxiv:2210.12560",
"region:us"
] | sarus-tech | Data and Code for [``PHEE: A Dataset for Pharmacovigilance Event Extraction from Text``](https://arxiv.org/abs/2210.12560/)\ | @misc{sun2022phee,
title={PHEE: A Dataset for Pharmacovigilance Event Extraction from Text},
author={Zhaoyue Sun and Jiazheng Li and Gabriele Pergola and Byron C. Wallace and Bino John and Nigel Greene and Joseph Kim and Yulan He},
year={2022},
eprint={2210.12560},
archivePrefix={arXiv},
... | 1 | 89 | 2023-06-21T14:49:19 | # PHEE dataset
This dataset is port of https://github.com/ZhaoyueSun/PHEE,
the data used in: [``PHEE: A Dataset for Pharmacovigilance Event Extraction from Text``](https://arxiv.org/abs/2210.12560/)
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vuducanh/b3-userstudy-data | 2023-10-23T12:57:27.000Z | [
"license:mit",
"region:us"
] | vuducanh | null | null | 0 | 89 | 2023-09-06T10:41:57 | ---
license: mit
---
dataset sources:
shark_dataset_location = "https://www.kaggle.com/datasets/mysarahmadbhat/shark-attacks"
nba_dataset_location = "https://zenodo.org/record/6419727"
fec_dataset_location = "https://github.com/wesm/pydata-book/blob/2nd-edition/datasets/fec/P00000001-ALL.csv"
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yzhuang/autotree_pmlb_10000_banana_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T01:51:46.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T01:51:42 | ---
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yzhuang/autotree_automl_10000_eye_movements_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T03:32:07.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T03:31:59 | ---
dataset_info:
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yzhuang/autotree_pmlb_10000_spambase_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T03:32:53.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T03:32:48 | ---
dataset_info:
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yzhuang/autotree_automl_10000_california_sgosdt_l256_dim8_d3_sd0 | 2023-09-07T03:44:46.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T03:44:39 | ---
dataset_info:
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yzhuang/autotree_pmlb_10000_phoneme_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T04:06:07.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T04:06:02 | ---
dataset_info:
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yzhuang/autotree_automl_10000_default-of-credit-card-clients_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T04:10:11.000Z | [
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] | yzhuang | null | null | 0 | 89 | 2023-09-07T04:10:04 | ---
dataset_info:
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yzhuang/autotree_pmlb_10000_Hill_Valley_with_noise_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T04:14:27.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T04:14:20 | ---
dataset_info:
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yzhuang/autotree_pmlb_10000_clean2_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T04:54:58.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T04:54:53 | ---
dataset_info:
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yzhuang/autotree_automl_10000_house_16H_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T05:11:36.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T05:11:28 | ---
dataset_info:
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yzhuang/autotree_pmlb_10000_Hill_Valley_without_noise_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T05:25:19.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T05:25:11 | ---
dataset_info:
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yzhuang/autotree_pmlb_10000_magic_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T05:44:01.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T05:43:51 | ---
dataset_info:
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yzhuang/autotree_automl_10000_MagicTelescope_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T05:48:36.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T05:48:29 | ---
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yzhuang/autotree_automl_10000_MiniBooNE_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T06:03:38.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T06:03:29 | ---
dataset_info:
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yzhuang/autotree_automl_10000_jannis_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T06:07:04.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 89 | 2023-09-07T06:06:55 | ---
dataset_info:
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