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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
maritaca-ai/ag_news_pt | 2023-02-16T00:58:33.000Z | [
"region:us"
] | maritaca-ai | AG is a collection of more than 1 million news articles. News articles have been
gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of
activity. ComeToMyHead is an academic news search engine which has been running
since July, 2004. The dataset is provided by the academic comunity for researc... | @inproceedings{Zhang2015CharacterlevelCN,
title={Character-level Convolutional Networks for Text Classification},
author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun},
booktitle={NIPS},
year={2015}
} | 1 | 208 | 2023-01-20T20:23:12 | Entry not found | 15 | [
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0.0379... |
ArmelR/stack-exchange-sample10000 | 2023-04-06T13:19:45.000Z | [
"region:us"
] | ArmelR | null | null | 2 | 208 | 2023-04-06T13:19:39 | ---
dataset_info:
features:
- name: qid
dtype: int64
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num_bytes: 27983797.447734267
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result-kand2-sdxl-wuerst-karlo/3658ecd8 | 2023-10-08T22:30:26.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 208 | 2023-10-08T22:30:26 | ---
dataset_info:
features:
- name: result
dtype: string
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dtype: int64
splits:
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num_bytes: 198
num_examples: 10
download_size: 1383
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "3658ecd... | 455 | [
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distil-whisper/earnings21 | 2023-10-13T10:33:58.000Z | [
"region:us"
] | distil-whisper | null | null | 0 | 208 | 2023-10-13T10:33:24 | ---
dataset_info:
config_name: full
features:
- name: audio
dtype: audio
- name: file_id
dtype: string
- name: audio_length
dtype: string
- name: sample_rate
dtype: string
- name: company_name
dtype: string
- name: financial_quarter
dtype: string
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dtype: strin... | 869 | [
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chrisgru/commonsense-dialogues3 | 2023-10-21T10:32:38.000Z | [
"region:us"
] | chrisgru | null | null | 0 | 208 | 2023-10-21T10:32:31 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 8749177.01151794... | 657 | [
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... |
wili_2018 | 2023-01-25T15:02:28.000Z | [
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ace",
"language:af",
"language:als",
"language:am",
"language:an",
"language:ang",
"langu... | null | It is a benchmark dataset for language identification and contains 235000 paragraphs of 235 languages | @dataset{thoma_martin_2018_841984,
author = {Thoma, Martin},
title = {{WiLI-2018 - Wikipedia Language Identification database}},
month = jan,
year = 2018,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.841984},
url = {https://doi.org/... | 3 | 207 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ace
- af
- als
- am
- an
- ang
- ar
- arz
- as
- ast
- av
- ay
- az
- azb
- ba
- bar
- bcl
- be
- bg
- bho
- bjn
- bn
- bo
- bpy
- br
- bs
- bxr
- ca
- cbk
- cdo
- ce
- ceb
- chr
- ckb
- co
- crh
- cs
- csb
- cv
- cy
- da
- de
- diq
- dsb
... | 10,953 | [
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-0.0400390625,
0.03662109375... |
hf-internal-testing/fixtures_sintel | 2021-12-07T08:13:09.000Z | [
"region:us"
] | hf-internal-testing | \\n | \\n | 0 | 207 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.0379... |
bigbio/n2c2_2018_track2 | 2022-12-22T15:46:01.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | bigbio | The National NLP Clinical Challenges (n2c2), organized in 2018, continued the
legacy of i2b2 (Informatics for Biology and the Bedside), adding 2 new tracks and 2
new sets of data to the shared tasks organized since 2006. Track 2 of 2018
n2c2 shared tasks focused on the extraction of medications, with their signature
in... | @article{DBLP:journals/jamia/HenryBFSU20,
author = {
Sam Henry and
Kevin Buchan and
Michele Filannino and
Amber Stubbs and
Ozlem Uzuner
},
title = {2018 n2c2 shared task on adverse drug events and medication extrac... | 2 | 207 | 2022-11-13T22:10:49 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: DUA
pretty_name: n2c2 2018 ADE
homepage: https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
bigbio_pubmed: False
bigbio_public: False
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION... | 3,557 | [
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... |
csebuetnlp/squad_bn | 2022-08-21T13:17:43.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:bn",
"license:cc-by-nc-sa-4.0",
"arxiv... | csebuetnlp | SQuAD-bn is derived from the SQuAD-2.0 and TyDI-QA datasets. | @misc{bhattacharjee2021banglabert,
title={BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding},
author={Abhik Bhattacharjee and Tahmid Hasan and Kazi Samin and Md Saiful Islam and M. Sohel Rahman and Anindya Iqbal and Rifat Shahriyar},
year={2021},
... | 2 | 206 | 2022-04-11T10:16:26 | ---
annotations_creators:
- machine-generated
language_creators:
- found
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
language:
- bn
license:
- cc-by-nc-sa-4.0
---
# Dataset Card for `squad_bn`
... | 7,775 | [
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maritaca-ai/sst2_pt | 2023-02-10T13:40:00.000Z | [
"region:us"
] | maritaca-ai | The Stanford Sentiment Treebank consists of sentences from movie reviews and
human annotations of their sentiment. The task is to predict the sentiment of a
given sentence. We use the two-way (positive/negative) class split, and use only
sentence-level labels. | @inproceedings{socher2013recursive,
title={Recursive deep models for semantic compositionality over a sentiment treebank},
author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
booktitle={Proceedings of the 2013 conference on ... | 1 | 206 | 2023-01-29T21:29:44 | Entry not found | 15 | [
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0.03790... |
flax-community/german_common_crawl | 2023-10-02T16:46:37.000Z | [
"language:de",
"region:us"
] | flax-community | German Only Extract from Common Crawl
This Dataset is for pretraining a German Language Model (Unsupervised) or tune a Multilingual Model specifically to German | @inproceedings{wenzek2020ccnet,
title={CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data},
author={Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, {\'E}douard},
booktitle={Proceedings of The 12th Lan... | 0 | 205 | 2022-03-02T23:29:22 | ---
language:
- de
---
The dataset script is more or less ready and one file has correctly been converted so far: `https://opendata.iisys.de/systemintegration/Datasets/CommonCrawl/head/de_head_0000_2015-48.tar.gz`
You can try downloading the file as follows:
```python
from datasets import load_dataset
ds = load_datas... | 1,629 | [
[
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0.0336608886... |
hf-internal-testing/fixtures_nlvr2 | 2021-12-23T10:57:49.000Z | [
"region:us"
] | hf-internal-testing | \\n | \\n | 0 | 205 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.03790... |
C-MTEB/OnlineShopping-classification | 2023-07-28T13:15:20.000Z | [
"region:us"
] | C-MTEB | null | null | 1 | 205 | 2023-07-28T13:15:09 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: cat
dtype: string
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1535074.0115334373
num_... | 653 | [
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europa_ecdc_tm | 2022-11-03T16:31:26.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"lang... | null | In October 2012, the European Union (EU) agency 'European Centre for Disease Prevention and Control' (ECDC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-five languages. This resource bears the name EAC Translation Memory, short EAC-TM.
ECDC... | @Article{Steinberger2014,
author={Steinberger, Ralf
and Ebrahim, Mohamed
and Poulis, Alexandros
and Carrasco-Benitez, Manuel
and Schl{\"u}ter, Patrick
and Przybyszewski, Marek
and Gilbro, Signe},
title={An ov... | 1 | 204 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hu
- is
- it
- lt
- lv
- mt
- nl
- 'no'
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
... | 16,499 | [
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0.0... |
mwsc | 2023-04-05T13:33:22.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-coreference-resolution",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|winograd_wsc",
"language:en",
"license:cc-by-4.0",
"... | null | Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
This modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing. | @article{McCann2018decaNLP,
title={The Natural Language Decathlon: Multitask Learning as Question Answering},
author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
journal={arXiv preprint arXiv:1806.08730},
year={2018}
} | 0 | 204 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Modified Winograd Schema Challenge (MWSC)
size_categories:
- n<1K
source_datasets:
- extended|winograd_wsc
task_categories:
- multiple-choice
task_ids:
- mul... | 6,697 | [
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0.01... |
flexthink/librig2p-nostress-space | 2022-06-24T01:23:49.000Z | [
"region:us"
] | flexthink | Grapheme-to-Phoneme training, validation and test sets | null | 0 | 204 | 2022-03-02T23:29:22 | # librig2p-nostress - Grapheme-To-Phoneme Dataset
This dataset contains samples that can be used to train a Grapheme-to-Phoneme system **without** stress information.
The dataset is derived from the following pre-existing datasets:
* [LibriSpeech ASR Corpus](https://www.openslr.org/12)
* [LibriSpeech Alignments](htt... | 463 | [
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-0.02398681640625... |
nlp-thedeep/humset | 2023-05-25T17:14:31.000Z | [
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_categories:token-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_... | nlp-thedeep | HumSet is a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consi... | @misc{https://doi.org/10.48550/arxiv.2210.04573,
doi = {10.48550/ARXIV.2210.04573},
url = {https://arxiv.org/abs/2210.04573},
author = {Fekih, Selim and Tamagnone, Nicolò and Minixhofer, Benjamin and Shrestha, Ranjan and Contla, Ximena and Oglethorpe, Ewan and Rekabsaz, Navid},
keywords = {Computation and Langu... | 1 | 204 | 2023-01-12T16:00:58 | ---
annotations_creators:
- expert-generated
language:
- en
- fr
- es
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: HumSet
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- humanitarian
- research
- analytical-framework
- multilabel
- humset
- hu... | 10,990 | [
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0.0296630... |
fcakyon/pokemon-classification | 2023-01-14T13:06:55.000Z | [
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"Gaming",
"region:us"
] | fcakyon | null | @misc{ pokedex_dataset,
title = { Pokedex Dataset },
type = { Open Source Dataset },
author = { Lance Zhang },
howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
journal = { Roboflow Universe },
... | 1 | 204 | 2023-01-14T12:47:57 | ---
task_categories:
- image-classification
tags:
- roboflow
- roboflow2huggingface
- Gaming
---
<div align="center">
<img width="640" alt="fcakyon/pokemon-classification" src="https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['Golbat', 'Mach... | 3,740 | [
[
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LeoLM/OpenSchnabeltier | 2023-08-27T19:03:20.000Z | [
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] | LeoLM | null | null | 2 | 204 | 2023-08-25T13:06:27 | ---
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jay401521/train | 2023-10-20T09:51:18.000Z | [
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bing_coronavirus_query_set | 2022-11-03T16:30:54.000Z | [
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allenai/cord19 | 2022-11-03T16:31:53.000Z | [
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ai4bharat/IndicXParaphrase | 2022-10-14T16:40:28.000Z | [
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dominguesm/alpaca-data-pt-br | 2023-04-01T12:00:07.000Z | [
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junelee/remon_without_nsfw | 2023-06-04T13:57:20.000Z | [
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verbrannter/invoice_dataset_large_cleaned_2 | 2023-07-16T14:59:01.000Z | [
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hmao/reformatted_multiapi_openai | 2023-10-23T23:42:01.000Z | [
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hsiungc/master_midi_testing | 2023-11-01T21:57:02.000Z | [
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qa_zre | 2023-04-05T13:37:03.000Z | [
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] | null | A dataset reducing relation extraction to simple reading comprehension questions | @inproceedings{levy-etal-2017-zero,
title = "Zero-Shot Relation Extraction via Reading Comprehension",
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Seo, Minjoon and
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booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 201... | 1 | 202 | 2022-03-02T23:29:22 | ---
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ScandEval/norne-nb-mini | 2023-07-05T09:42:22.000Z | [
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tomekkorbak/detoxify-pile-chunk3-0-50000 | 2022-10-06T02:57:39.000Z | [
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ClementRomac/cleaned_deduplicated_oscar | 2023-10-25T14:05:19.000Z | [
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C-MTEB/waimai-classification | 2023-07-28T12:08:38.000Z | [
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C-MTEB/MultilingualSentiment-classification | 2023-07-28T13:29:38.000Z | [
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lighteval/mutual_harness | 2023-08-09T15:50:01.000Z | [
"region:us"
] | lighteval | MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is
modified from Chinese high school English listening comprehension test data. | @inproceedings{mutual,
title = "MuTual: A Dataset for Multi-Turn Dialogue Reasoning",
author = "Cui, Leyang and Wu, Yu and Liu, Shujie and Zhang, Yue and Zhou, Ming" ,
booktitle = "Proceedings of the 58th Conference of the Association for Computational Linguistics",
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LeoLM/HellaSwag_de | 2023-08-29T13:31:56.000Z | [
"region:us"
] | LeoLM | null | null | 0 | 202 | 2023-08-10T13:49:51 | ---
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offenseval_dravidian | 2023-06-01T14:59:49.000Z | [
"task_categories:text-classification",
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"l... | null | Offensive language identification in dravidian lanaguages dataset. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. | @inproceedings{dravidianoffensive-eacl,
title={Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada},
author={Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Jose, Navya and
M, Anand Kumar and
Mandl, Thomas and
Kumaresan, Prasanna Kumar and
Ponnsamy, Rah... | 2 | 201 | 2022-03-02T23:29:22 | ---
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pretty_name: Offenseval Dravidian
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swedish_medical_ner | 2023-01-25T14:45:18.000Z | [
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"license... | null | SwedMedNER is a dataset for training and evaluating Named Entity Recognition systems on medical texts in Swedish.
It is derived from medical articles on the Swedish Wikipedia, Läkartidningen, and 1177 Vårdguiden. | @inproceedings{almgrenpavlovmogren2016bioner,
title={Named Entity Recognition in Swedish Medical Journals with Deep Bidirectional Character-Based LSTMs},
author={Simon Almgren, Sean Pavlov, Olof Mogren},
booktitle={Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (... | 2 | 201 | 2022-03-02T23:29:22 | ---
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bertin-project/mc4-es-sampled | 2023-03-16T08:56:10.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
... | bertin-project | 50 million documents in Spanish extracted from mC4 applying perplexity sampling via mc4-sampling: "https://huggingface.co/datasets/bertin-project/mc4-sampling". Please, refer to BERTIN Project. The original dataset is the Multlingual Colossal, Cleaned version of Common Crawl's web crawl corpus (mC4), based on the Commo... | @article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2... | 2 | 201 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- es
license:
- odc-by
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
- 1M<n<10M
- 10M<n<100M
- 100M<n<1B
source_datasets:
- mc4
- bertin-project/mc4-sampling
task_categories:
- text-generation
- fill-mask
task_ids:
- language-m... | 7,135 | [
[
-0.043609619140625,
-0.02984619140625,
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-0.05828857421875,
-0.029327392578125,
... |
M-CLIP/ImageCaptions-7M-Embeddings | 2022-05-17T23:34:13.000Z | [
"region:us"
] | M-CLIP | null | null | 0 | 201 | 2022-05-17T18:19:45 | Entry not found | 15 | [
[
-0.02142333984375,
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-0.060394287109375,
0.0379... |
ScandEval/scandiqa-no-mini | 2023-07-05T09:44:15.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:nb",
"language:no",
"language:nn",
"license:cc-by-3.0",
"region:us"
] | ScandEval | null | null | 0 | 201 | 2022-12-05T16:42:53 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: context
dtype: string
- name: answers_en
struct:
- name: answer_start
seque... | 974 | [
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0.0758056640625,
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-0.07086181640625,
-0.04888916015625,
-0.040496826171875,
... |
medalpaca/medical_meadow_health_advice | 2023-04-06T16:51:22.000Z | [
"task_categories:question-answering",
"task_categories:text-classification",
"language:en",
"region:us"
] | medalpaca | null | null | 3 | 201 | 2023-04-06T16:47:45 | ---
task_categories:
- question-answering
- text-classification
language:
- en
---
# Health Advice
## Dataset Description
- **Paper:** https://experts.syr.edu/en/publications/detecting-causal-language-use-in-science-findings
### Dataset Summary
This is the dataset use in the paper: Detecting Causal Language Use in... | 1,042 | [
[
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0.030... |
heegyu/korquad-chat-v1 | 2023-05-06T09:12:14.000Z | [
"license:mit",
"region:us"
] | heegyu | null | null | 7 | 201 | 2023-05-06T09:05:56 | ---
license: mit
---
- 총 9619개의 한국어 지식기반 대화 데이터셋입니다.
- KorQuAD 1.0 데이터에 있는 문서를 ChatGPT에게 준 뒤 생성했습니다.
서로를 호칭하는 부분은 아래처럼 처리되어있습니다.
```
안녕하세요 <|bot|>. 요즘 어떻게 지내시나요?\n<bot> 안녕하세요 <|user|>.
```
데이터 샘플
```
{
"source": "korquad-chat",
"text": "
<sys>유전자의 이름은 인도의 수도 뉴델리의 이름을 따 붙여졌는데, 이는 2009년 용 (Yong) 등이 ... | 1,911 | [
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0.0256500244140625,
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-0.0266876220703125,
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-0.... |
result-kand2-sdxl-wuerst-karlo/a19a65d2 | 2023-10-09T05:20:58.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 201 | 2023-10-09T05:20:57 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 174
num_examples: 10
download_size: 1323
dataset_size: 174
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a19a65d... | 455 | [
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-0.040191650390625,
-0.0439453125,
-0.0177... |
bianet | 2023-06-01T14:59:50.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:ku",
"language:tr",
"license:unknown",
"region:us"
] | null | A parallel news corpus in Turkish, Kurdish and English.
Bianet collects 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper.
3 languages, 3 bitexts
total number of files: 6
total number of tokens: 2.25M
total number of sentence fragments: 0.14M | @InProceedings{ATAMAN18.6,
author = {Duygu Ataman},
title = {Bianet: A Parallel News Corpus in Turkish, Kurdish and English},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {may},
date = {7-12},
location = {Miyaza... | 0 | 200 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- ku
- tr
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: bianet
pretty_name: Bianet
dataset_info:
- config_na... | 4,726 | [
[
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0.02441... |
omp | 2023-01-25T14:42:05.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | The “One Million Posts” corpus is an annotated data set consisting of
user comments posted to an Austrian newspaper website (in German language).
DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website,
there is a discussion section below each news article where readers engage in
online disc... | @InProceedings{Schabus2017,
Author = {Dietmar Schabus and Marcin Skowron and Martin Trapp},
Title = {One Million Posts: A Data Set of German Online Discussions},
Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},
Pages ... | 1 | 200 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- de
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: one-million-pos... | 12,762 | [
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0.0253448486... |
mhhmm/leetcode-solutions-python | 2023-04-27T06:40:41.000Z | [
"license:lgpl",
"region:us"
] | mhhmm | null | null | 14 | 200 | 2023-04-25T10:48:36 | ---
license: lgpl
---
All credits belong to https://www.kaggle.com/datasets/erichartford/leetcode-solutions
I collected only python solutions:
```
id: <number>
code_with_data:
<
# Slug
# Title
# Difficulty
# Content
Code Answer in Python
# Explanation
>
code_only: < Code Answer in Python >
code_with_probl... | 488 | [
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0.04254150390625,
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-0.04522705078125,
-0.01467132568359375... |
DFKI-SLT/conll2012_ontonotesv5 | 2023-07-13T15:02:27.000Z | [
"region:us"
] | DFKI-SLT | OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
multilingual corpus manually annotated with syntactic, semantic and discourse information.
This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task.
It includes v4 train/dev and v9 test d... | @inproceedings{pradhan-etal-2013-towards,
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
author = {Pradhan, Sameer and
Moschitti, Alessandro and
Xue, Nianwen and
Ng, Hwee Tou and
Bj{\"o}rkelund, Anders and
Uryupina, Olga and
Zhang, Yuchen and
Z... | 0 | 200 | 2023-07-13T15:02:11 | Entry not found | 15 | [
[
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0.01702880859375,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.03790... |
C-MTEB/OCNLI | 2023-07-28T07:10:28.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 200 | 2023-07-28T07:10:23 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
dataset_info:
features:
- name: sent1
sequence: string
- name: sent2
sequence: string
- name: labels
sequence: int64
splits:
- name: validation
num_bytes: 222873
num_examples: 1
download... | 520 | [
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-0.02023315429687... |
DykeF/NCTCRCHE100K | 2023-10-04T19:37:15.000Z | [
"license:cc-by-4.0",
"region:us"
] | DykeF | This is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue.
All images are 224x224 pixels (px) at 0.5 microns per pixel (MPP). All images are color-normalized using Macenko's method (http://ieeexplore.ieee.org/abst... | Kather, Jakob Nikolas, Halama, Niels, & Marx, Alexander. (2018). 100,000 histological images of human colorectal cancer and healthy tissue (v0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1214456 | 0 | 200 | 2023-10-03T07:42:24 | ---
license: cc-by-4.0
---
# NCTCRCHE100K Dataset Card
# Citation
```bash
Kather, Jakob Nikolas, Halama, Niels, & Marx, Alexander. (2018). 100,000 histological images of human colorectal cancer and healthy tissue (v0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1214456
```
# Description
This is a set of 100,... | 1,986 | [
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0.060028076171875,
-0.039581298828125,
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-0.02655029296875,... |
hybrid_qa | 2023-03-28T12:23:49.000Z | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"multihop-tabular-text-qa",
"arxiv:1909.05358",
"region:us"
] | null | Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present Hyb... | @article{chen2020hybridqa,
title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data},
author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William},
journal={Findings of EMNLP 2020},
year={2020}
} | 1 | 199 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: hybridqa
pretty_name: HybridQA
tags:
- multihop-ta... | 6,912 | [
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0.028472900390625,
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-0.05908203125,
-0.0214080810546875,
0.0263... |
imagenet_sketch | 2023-04-05T13:45:57.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|imagenet-1k",
"language:en",
"license:unknown",
"arxiv:... | null | ImageNet-Sketch data set consists of 50000 images, 50 images for each of the 1000 ImageNet classes.
We construct the data set with Google Image queries "sketch of __", where __ is the standard class name.
We only search within the "black and white" color scheme. We initially query 100 images for every class,
and then m... | @inproceedings{wang2019learning,
title={Learning Robust Global Representations by Penalizing Local Predictive Power},
author={Wang, Haohan and Ge, Songwei and Lipton, Zachary and Xing, Eric P},
booktitle={Advances in Neural Information Processing Systems},
pages={10506--10518},
y... | 5 | 199 | 2022-05-20T14:13:58 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|imagenet-1k
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: imagen... | 78,725 | [
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... |
carlosdanielhernandezmena/ravnursson_asr | 2023-07-10T21:20:03.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fo",
"license:cc-by-4.0",
"faroe islands",
"faroese",
"ravnur project"... | carlosdanielhernandezmena | The corpus \"RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS\" (or RAVNURSSON Corpus for short) is a collection of speech recordings with transcriptions intended for Automatic Speech Recognition (ASR) applications in the language that is spoken at the Faroe Islands (Faroese). It was curated at the Reykjavík University (RU) i... | @misc{carlosmenaravnursson2022,
title={Ravnursson Faroese Speech and Transcripts},
author={Hernandez Mena, Carlos Daniel and Simonsen, Annika},
year={2022},
url={http://hdl.handle.net/20.500.12537/276},
} | 1 | 199 | 2022-11-19T00:02:04 | ---
annotations_creators:
- expert-generated
language:
- fo
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- faroe islands
- faroese
- ravnur project
- speech... | 12,371 | [
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Abzu/dolly_hhrlhf | 2023-06-04T19:33:11.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | Abzu | null | null | 3 | 199 | 2023-05-25T08:34:30 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 22346337.075312525
num_examples: 35205
- name: test
num_bytes: 2483137.924687476
num_examples: 3912
download_size: 16025539
dataset_size: 24829475
license: cc-... | 663 | [
[
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0.07208251953125,
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-0.05731201171875,
-0.059539794921875,
0.002... |
axiong/pmc_llama_instructions | 2023-09-01T04:52:44.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:openrail",
"biology",
"med",
"region:us"
] | axiong | null | null | 10 | 199 | 2023-09-01T00:56:32 | ---
license: openrail
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- biology
- med
---
This repo provides part of the dataset used for PMC-LLaMA-13B's instruction tuning.
| Data | Size | Link |
| --- | --- | --- |
| ChatDoctor | 100K | https://www.yunxiangli.top/ChatDoctor/ |
| MedQA |... | 921 | [
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tmu_gfm_dataset | 2022-11-03T16:30:48.000Z | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"grammatical-error-correction",
"region:us"
] | null | A dataset for GEC metrics with manual evaluations of grammaticality, fluency, and meaning preservation for system outputs. More detail about the creation of the dataset can be found in Yoshimura et al. (2020). | @inproceedings{yoshimura-etal-2020-reference,
title = "{SOME}: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction",
author = "Yoshimura, Ryoma and
Kaneko, Masahiro and
Kajiwara, Tomoyuki and
Komachi, Mamoru",
booktitle = "Proceedings of the 28th ... | 2 | 198 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: null
pretty_name: TMU-GFM-Dataset
tags:
- gramm... | 8,660 | [
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ScandEval/norne-nn-mini | 2023-07-05T09:41:26.000Z | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:nn",
"license:other",
"region:us"
] | ScandEval | null | null | 0 | 198 | 2022-06-14T18:21:22 | ---
dataset_info:
features:
- name: text
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 341534
num_examples: 1024
- name: test
num_bytes: 721476
num_examples: 2048
- name: val
num_bytes: 90956
num_example... | 644 | [
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fahamu/ioi | 2022-11-28T03:58:50.000Z | [
"license:mit",
"doi:10.57967/hf/0142",
"region:us"
] | fahamu | null | null | 2 | 198 | 2022-11-28T01:10:59 | ---
license: mit
---
# Dataset Release: Indirect Object Identification
`mecha_ioi` is a pair of datasets tailored for the Indirect Object Identification task, where sentences are generated from the following set of templates:
- BABA
```
baba_templates = [
"Then, {B} and {A} went to the {PLACE}. {B} gave a {OBJEC... | 3,450 | [
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... |
animelover/danbooru2022 | 2023-07-13T05:49:37.000Z | [
"task_categories:text-to-image",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"doi:10.57967/hf/0425",
"region:us"
] | animelover | null | null | 103 | 198 | 2023-01-28T03:34:32 | ---
license: cc0-1.0
task_categories:
- text-to-image
language:
- en
pretty_name: Danbooru 2022
size_categories:
- 1M<n<10M
---
Collect images from [danbooru website](https://danbooru.donmai.us/).
Post id range: 6019085 - 1019085
About 4M+ images.
All images with the shortest edge greater than 768 are scaled to the... | 654 | [
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azhx/counterfact | 2023-04-07T21:22:57.000Z | [
"region:us"
] | azhx | null | null | 0 | 198 | 2023-04-07T21:18:02 | ---
dataset_info:
features:
- name: case_id
dtype: int64
- name: pararel_idx
dtype: int64
- name: requested_rewrite
struct:
- name: prompt
dtype: string
- name: relation_id
dtype: string
- name: subject
dtype: string
- name: target_new
struct:
- name: id... | 1,119 | [
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eraser_multi_rc | 2023-04-05T10:05:21.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | Eraser Multi RC is a dataset for queries over multi-line passages, along with
answers and a rationalte. Each example in this dataset has the following 5 parts
1. A Mutli-line Passage
2. A Query about the passage
3. An Answer to the query
4. A Classification as to whether the answer is right or wrong
5. An Explanation j... | @unpublished{eraser2019,
title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
}
@inproceedings{MultiRC2018,
author = {Daniel Khashabi and Snigdha Chaturve... | 3 | 197 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
pretty_name: Eraser MultiRC (Multi-Sentence Reading Comprehension... | 13,019 | [
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0.01257324218... |
ARTeLab/fanpage | 2022-11-17T02:49:54.000Z | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"source_datasets:original",
"language:it",
"license:unknown",
"region:us"
] | ARTeLab | null | null | 3 | 197 | 2022-03-02T23:29:22 | ---
language:
- it
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
source_datasets:
- original
task_categories:
- summarization
---
# Dataset Card for fanpage
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks... | 4,171 | [
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0.028167724609... |
ScandEval/scandiqa-sv-mini | 2023-07-05T09:43:55.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:sv",
"license:cc-by-3.0",
"region:us"
] | ScandEval | null | null | 0 | 197 | 2022-12-05T16:43:54 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: context
dtype: string
- name: answers_en
struct:
- name: answer_start
seque... | 961 | [
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fscheffczyk/2D_20newsgroups_embeddings | 2023-02-05T18:57:29.000Z | [
"task_categories:feature-extraction",
"task_categories:sentence-similarity",
"task_categories:question-answering",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|fscheffczyk/20newsgroups_embeddings",
"language:en",
"news",
"20newsgroups",
"region:us"
] | fscheffczyk | null | null | 0 | 197 | 2023-02-05T18:52:06 | ---
annotations_creators: []
language:
- en
language_creators: []
license: []
multilinguality:
- monolingual
pretty_name: Dimensional reduced feature vector embeddings of the 20newsgroup dataset
size_categories:
- unknown
source_datasets:
- extended|fscheffczyk/20newsgroups_embeddings
tags:
- news
- 20newsgroups
task_c... | 3,524 | [
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EleutherAI/pile-duped-pythia-random-sampled | 2023-08-25T08:07:30.000Z | [
"region:us"
] | EleutherAI | null | null | 1 | 197 | 2023-03-27T08:03:38 | ---
dataset_info:
features:
- name: Index
dtype: int64
- name: 70M
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dtyp... | 691 | [
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osunlp/ConflictQA | 2023-06-15T18:45:52.000Z | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"arxiv:2305.13300",
"region:us"
] | osunlp | data for ConflictQA. | @article{xie2023adaptive,
title={Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Conflicts},
author={Xie, Jian and Zhang, Kai and Chen, Jiangjie and Lou, Renze and Su, Yu},
journal={arXiv preprint arXiv:2305.13300},
year={2023}
} | 5 | 197 | 2023-06-03T13:09:23 | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
pretty_name: conflictQA
size_categories:
- 10K<n<100K
---
# Dataset Card for ConflcitQA
## Dataset Description
- **Repository:** https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict
- **Paper:** https://arxiv.org/abs/2305.13300
- **Point o... | 2,542 | [
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narad/ravdess | 2022-11-02T03:21:19.000Z | [
"task_categories:audio-classification",
"task_ids:audio-emotion-recognition",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | narad | \ | \ | 4 | 196 | 2022-08-18T14:54:03 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- audio-emotion-recognition
---
# Dataset Card for RAVDESS
## Table of... | 5,992 | [
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stvhuang/cc100 | 2023-10-30T03:00:20.000Z | [
"region:us"
] | stvhuang | null | null | 1 | 196 | 2023-10-30T02:25:40 | Entry not found | 15 | [
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turkish_ner | 2023-01-25T14:54:39.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:tr",
"license:cc-by-4.0",
"arxiv:1702.02363... | null | Turkish Wikipedia Named-Entity Recognition and Text Categorization
(TWNERTC) dataset is a collection of automatically categorized and annotated
sentences obtained from Wikipedia. The authors constructed large-scale
gazetteers by using a graph crawler algorithm to extract
relevant entity and domain information
from a se... | @InProceedings@article{DBLP:journals/corr/SahinTYES17,
author = {H. Bahadir Sahin and
Caglar Tirkaz and
Eray Yildiz and
Mustafa Tolga Eren and
Omer Ozan Sonmez},
title = {Automatically Annotated Turkish Corpus for Named Entity Recognition
... | 5 | 195 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- tr
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: TurkishNer
dataset_inf... | 5,069 | [
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0.031... |
SkelterLabsInc/JaQuAD | 2022-10-25T09:06:40.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ja",
"license:cc-by-sa-3.0",
"arxiv... | SkelterLabsInc | null | null | 6 | 195 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- ja
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: "JaQuAD: Japanese Question Answering Dataset"
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- questio... | 6,586 | [
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rahular/itihasa | 2022-10-24T18:06:01.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:original",
"language:sa",
"language:en",
"license:apache-2.0",
"conditional-text-generation",
"region:... | rahular | A Sanskrit-English machine translation dataset. | @inproceedings{aralikatte-etal-2021-itihasa,
title = "Itihasa: A large-scale corpus for {S}anskrit to {E}nglish translation",
author = "Aralikatte, Rahul and
de Lhoneux, Miryam and
Kunchukuttan, Anoop and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 8th Workshop on Asian Transla... | 4 | 195 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- sa
- en
license:
- apache-2.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: Itihasa
metrics:
- bleu
- sacrebleu
- rouge... | 2,869 | [
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... |
medalpaca/medical_meadow_cord19 | 2023-04-06T16:47:03.000Z | [
"task_categories:summarization",
"size_categories:100K<n<1M",
"language:en",
"region:us"
] | medalpaca | null | null | 3 | 195 | 2023-04-06T16:24:06 | ---
task_categories:
- summarization
language:
- en
size_categories:
- 100K<n<1M
---
# CORD 19
## Dataset Description
- **Homepage:** https://www.kaggle.com/datasets/allen-institute-for-ai/CORD-19-research-challenge
### Dataset Summary
In response to the COVID-19 pandemic, the White House and a coalition of leadin... | 2,010 | [
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0.005... |
KoddaDuck/dataset_backup | 2023-04-15T03:04:47.000Z | [
"region:us"
] | KoddaDuck | null | null | 0 | 195 | 2023-04-15T02:55:22 | Entry not found | 15 | [
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eloukas/edgar-corpus | 2023-07-14T07:17:12.000Z | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:monolingual",
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"language:en",
"license:apache-2.0",
"research papers",
"edgar",
"sec",
"finance",
"financial",
"filings",... | eloukas | The dataset contains annual filings (10K) of all publicly traded firms from 1993-2020. The table data is stripped but all text is retained.
This dataset allows easy access to the EDGAR-CORPUS dataset based on the paper EDGAR-CORPUS: Billions of Tokens Make The World Go Round (See References in README.md for details). | null | 16 | 194 | 2022-12-30T16:55:08 | ---
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christykoh/imdb_pt | 2023-04-05T16:28:11.000Z | [
"region:us"
] | christykoh | null | null | 0 | 194 | 2023-04-05T16:27:52 | ---
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owkin/camelyon16-features | 2023-10-30T11:20:51.000Z | [
"task_categories:feature-extraction",
"task_categories:image-classification",
"size_categories:n<1K",
"language:en",
"license:other",
"biology",
"medical",
"cancer",
"region:us"
] | owkin | null | null | 0 | 194 | 2023-09-29T15:26:47 | ---
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kkboy1/LeAudio | 2023-10-09T06:38:08.000Z | [
"task_categories:text2text-generation",
"region:us"
] | kkboy1 | null | null | 0 | 194 | 2023-10-04T07:38:02 | ---
annotations_creators: []
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pretty_name: LE AUDIO BOOK
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erhwenkuo/c4-chinese-zhtw | 2023-10-12T04:00:07.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:1M<n<10M",
"language:zh",
"region:us"
] | erhwenkuo | null | null | 3 | 194 | 2023-10-11T13:39:56 | ---
language:
- zh
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distil-whisper/rev16 | 2023-10-17T17:15:02.000Z | [
"region:us"
] | distil-whisper | null | null | 0 | 194 | 2023-10-13T16:09:08 | ---
dataset_info:
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code_x_glue_cc_defect_detection | 2022-11-18T19:31:11.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
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"multilinguality:other-programming-languages",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"license:c-uda",
"region:us"
] | null | Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
The dataset we use comes from the... | @inproceedings{zhou2019devign,
title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
booktitle={Advances in Neural Information Processing Systems},
pages={101... | 6 | 193 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
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- code
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- c-uda
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- other-programming-languages
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task_categories:
- text-classification
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pretty_name: CodeXGlueCcDefectDetection
da... | 5,426 | [
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crd3 | 2022-11-18T19:47:20.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
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"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
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"language:... | null | Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.
Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.
The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398... | @inproceedings{
title = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},
author = {Rameshkumar, Revanth and Bailey, Peter},
year = {2020},
publisher = {Association for Computational Linguistics},
conference = {ACL}
} | 12 | 193 | 2022-03-02T23:29:22 | ---
pretty_name: CRD3 (Critical Role Dungeons and Dragons Dataset)
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
task_ids:
- dialogue... | 7,812 | [
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librispeech_lm | 2023-04-05T10:09:21.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
] | null | Language modeling resources to be used in conjunction with the LibriSpeech ASR corpus. | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--... | 0 | 193 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: LibrispeechLm
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: null
dataset_info... | 5,696 | [
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Zaid/quac_expanded | 2021-10-04T19:41:30.000Z | [
"region:us"
] | Zaid | \\nQuestion Answering in Context is a dataset for modeling, understanding,
and participating in information seeking dialog. Data instances consist
of an interactive dialog between two crowd workers: (1) a student who
poses a sequence of freeform questions to learn as much as possible
about a hidden Wikipedia text, and ... | \\n@inproceedings{choi-etal-2018-quac,
title = "QUAC: Question answering in context",
abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform... | 0 | 193 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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mozilla-foundation/common_voice_12_0 | 2023-06-26T15:23:50.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | mozilla-foundation | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Lang... | 11 | 193 | 2023-03-12T17:28:02 | ---
pretty_name: Common Voice Corpus 12.0
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language_bcp47:
- ab
- ar
- as
- ast
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy-NL
- ga-IE
- gl
- gn
- ha
- hi
- hsb
... | 14,535 | [
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climatebert/climate_detection | 2023-04-18T14:39:49.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | climatebert | null | null | 2 | 193 | 2023-04-11T13:06:20 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: ClimateTalkDetection
dataset_info:
features:
- name:... | 4,345 | [
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wenhu/TheoremQA | 2023-07-15T17:54:40.000Z | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"license:mit",
"question answering",
"math",
"science",
"visual question answering",
"arxiv:2305.12524",
"region:us"
] | wenhu | null | null | 10 | 193 | 2023-05-24T02:57:57 | ---
license: mit
task_categories:
- question-answering
language:
- en
tags:
- question answering
- math
- science
- visual question answering
pretty_name: ThoeremQA
size_categories:
- n<1K
---
## Introduction
We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 35... | 1,126 | [
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id_nergrit_corpus | 2023-01-25T14:32:40.000Z | [
"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:id",
"license:other",
"region:us"
] | null | Nergrit Corpus is a dataset collection for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment
Analysis. id_nergrit_corpus is the Named Entity Recognition of this dataset collection which contains 18 entities as
follow:
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': Facility
'G... | @inproceedings{id_nergrit_corpus,
author = {Gria Inovasi Teknologi},
title = {NERGRIT CORPUS},
year = {2019},
url = {https://github.com/grit-id/nergrit-corpus},
} | 2 | 192 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- id
license:
- other
multilinguality:
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size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: nergrit-corpus
prett... | 7,411 | [
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scb_mt_enth_2020 | 2022-11-18T21:43:37.000Z | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:found",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language_creators:found",
"language_creators:machine-generated",
"multilingualit... | null | scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
The primary objective of our work is to build a large-scale English-Thai dataset for machine translation.
We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources,
namely news, Wikipedia articles, SMS... | @article{lowphansirikul2020scb,
title={scb-mt-en-th-2020: A Large English-Thai Parallel Corpus},
author={Lowphansirikul, Lalita and Polpanumas, Charin and Rutherford, Attapol T and Nutanong, Sarana},
journal={arXiv preprint arXiv:2007.03541},
year={2020}
} | 2 | 192 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
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- found
- machine-generated
language_creators:
- expert-generated
- found
- machine-generated
language:
- en
- th
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
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task... | 12,773 | [
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chintagunta85/bc4chemd | 2022-07-27T12:42:13.000Z | [
"region:us"
] | chintagunta85 | The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robu... | @article{Krallinger2015TheCC,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Martin Krallinger and Obdulia Rabal and Florian Leitner and Miguel Vazquez and David Salgado and Zhiyong Lu and Robert Leaman and Yanan Lu and Dong-Hong Ji and Daniel M. Lowe and Roger A. Sayle an... | 1 | 192 | 2022-07-27T12:40:06 | Entry not found | 15 | [
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masakhane/mafand | 2023-09-11T18:01:53.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:fr",
"language:am",
"language:bm",
"lang... | masakhane | MAFAND-MT is the largest MT benchmark for African languages in the news domain, covering 21 languages. The languages covered are:
- Amharic
- Bambara
- Ghomala
- Ewe
- Fon
- Hausa
- Igbo
- Kinyarwanda
- Luganda
- Luo
- Mossi
- Nigerian-Pidgin
- Chichewa
- Shona
- Swahili
- Setswana
- Twi
- Wolof
- Xhosa
- Yoruba
- Zulu... | @inproceedings{adelani-etal-2022-thousand,
title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation",
author = "Adelani, David and
Alabi, Jesujoba and
Fan, Angela and
Kreutzer, Julia and
Shen, Xiaoyu and
Reid, Machel and... | 6 | 192 | 2022-08-22T09:29:01 | ---
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pretty_name: mafand
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CVdatasets/ImageNet15_animals_unbalanced_aug1 | 2023-02-28T18:48:02.000Z | [
"region:us"
] | CVdatasets | null | null | 0 | 192 | 2023-02-28T18:47:49 | ---
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center-for-humans-and-machines/style-diffusion | 2023-06-30T17:45:02.000Z | [
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] | center-for-humans-and-machines | null | null | 0 | 192 | 2023-05-16T11:27:45 | ---
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---
# Dataset Card for "style-diffusion"
[More Informatio... | 435 | [
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pie/conll2012_ontonotesv5 | 2023-09-28T18:14:28.000Z | [
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C-MTEB/ATEC | 2023-07-28T13:53:38.000Z | [
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crawl_domain | 2022-11-18T19:47:14.000Z | [
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"source_datasets:original",... | null | Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries (e.g. "commoncrawl" to "common crawl"). Breaking domain names such as "openresearch" into component words "open" and "research" is important for applications such as Text-to-Speech synthesis and web search. Common Crawl is an... | @inproceedings{zrs2020urlsegmentation,
title={Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities},
author={Hao Zhang and Jae Ro and Richard William Sproat},
booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
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... | 0 | 191 | 2022-03-02T23:29:22 | ---
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sent_comp | 2022-11-18T21:45:18.000Z | [
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] | null | Large corpus of uncompressed and compressed sentences from news articles. | @inproceedings{filippova-altun-2013-overcoming,
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Altun, Yasemin",
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year = "20... | 1 | 191 | 2022-03-02T23:29:22 | ---
annotations_creators:
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fscheffczyk/20newsgroups_embeddings | 2023-02-05T17:59:34.000Z | [
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annotations_creators: []
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PKU-Alignment/BeaverTails | 2023-10-17T11:47:53.000Z | [
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] | PKU-Alignment | null | null | 11 | 191 | 2023-06-07T17:22:12 | ---
license: cc-by-nc-4.0
task_categories:
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size_categories:
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path: round0/330k/train.jsonl.x... | 6,951 | [
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emozilla/pg19 | 2023-10-09T15:06:39.000Z | [
"region:us"
] | emozilla | null | null | 3 | 191 | 2023-08-08T13:11:36 | ---
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features:
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result-kand2-sdxl-wuerst-karlo/9208a1cc | 2023-10-09T14:11:16.000Z | [
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] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 191 | 2023-10-09T14:11:16 | ---
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# Dataset Card for "9208a1c... | 455 | [
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