author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1
class | downloads float64 1 1M ⌀ | gated bool 2
classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2
classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ydshieh | null | @article{DBLP:journals/corr/LinMBHPRDZ14,
author = {Tsung{-}Yi Lin and
Michael Maire and
Serge J. Belongie and
Lubomir D. Bourdev and
Ross B. Girshick and
James Hays and
Pietro Perona and
Deva Ramanan and
... | COCO is a large-scale object detection, segmentation, and captioning dataset. | false | 937 | false | ydshieh/coco_dataset_script | 2022-02-14T17:32:43.000Z | null | false | 6414bae7a39b5f41feab2fd6a1cb773033254c93 | [] | [] | https://huggingface.co/datasets/ydshieh/coco_dataset_script/resolve/main/README.md | ## Usage
For testing purpose, you can use the hosted dummy dataset (`dummy_data`) as follows:
```
import datasets
ds = datasets.load_dataset("ydshieh/coco_dataset_script", "2017", data_dir="./dummy_data/")
```
For using the COCO dataset (2017), you need to download it manually first:
```
wget http://images.cocodatas... |
yharyarias | null | null | null | false | 1 | false | yharyarias/tirads_tiroides | 2022-01-24T01:53:21.000Z | null | false | 3673fb0d96829eb005d6d0816ed0be21bbac249f | [] | [] | https://huggingface.co/datasets/yharyarias/tirads_tiroides/resolve/main/README.md | Thyroid ultrasound images, classified into 5 classes that correspond to the European EU-TIRADS scale, this consists of:
EU-TIRADS 1: no nodule
EU-TIRADS 2: benign
EU-TIRADS 3: low risk (oval, smooth margin, iso / hyperechoic, no high risk features)
EU-TIRADS 4: intermediate risk (oval, smooth margin, mildly hypoechoic... |
yhavinga | null | @article{JMLR:v21:20-074,
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 = {Journal of Machine Learn... | A thoroughly cleaned version of the Dutch portion of the multilingual
colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning
detailed in the reposi... | false | 255 | false | yhavinga/mc4_nl_cleaned | 2022-10-25T07:28:22.000Z | mc4 | false | 8e6113cc20fe8ef7c4bc02a2b166fbb88f536a69 | [] | [
"arxiv:1910.10683",
"annotations_creators:no-annotation",
"language_creators:found",
"language:nl",
"language:en",
"license:odc-by",
"multilinguality:monolingual",
"multilinguality:en-nl",
"size_categories:120k",
"size_categories:1M<n<10M",
"size_categories:10M<n<100M",
"size_categories:100M<n... | https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- nl
- en
license:
- odc-by
multilinguality:
- monolingual
- en-nl
size_categories:
micro:
- 120k
tiny:
- 1M<n<10M
small:
- 10M<n<100M
medium:
- 10M<n<100M
large:
- 10M<n<100M
full:
- 100M<n<1B
source_datasets:
- exte... |
yluisfern | null | null | null | false | 1 | false | yluisfern/PBU | 2021-04-02T16:39:30.000Z | null | false | 9111d6987c89a76a1a640bfc661ccdb712e9e4cd | [] | [] | https://huggingface.co/datasets/yluisfern/PBU/resolve/main/README.md | https://www.geogebra.org/m/cwcveget
https://www.geogebra.org/m/b8dzxk6z
https://www.geogebra.org/m/nqanttum
https://www.geogebra.org/m/pd3g8a4u
https://www.geogebra.org/m/jw8324jz
https://www.geogebra.org/m/wjbpvz5q
https://www.geogebra.org/m/qm3g3ma6
https://www.geogebra.org/m/sdajgph8
https://www.geogebra.org/m/e3ghh... |
yonesuke | null | null | null | false | 1 | false | yonesuke/Ising2D | 2022-01-18T11:50:23.000Z | null | false | 06ee53dad2bab38ab0c45f13cd6d3c1c85d640ee | [] | [] | https://huggingface.co/datasets/yonesuke/Ising2D/resolve/main/README.md | - hoge
- fuga |
yonesuke | null | null | null | false | 1 | false | yonesuke/Vicsek | 2022-02-17T05:34:34.000Z | null | false | e5a3648ec4ec400d298640b5ee252ee82dc5eebe | [] | [
"license:mit"
] | https://huggingface.co/datasets/yonesuke/Vicsek/resolve/main/README.md | ---
license: mit
---
|
ysharma | null | null | null | false | 1 | false | ysharma/rickandmorty | 2022-01-02T00:45:54.000Z | null | false | 3368ab40c719d3fc556a2d11b8c1d32fac9278be | [] | [] | https://huggingface.co/datasets/ysharma/rickandmorty/resolve/main/README.md | This dataset contains scripts for all episodes of Rick and Morty season 1,2, and 3.
Columns : index, season no., episode no., episode name, (character) name, line (dialogue) |
yuanchuan | null | @techreport{kee2021,
author = {Yuan Chuan Kee},
title = {Synthesis of a large dataset of annotated reference strings for developing citation parsers},
institution = {National University of Singapore},
year = {2021}
} | A repository of reference strings annotated using CSL processor using citations obtained from various sources. | false | 1 | false | yuanchuan/annotated_reference_strings | 2022-10-26T14:53:23.000Z | null | false | 86de7d45936fe0885b6783dff6bdd6e6eca8eff0 | [] | [
"annotations_creators:other",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:parsing"
] | https://huggingface.co/datasets/yuanchuan/annotated_reference_strings/resolve/main/README.md | ---
annotations_creators:
- other
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- parsing
pretty_name: Annotated Reference Strings
---
# Dataset Card for annotated... |
z-uo | null | null | null | false | 1 | false | z-uo/female-LJSpeech-italian | 2022-10-23T04:56:44.000Z | null | false | 14ab48911e45af72b8aec9f6eda9906694c3f094 | [] | [
"task_ids:tts",
"language:it",
"multilinguality:monolingual"
] | https://huggingface.co/datasets/z-uo/female-LJSpeech-italian/resolve/main/README.md | ---
task_ids:
- tts
language:
- it
task_categories:
- tts
multilinguality:
- monolingual
---
# Italian Male Voice
This dataset is an Italian version of [LJSpeech](https://keithito.com/LJ-Speech-Dataset/), that merge all female audio of the same speaker finded into [M-AILABS Speech Dataset](https://www.caito.de/2019... |
z-uo | null | null | null | false | 1 | false | z-uo/male-LJSpeech-italian | 2022-10-23T04:57:26.000Z | null | false | ac9f1f8c8831eb367b460ff1c87b991ad1996519 | [] | [
"task_ids:tts",
"language:it",
"multilinguality:monolingual"
] | https://huggingface.co/datasets/z-uo/male-LJSpeech-italian/resolve/main/README.md | ---
task_ids:
- tts
language:
- it
task_categories:
- tts
multilinguality:
- monolingual
---
# Italian Male Voice
This dataset is an Italian version of [LJSpeech](https://keithito.com/LJ-Speech-Dataset/), that merge all male audio of the same speaker finded into [M-AILABS Speech Dataset](https://www.caito.de/2019/... |
z-uo | null | null | null | false | 2 | false | z-uo/squad-it | 2022-10-25T10:01:57.000Z | null | false | d73d22a877588114280072b6639292f9c3a99e5b | [] | [
"language:it",
"multilinguality:monolingual",
"size_categories:8k<n<10k",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/z-uo/squad-it/resolve/main/README.md | ---
language:
- it
multilinguality:
- monolingual
size_categories:
- 8k<n<10k
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Squad-it
This dataset is an adapted version of that [squad-it](https://github.com/crux82/squad-it) to train on HuggingFace models.
It contains:
- train samples: 87599
- t... |
zhoujun | null | null | null | false | 1 | false | zhoujun/hitab | 2022-02-08T08:35:57.000Z | null | false | beefaac934f54882041d2840222dbd0b7f48ea34 | [] | [] | https://huggingface.co/datasets/zhoujun/hitab/resolve/main/README.md | annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
languages:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- tableqa, data2text
task_ids:
- tableqa |
zhufy | null | @article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprin... | XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translat... | false | 1 | false | zhufy/xquad_split | 2022-02-24T02:29:43.000Z | null | false | b37680e9413ca148de6f60b3c4b9c956a11974c4 | [] | [] | https://huggingface.co/datasets/zhufy/xquad_split/resolve/main/README.md |
# Dataset Card
## Dataset Summary
We split [the original xquad dataset] (https://github.com/deepmind/xquad) into subsets.
We keep the original data format.
## Supported Tasks
extractive question answering
## Language
Thai
## Dataset Split
There are 876/161/153 question-answer pairs from 34/7/7 articles for train/... |
zwang199 | null | null | null | false | 1 | false | zwang199/autonlp-data-traffic_nlp_binary | 2022-10-25T10:02:03.000Z | null | false | c574d814c1502e2cdbe22ad61ae0e56013f08a9a | [] | [
"language:en",
"task_categories:text-classification"
] | https://huggingface.co/datasets/zwang199/autonlp-data-traffic_nlp_binary/resolve/main/README.md | ---
language:
- en
task_categories:
- text-classification
---
# AutoNLP Dataset for project: traffic_nlp_binary
## Table of content
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)... |
fancyerii | null | null | null | false | 2 | false | fancyerii/test | 2022-10-25T10:02:14.000Z | null | false | ad25d57e9499f8417e25ac06dd57f6010786aa65 | [] | [
"size_categories:10K<n<100K",
"task_categories:text-classification",
"task_ids:semantic-similarity-classification"
] | https://huggingface.co/datasets/fancyerii/test/resolve/main/README.md | ---
annotations_creators: []
language_creators: []
language: []
license: []
multilinguality: []
pretty_name: demo
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table ... |
huggan | null | null | null | false | 58 | false | huggan/anime-faces | 2022-03-22T10:01:22.000Z | null | false | 67ebcf8c69b45feb3883d695f04227078a6c9da9 | [] | [
"license:cc0-1.0"
] | https://huggingface.co/datasets/huggan/anime-faces/resolve/main/README.md | ---
license: cc0-1.0
---
# Dataset Card for anime-faces
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instance... |
GEM-submissions | null | null | null | false | 2 | false | GEM-submissions/lewtun__this-is-a-test__1646314818 | 2022-03-03T13:40:29.000Z | null | false | f0f49db9aeb2fe8e7640ae7ee10da1582ecd9569 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:This is a test",
"tags:evaluation",
"tags:benchmark"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__this-is-a-test__1646314818/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: This is a test
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test
|
GEM-submissions | null | null | null | false | 1 | false | GEM-submissions/lewtun__this-is-a-test__1646316929 | 2022-03-03T14:15:35.000Z | null | false | 2a1eb941a4459be7ac03c51e4c2875d938aee9bf | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:This is a test",
"tags:evaluation",
"tags:benchmark"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__this-is-a-test__1646316929/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: This is a test
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test
|
firzens | null | null | null | false | 3 | false | firzens/authors | 2022-03-04T07:48:26.000Z | null | false | fa900453f521486ba24c32a3045e2ee7ccd2a40f | [] | [] | https://huggingface.co/datasets/firzens/authors/resolve/main/README.md | |
NLPC-UOM | null | null | null | false | 1 | false | NLPC-UOM/Sinhala-Tamil-Aligned-Parallel-Corpus | 2022-10-25T10:02:16.000Z | null | false | fdf66398fed02051156c3b34d80b2f4fbe5f01f4 | [] | [
"language:si",
"license:mit"
] | https://huggingface.co/datasets/NLPC-UOM/Sinhala-Tamil-Aligned-Parallel-Corpus/resolve/main/README.md | ---
annotations_creators: []
language:
- si
license:
- mit
--- |
NLPC-UOM | null | null | null | false | 2 | false | NLPC-UOM/AnanyaSinhalaNERDataset | 2022-10-25T10:02:18.000Z | null | false | d8ff10fc5ffd05877bf61ea19f0833565c5a6fd8 | [] | [
"language:si",
"license:mit"
] | https://huggingface.co/datasets/NLPC-UOM/AnanyaSinhalaNERDataset/resolve/main/README.md | # AnanyaSinhalaNERDataset
---
annotations_creators: []
language:
- si
license:
- mit
---
This is part of the dataset used in the paper: Manamini, S.A.P.M., Ahamed, A.F., Rajapakshe, R.A.E.C., Reemal, G.H.A., Jayasena, S., Dias, G.V. and Ranathunga, S., 2016, April. Ananya-a Named-Entity-Recognition (NER) system for Sin... |
openclimatefix | null | @InProceedings{ocf:gfs,
title = {GFS Forecast Dataset},
author={Jacob Bieker},
year={2022}
} | This dataset consists of various NOAA datasets related to operational forecasts, including FNL Analysis files,
GFS operational forecasts, and the raw observations used to initialize the grid. | false | 5 | false | openclimatefix/gfs-reforecast | 2022-10-28T10:25:32.000Z | null | false | 8596eadefb500d1943e7b5e04a78a88ab065eacc | [] | [] | https://huggingface.co/datasets/openclimatefix/gfs-reforecast/resolve/main/README.md | [Needs More Information]
# Dataset Card for GFS-Reforecast
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#dat... |
nlpaueb | null | @inproceedings{loukas-etal-2022-finer,
title = "{FiNER: Financial Numeric Entity Recognition for XBRL Tagging}",
author = "Loukas, Lefteris and
Fergadiotis, Manos and
Chalkidis, Ilias and
Spyropoulou, Eirini and
Malakasiotis, Prodromos and
Androutsopoulos, Ion and
Palioura... | FiNER-139 is a named entity recognition dataset consisting of 10K annual
and quarterly English reports (filings) of publicly traded companies
downloaded from the U.S. Securities and Exchange Commission (SEC)
annotated with 139 XBRL tags in the IOB2 format. | false | 392 | false | nlpaueb/finer-139 | 2022-10-23T05:05:03.000Z | null | false | 080f677a026e304c38666d759ef625d621dc8cb9 | [] | [
"arxiv:2203.06482",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/nlpaueb/finer-139/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: FiNER-139
size_categories:
- 1M<n<10M
source_datasets: []
task_categories:
- structure-prediction
- named-entity-recognition
- entity-extraction
task_ids:... |
GEM-submissions | null | null | null | false | 3 | false | GEM-submissions/ratishsp__seqplan__1646397329 | 2022-03-04T12:35:32.000Z | null | false | 9283dd0d667c67679d54ae59bf871e765e81a8d7 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:SeqPlan",
"tags:evaluation",
"tags:benchmark"
] | https://huggingface.co/datasets/GEM-submissions/ratishsp__seqplan__1646397329/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: SeqPlan
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: SeqPlan
|
GEM-submissions | null | null | null | false | 3 | false | GEM-submissions/ratishsp__seqplan__1646397829 | 2022-03-14T09:21:16.000Z | null | false | 376f8f130939ea4c01e718c71e2cf8f88577e5ef | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:SeqPlan - RotoWire",
"tags:evaluation",
"tags:benchmark"
] | https://huggingface.co/datasets/GEM-submissions/ratishsp__seqplan__1646397829/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: SeqPlan - RotoWire
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: SeqPlan - RotoWire
|
Alvenir | null | null | Dataset of a little bit more than 5hours primarily intended as an evaluation dataset for Danish. | false | 45 | false | Alvenir/alvenir_asr_da_eval | 2022-06-16T09:13:33.000Z | null | false | 4bbf7c8537c8d75ea9b57ec23b4e33505d365cce | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Alvenir/alvenir_asr_da_eval/resolve/main/README.md | ---
license: cc-by-4.0
---
# Dataset Card alvenir_asr_da_eval
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-spl... |
google | null | @article{conneau2022xtreme,
title={XTREME-S: Evaluating Cross-lingual Speech Representations},
author={Conneau, Alexis and Bapna, Ankur and Zhang, Yu and Ma, Min and von Platen, Patrick and Lozhkov, Anton and Cherry, Colin and Jia, Ye and Rivera, Clara and Kale, Mihir and others},
journal={arXiv preprint arXiv:22... | XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval. Covering 102
languages from 10+ language families, 3 different domains and 4
task families, XTREME-S aims to simplify multilingual speech
representation evaluation, as well as catalyze research in “universa... | false | 613 | false | google/xtreme_s | 2022-07-28T12:47:02.000Z | librispeech-1 | false | 3cf59334aa52a74c008a67a3de30f98dd8a28118 | [] | [
"arxiv:2203.10752",
"arxiv:2205.12446",
"arxiv:2007.10310",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:afr",
"language:amh",
"language:ar... | https://huggingface.co/datasets/google/xtreme_s/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- expert-generated
language:
- afr
- amh
- ara
- asm
- ast
- azj
- bel
- ben
- bos
- cat
- ceb
- cmn
- ces
- cym
- dan
- deu
- ell
- eng
- spa
- est
- fas
- ful
- fin
- tgl
- fra
- gle
- glg
- guj
- hau
- h... |
anjandash | null | null | null | false | 2 | false | anjandash/java-8m-methods-v1 | 2022-07-01T20:32:32.000Z | null | false | 4d770e93b949baa821a5a6603039849e590cb260 | [] | [
"language:java",
"license:mit",
"multilinguality:monolingual"
] | https://huggingface.co/datasets/anjandash/java-8m-methods-v1/resolve/main/README.md | ---
language:
- java
license:
- mit
multilinguality:
- monolingual
pretty_name:
- java-8m-methods-v1
--- |
null | null | @inproceedings{otegi-etal-2020-conversational,
title = "{Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for {B}asque}",
author = "Otegi, Arantxa and
Agirre, Aitor and
Campos, Jon Ander and
Soroa, Aitor and
Agirre, Eneko",
booktitle = "Procee... | ElkarHizketak is a low resource conversational Question Answering
(QA) dataset in Basque created by Basque speaker volunteers. The
dataset contains close to 400 dialogues and more than 1600 question
and answers, and its small size presents a realistic low-resource
scenario for conversational QA systems. The dataset is ... | false | 16 | false | elkarhizketak | 2022-11-03T15:51:00.000Z | null | false | 203e799e8154b06c56de04dac7c29ae9f01dbf0f | [] | [
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"language:eu",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa",
"tags:dialogue-qa"
] | https://huggingface.co/datasets/elkarhizketak/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- eu
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
pretty_name: ElkarHizketak
tags:
- dialogue-qa
dataset... |
ruanchaves | null | @article{kodali2022hashset,
title={HashSet--A Dataset For Hashtag Segmentation},
author={Kodali, Prashant and Bhatnagar, Akshala and Ahuja, Naman and Shrivastava, Manish and Kumaraguru, Ponnurangam},
journal={arXiv preprint arXiv:2201.06741},
year={2022}
} | Hashset is a new dataset consisiting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
baseline datasets (STAN and BOUN). We compare and analyse the results across the ... | false | 3 | false | ruanchaves/hashset_distant_sampled | 2022-10-20T19:13:24.000Z | null | false | fb8b329c87153970e0d65e79f8b50220cc2b5ed9 | [] | [
"arxiv:2201.06741",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"language:hi",
"language:en",
"license:unknown",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/hashset_distant_sampled/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- hi
- en
license:
- unknown
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: HashSet Distant Sampled
tags:
- word-segmen... |
ruanchaves | null | @article{kodali2022hashset,
title={HashSet--A Dataset For Hashtag Segmentation},
author={Kodali, Prashant and Bhatnagar, Akshala and Ahuja, Naman and Shrivastava, Manish and Kumaraguru, Ponnurangam},
journal={arXiv preprint arXiv:2201.06741},
year={2022}
} | Hashset is a new dataset consisiting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
baseline datasets (STAN and BOUN). We compare and analyse the results across the ... | false | 2 | false | ruanchaves/hashset_distant | 2022-10-20T19:13:21.000Z | null | false | 0df29003f66c0cb4e17e908cb42e3843d4bd6b11 | [] | [
"arxiv:2201.06741",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"language:hi",
"language:en",
"license:unknown",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/hashset_distant/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- hi
- en
license:
- unknown
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: HashSet Distant
tags:
- word-segmentation
-... |
ruanchaves | null | @article{kodali2022hashset,
title={HashSet--A Dataset For Hashtag Segmentation},
author={Kodali, Prashant and Bhatnagar, Akshala and Ahuja, Naman and Shrivastava, Manish and Kumaraguru, Ponnurangam},
journal={arXiv preprint arXiv:2201.06741},
year={2022}
} | Hashset is a new dataset consisiting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
baseline datasets (STAN and BOUN). We compare and analyse the results across the ... | false | 1 | false | ruanchaves/hashset_manual | 2022-10-20T19:13:18.000Z | null | false | d5aeed029db258e17d93b7e2bf0d1a84ff4f56e5 | [] | [
"arxiv:2201.06741",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:hi",
"language:en",
"license:unknown",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"task_ids:named-entity-recognition",
"tags:word-segmentation... | https://huggingface.co/datasets/ruanchaves/hashset_manual/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- hi
- en
license:
- unknown
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids:
- named-entity-recognition
pretty_name: HashSet Manual
tags:... |
ruanchaves | null | @inproceedings{maddela-etal-2019-multi,
title = "Multi-task Pairwise Neural Ranking for Hashtag Segmentation",
author = "Maddela, Mounica and
Xu, Wei and
Preo{\c{t}}iuc-Pietro, Daniel",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
... | The description below was taken from the paper "Multi-task Pairwise Neural Ranking for Hashtag Segmentation"
by Maddela et al..
"STAN large, our new expert curated dataset, which includes all 12,594 unique English hashtags and their
associated tweets from the same Stanford dataset.
STAN small is the most commo... | false | 2 | false | ruanchaves/stan_large | 2022-10-20T19:13:15.000Z | null | false | 926842c8fbeadabe99a88d30d4b7ce06a42fb64c | [] | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:agpl-3.0",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/stan_large/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- agpl-3.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: STAN Large
tags:
- word-segmentation
---
# Datas... |
ruanchaves | null | @misc{bansal2015deep,
title={Towards Deep Semantic Analysis Of Hashtags},
author={Piyush Bansal and Romil Bansal and Vasudeva Varma},
year={2015},
eprint={1501.03210},
archivePrefix={arXiv},
primaryClass={cs.IR}
} | Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al.. | false | 3 | false | ruanchaves/stan_small | 2022-10-20T19:13:12.000Z | null | false | af6d38e28c5033a1f89b50b9e26950fe73550e29 | [] | [
"arxiv:1501.03210",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/stan_small/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
- conditional-text-generation
task_ids: []
pretty_name: STAN Small
tags:
- w... |
ruanchaves | null | @article{celebi2018segmenting,
title={Segmenting hashtags and analyzing their grammatical structure},
author={Celebi, Arda and {\"O}zg{\"u}r, Arzucan},
journal={Journal of the Association for Information Science and Technology},
volume={69},
number={5},
pages={675--686},
year={2018},
publisher={... | Dev-BOUN Development set that includes 500 manually segmented hashtags. These are selected from tweets about movies,
tv shows, popular people, sports teams etc. Test-BOUN Test set that includes 500 manually segmented hashtags.
These are selected from tweets about movies, tv shows, popular people, sports teams etc. | false | 3 | false | ruanchaves/boun | 2022-10-20T19:13:09.000Z | null | false | 27f9f67d4662570c17e251438164c3508643c32d | [] | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/boun/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: BOUN
tags:
- word-segmentation
---
# Dataset Card... |
ruanchaves | null | @article{celebi2018segmenting,
title={Segmenting hashtags and analyzing their grammatical structure},
author={Celebi, Arda and {\"O}zg{\"u}r, Arzucan},
journal={Journal of the Association for Information Science and Technology},
volume={69},
number={5},
pages={675--686},
year={2018},
publisher={... | 1000 hashtags manually segmented by Çelebi et al. for development purposes,
randomly selected from the Stanford Sentiment Tweet Corpus by Sentiment140. | false | 3 | false | ruanchaves/dev_stanford | 2022-10-20T19:13:37.000Z | null | false | 292e00146ecc1be6feefdb52362eace417791f4f | [] | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/dev_stanford/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: Dev-Stanford
tags:
- word-segmentation
---
# Data... |
ruanchaves | null | @misc{bansal2015deep,
title={Towards Deep Semantic Analysis Of Hashtags},
author={Piyush Bansal and Romil Bansal and Vasudeva Varma},
year={2015},
eprint={1501.03210},
archivePrefix={arXiv},
primaryClass={cs.IR}
} | Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al.. | false | 3 | false | ruanchaves/test_stanford | 2022-10-20T19:13:07.000Z | null | false | 48f64996c295b22e76cec4454362babfad31f581 | [] | [
"arxiv:1501.03210",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/test_stanford/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: Test-Stanford
tags:
- word-segmentation
---
# Dat... |
batterydata | null | null | null | false | 2 | false | batterydata/paper-abstracts | 2022-09-05T15:54:02.000Z | null | false | 2d33f11d465c83eb043544177daceb8f4d508343 | [] | [
"language:en",
"license:apache-2.0",
"task_categories:text-classification"
] | https://huggingface.co/datasets/batterydata/paper-abstracts/resolve/main/README.md | ---
language:
- en
license:
- apache-2.0
task_categories:
- text-classification
pretty_name: 'Battery Abstracts Dataset'
---
# Battery Abstracts Dataset
This dataset includes 29,472 battery papers and 17,191 non-battery papers, a total of 46,663 papers. These papers are manually labelled in terms of the journals to ... |
Davis | null | null | null | false | 7 | false | Davis/Swahili-tweet-sentiment | 2022-03-05T17:58:17.000Z | null | false | 586ba42e6c8a76b305b4e27fc20ce99226a2c1d4 | [] | [
"license:mit"
] | https://huggingface.co/datasets/Davis/Swahili-tweet-sentiment/resolve/main/README.md | ---
license: mit
---
A new Swahili tweet dataset for sentiment analysis.
## Issues ⚠️
Incase you have any difficulties or issues while trying to run the script
you can raise it on the issues section.
## Pull Requests 🔧
If you have something to add or new idea to implement, you are welcome to create ... |
ruanchaves | null | @article{glushkova2019char,
title={Char-RNN and Active Learning for Hashtag Segmentation},
author={Glushkova, Taisiya and Artemova, Ekaterina},
journal={arXiv preprint arXiv:1911.03270},
year={2019}
} | 2000 real hashtags collected from several pages about civil services on vk.com (a Russian social network)
and then segmented manually. | false | 3 | false | ruanchaves/nru_hse | 2022-10-20T19:12:59.000Z | null | false | 4fb954beab9774a12cac3a13ee08616d5e10df6d | [] | [
"arxiv:1911.03270",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:ru",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/nru_hse/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- ru
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: NRU-HSE
tags:
- word-segmentation
---
# Dataset C... |
ruanchaves | null | @article{hill2014empirical,
title={An empirical study of identifier splitting techniques},
author={Hill, Emily and Binkley, David and Lawrie, Dawn and Pollock, Lori and Vijay-Shanker, K},
journal={Empirical Software Engineering},
volume={19},
number={6},
pages={1754--1780},
year={2014},
publishe... | In programming languages, identifiers are tokens (also called symbols) which name language entities.
Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
The Loyola University of Delaware Identifier Splitting Oracle is a dataset for identifier segment... | false | 1 | false | ruanchaves/loyola | 2022-10-20T19:13:04.000Z | null | false | e51544fd07e72dfa6bf830b56e417adba8dc50ba | [] | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:code",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/loyola/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- code
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: The Loyola University of Delaware Identifier Spl... |
AhmedSSoliman | null | null | null | false | 2 | false | AhmedSSoliman/QRCD | 2022-03-06T18:58:06.000Z | null | false | f47b2a116e3e6ad75fc4dbf17a4c8527d0fb0126 | [] | [] | https://huggingface.co/datasets/AhmedSSoliman/QRCD/resolve/main/README.md | This dataset is presented for the task of Answering Questions on the Holy Qur'an.
https://sites.google.com/view/quran-qa-2022
QRCD (Qur'anic Reading Comprehension Dataset) is composed of 1,093 tuples of question-passage pairs that are coupled with their extracted answers to constitute 1,337 question-passage-answer tri... |
mbartolo | null | @inproceedings{bartolo-etal-2021-improving,
title = "Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation",
author = "Bartolo, Max and
Thrush, Tristan and
Jia, Robin and
Riedel, Sebastian and
Stenetorp, Pontus and
Kiela, Douwe",
booktitl... | SynQA is a Reading Comprehension dataset created in the work "Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation" (https://aclanthology.org/2021.emnlp-main.696/).
It consists of 314,811 synthetically generated questions on the passages in the SQuAD v1.1 (https://arxiv.org/abs/1606.... | false | 16 | false | mbartolo/synQA | 2022-10-25T10:02:24.000Z | null | false | f60c3e93c0985c90741d15948afc694f9460b3d9 | [] | [
"arxiv:1606.05250",
"annotations_creators:generated",
"language_creators:found",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa"
] | https://huggingface.co/datasets/mbartolo/synQA/resolve/main/README.md | ---
annotations_creators:
- generated
language_creators:
- found
language:
- en
license: mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
pretty_name: synQA
---
# Dataset Card for synQA
## Tabl... |
Paulosdeanllons | null | null | null | false | 3 | false | Paulosdeanllons/sedar | 2022-03-05T22:38:44.000Z | null | false | 3a424cd1ff2d75a58e267c7f897e1f7d6ae121d4 | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/Paulosdeanllons/sedar/resolve/main/README.md | ---
license: afl-3.0
---
|
ruanchaves | null | @inproceedings{li2018helpful,
title={Helpful or Not? An investigation on the feasibility of identifier splitting via CNN-BiLSTM-CRF.},
author={Li, Jiechu and Du, Qingfeng and Shi, Kun and He, Yu and Wang, Xin and Xu, Jincheng},
booktitle={SEKE},
pages={175--174},
year={2018}
} | In programming languages, identifiers are tokens (also called symbols) which name language entities.
Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
BT11 is a dataset for identifier segmentation,
i.e. the task of adding spaces between the words... | false | 3 | false | ruanchaves/bt11 | 2022-10-20T19:13:02.000Z | null | false | 1877395c47bcf77735761c694234dd55d3598bc5 | [] | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:code",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/bt11/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- code
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: BT11
tags:
- word-segmentation
---
# Dataset Ca... |
ruanchaves | null | @inproceedings{inproceedings,
author = {Lawrie, Dawn and Binkley, David and Morrell, Christopher},
year = {2010},
month = {11},
pages = {3 - 12},
title = {Normalizing Source Code Vocabulary},
journal = {Proceedings - Working Conference on Reverse Engineering, WCRE},
doi = {10.1109/WCRE.2010.10}
} | In programming languages, identifiers are tokens (also called symbols) which name language entities.
Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
Binkley is a dataset for identifier segmentation,
i.e. the task of adding spaces between the wo... | false | 3 | false | ruanchaves/binkley | 2022-10-20T19:12:56.000Z | null | false | 5ccd62cfd185abd77dffc846d2cd3499e0c286c9 | [] | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:code",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/binkley/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- code
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: Binkley
tags:
- word-segmentation
---
# Dataset... |
ruanchaves | null | @inproceedings{li2018helpful,
title={Helpful or Not? An investigation on the feasibility of identifier splitting via CNN-BiLSTM-CRF.},
author={Li, Jiechu and Du, Qingfeng and Shi, Kun and He, Yu and Wang, Xin and Xu, Jincheng},
booktitle={SEKE},
pages={175--174},
year={2018}
} | In programming languages, identifiers are tokens (also called symbols) which name language entities.
Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
Jhotdraw is a dataset for identifier segmentation,
i.e. the task of adding spaces between the w... | false | 3 | false | ruanchaves/jhotdraw | 2022-10-20T19:12:53.000Z | null | false | df859ecce54578af17e873cf79438b082632de1d | [] | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:code",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/jhotdraw/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- code
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: Jhotdraw
tags:
- word-segmentation
---
# Datase... |
ruanchaves | null | @inproceedings{li2018helpful,
title={Helpful or Not? An investigation on the feasibility of identifier splitting via CNN-BiLSTM-CRF.},
author={Li, Jiechu and Du, Qingfeng and Shi, Kun and He, Yu and Wang, Xin and Xu, Jincheng},
booktitle={SEKE},
pages={175--174},
year={2018}
} | In programming languages, identifiers are tokens (also called symbols) which name language entities.
Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
Lynx is a dataset for identifier segmentation,
i.e. the task of adding spaces between the words... | false | 2 | false | ruanchaves/lynx | 2022-10-20T19:12:51.000Z | null | false | 9046da8c9a595ead11d7d243780db677f2ce9618 | [] | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:code",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/lynx/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- code
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
- code-generation
- conditional-text-generation
task_ids: []
pretty_name: ... |
ruanchaves | null | @inproceedings{celebi2016segmenting,
title={Segmenting hashtags using automatically created training data},
author={Celebi, Arda and {\"O}zg{\"u}r, Arzucan},
booktitle={Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)},
pages={2981--2985},
year={2016}
} | Automatically segmented 803K SNAP Twitter Data Set hashtags with the heuristic described in the paper "Segmenting hashtags using automatically created training data". | false | 2 | false | ruanchaves/snap | 2022-10-20T19:12:47.000Z | null | false | dec0e19ff4bab5b5b1a972909b2ea38118644d0f | [] | [
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"tags:word-segmentation"
] | https://huggingface.co/datasets/ruanchaves/snap/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: SNAP
tags:
- word-segmentation
---
# Dataset Card... |
rocca | null | null | null | false | 6 | false | rocca/emojis | 2022-04-29T09:37:55.000Z | null | false | 0a295fc67ae9892cf83d9f585fbd5f29330bf502 | [] | [] | https://huggingface.co/datasets/rocca/emojis/resolve/main/README.md | A collection of 38,176 emoji images from Facebook, Google, Apple, WhatsApp, Samsung, [JoyPixels](https://www.joypixels.com/), Twitter, [emojidex](https://www.emojidex.com/), LG, [OpenMoji](https://openmoji.org/), and Microsoft. It includes all the emojis for these apps/platforms as of early 2022.
* Counts: Facebook=36... |
Carlisle | null | null | null | false | 3 | false | Carlisle/msmarco-passage-non-abs | 2022-03-06T18:40:15.000Z | null | false | b6ac7236577e02ea792277816649217bd6068381 | [] | [
"license:mit"
] | https://huggingface.co/datasets/Carlisle/msmarco-passage-non-abs/resolve/main/README.md | ---
license: mit
---
|
Carlisle | null | null | null | false | 3 | false | Carlisle/msmarco-passage-abs | 2022-03-06T20:04:45.000Z | null | false | 207e3206c2b03cfd98e167d1f2588c7412e37f6b | [] | [
"license:mit"
] | https://huggingface.co/datasets/Carlisle/msmarco-passage-abs/resolve/main/README.md | ---
license: mit
---
|
gustavecortal | null | null | null | false | 18 | false | gustavecortal/fr_covid_news | 2022-10-20T19:01:24.000Z | null | false | 72047fee5890ca82c752902aedb138cc72c6fb96 | [] | [
"annotations_creators:machine-generated",
"language_creators:found",
"language:fr",
"language_bcp47:fr-FR",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:topic-classification",
"task_id... | https://huggingface.co/datasets/gustavecortal/fr_covid_news/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- fr
language_bcp47:
- fr-FR
license:
- unknown
multilinguality:
- monolingual
pretty_name: COVID-19 French News dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
- sequence-modeling... |
FinScience | null | null | null | false | 3 | false | FinScience/FS-distilroberta-fine-tuned | 2022-10-25T10:02:42.000Z | null | false | e5322fec79e6702f69d79829efdc7853f1853802 | [] | [
"language:en"
] | https://huggingface.co/datasets/FinScience/FS-distilroberta-fine-tuned/resolve/main/README.md | ---
language:
- en
---
---
annotations_creators:
- crowdsourced
languages:
- en
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
--- |
Carlisle | null | null | null | false | 3 | false | Carlisle/msmacro-test | 2022-03-11T00:19:32.000Z | null | false | d2ae9ace717cb0ac375fb3b2c14d2bb5205da8a8 | [] | [
"license:mit"
] | https://huggingface.co/datasets/Carlisle/msmacro-test/resolve/main/README.md | ---
license: mit
---
|
Carlisle | null | null | null | false | 2 | false | Carlisle/msmacro-passage-non-abs-small | 2022-03-07T18:19:10.000Z | null | false | 8b0ee369302c23871e42335fe72e76622f486fdf | [] | [
"license:mit"
] | https://huggingface.co/datasets/Carlisle/msmacro-passage-non-abs-small/resolve/main/README.md | ---
license: mit
---
|
Carlisle | null | null | null | false | 3 | false | Carlisle/msmacro-test-corpus | 2022-03-11T00:13:14.000Z | null | false | 18ce5e787650a1f682fec9588df0cc463a984f0e | [] | [
"license:mit"
] | https://huggingface.co/datasets/Carlisle/msmacro-test-corpus/resolve/main/README.md | ---
license: mit
---
|
pensieves | null | @inproceedings{khetan-etal-2022-mimicause,
title={MIMICause: Representation and automatic extraction of causal relation types from clinical notes},
author={Vivek Khetan and Md Imbesat Hassan Rizvi and Jessica Huber and Paige Bartusiak and Bogdan Sacaleanu and Andrew Fano},
booktitle ={Findings of the Associ... | MIMICause Dataset: A dataset for representation and automatic extraction of causal relation types from clinical notes.
The dataset has 2714 samples having both explicit and implicit causality in which entities are in the same sentence or different sentences.
The dataset has following nine semantic causal relations (wit... | false | 3 | false | pensieves/mimicause | 2022-03-29T14:54:48.000Z | null | false | 87615eac7add0a10355c50b25b5cff17e782cad3 | [] | [
"arxiv:2110.07090",
"license:apache-2.0"
] | https://huggingface.co/datasets/pensieves/mimicause/resolve/main/README.md | ---
license: apache-2.0
pretty_name: MIMICause
---
# Dataset Card for "MIMICause"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-in... |
z-uo | null | null | null | false | 2 | false | z-uo/qasper-squad | 2022-10-25T10:02:49.000Z | null | false | 86d2ca7da33fbef822c6a0786c12eaa8cb3772fa | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"language_bcp47:en-US"
] | https://huggingface.co/datasets/z-uo/qasper-squad/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- question-answering
task_ids:
- closed-domain-qa
pretty_name: qasper-squad
language_bcp47:
- en-US
---
# Quasper into squad version
This is a... |
shpotes | null | @inproceedings{BehrendtNovak2017ICRA,
title={A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification},
author={Behrendt, Karsten and Novak, Libor},
booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
organization={IEEE}
} | This dataset contains 13427 camera images at a resolution of 1280x720 pixels and contains about
24000 annotated traffic lights. The annotations include bounding boxes of traffic lights as well
as the current state (active light) of each traffic light. The camera images are provided as raw
12bit HDR images taken with... | false | 2 | false | shpotes/bosch-small-traffic-lights-dataset | 2022-03-10T20:00:45.000Z | null | false | b333b72d400f6b4a23fd33524065cb732b372c8a | [] | [
"license:other"
] | https://huggingface.co/datasets/shpotes/bosch-small-traffic-lights-dataset/resolve/main/README.md | ---
license: other
---
|
Carlosholivan | null | null | null | false | 3 | false | Carlosholivan/base | 2022-03-08T18:14:11.000Z | null | false | abab96a91ef584e7da293226844f0eaafb9498b7 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/Carlosholivan/base/resolve/main/README.md | ---
license: apache-2.0
---
|
SocialGrep | null | null | This dataset follows the notorious subreddit /r/Antiwork, a place for many Redditors to share resources and discuss grievances with the current labour market. | false | 2 | false | SocialGrep/the-antiwork-subreddit-dataset | 2022-07-01T17:57:34.000Z | null | false | 4a906f0b97bc7341bfc5d4453ae23a78edefc0b3 | [] | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original"
] | https://huggingface.co/datasets/SocialGrep/the-antiwork-subreddit-dataset/resolve/main/README.md | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for the-antiwork-subreddit-dataset
## Table of Contents
- [Dataset Description](#dat... |
laion | null | null | null | false | 177 | false | laion/laion2B-en | 2022-03-09T00:25:22.000Z | null | false | 9d1b74d39b6517383b2a2152ae2772888b594e45 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/laion/laion2B-en/resolve/main/README.md | ---
license: cc-by-4.0
---
|
christianloyal | null | null | null | false | 2 | false | christianloyal/loyal_clinc_MLE | 2022-03-10T17:50:54.000Z | null | false | 90b930b5609f5f668c765a5d23f9610d5d0dbcf1 | [] | [
"license:mit"
] | https://huggingface.co/datasets/christianloyal/loyal_clinc_MLE/resolve/main/README.md | ---
license: mit
---
Dataset for Loyal Health Inc Software Engineer Machine Learning Interview |
laion | null | null | null | false | 74 | false | laion/laion2B-multi | 2022-03-09T03:46:58.000Z | null | false | fc4613eeec55c60d113ac9cab58dca7c3e12523e | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/laion/laion2B-multi/resolve/main/README.md | ---
license: cc-by-4.0
---
|
hadehuang | null | null | null | false | 1 | false | hadehuang/testdataset | 2022-03-09T08:24:49.000Z | null | false | 1b9776677fd2d5b21056e200089942709d0c3206 | [] | [] | https://huggingface.co/datasets/hadehuang/testdataset/resolve/main/README.md | This is my first dataset |
khcy82dyc | null | null | null | false | 2 | false | khcy82dyc/zzzz | 2022-03-09T11:03:58.000Z | null | false | 59566ca6c10db39a863bef6d894e095e85e5c930 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/khcy82dyc/zzzz/resolve/main/README.md | ---
license: apache-2.0
---
|
ai4bharat | null | @inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
u... | This is the paraphrasing dataset released as part of IndicNLG Suite. Each
input is paired with up to 5 references. We create this dataset in eleven
languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The total
size of the dataset is 5.57M. | false | 3 | false | ai4bharat/IndicParaphrase | 2022-10-13T06:08:55.000Z | null | false | d74c67aec2ac5a2f561bcb30aa8e1fc7d7d88b92 | [] | [
"arxiv:2203.05437",
"annotations_creators:no-annotation",
"language_creators:found",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"license:cc-by-nc-4.0",
"multilingua... | https://huggingface.co/datasets/ai4bharat/IndicParaphrase/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: IndicParaphrase
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- conditional-text-generatio... |
rubrix | null | null | null | false | 3 | false | rubrix/sst2_with_predictions | 2022-09-16T13:23:05.000Z | null | false | 03d5016d18872b209e80fd9eb913225c096defd0 | [] | [] | https://huggingface.co/datasets/rubrix/sst2_with_predictions/resolve/main/README.md | # Comparing model predictions and ground truth labels with Rubrix and Hugging Face
## Build dataset
You can skip this step if you run:
```python
from datasets import load_dataset
import rubrix as rb
ds = rb.DatasetForTextClassification.from_datasets(load_dataset("rubrix/sst2_with_predictions", split="train"))
```... |
nthngdy | null | @inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{\'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Associat... | The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\ | false | 57 | false | nthngdy/oscar-mini | 2022-10-25T08:56:37.000Z | oscar | false | e41c9a32ae582f42bbb1fa2858e850f75bb7e9fe | [] | [
"arxiv:2010.14571",
"annotations_creators:no-annotation",
"language_creators:found",
"language:af",
"language:am",
"language:ar",
"language:arz",
"language:as",
"language:az",
"language:azb",
"language:ba",
"language:be",
"language:bg",
"language:bn",
"language:bo",
"language:br",
"l... | https://huggingface.co/datasets/nthngdy/oscar-mini/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- am
- ar
- arz
- as
- az
- azb
- ba
- be
- bg
- bn
- bo
- br
- ca
- ce
- ceb
- ckb
- cs
- cv
- cy
- da
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gl
- gu
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- ka
- kk
- ... |
laion | null | null | null | false | 3 | false | laion/laion1B-nolang | 2022-03-09T15:04:35.000Z | null | false | 2ecab88787cb57c38f3c2ddf1da94a9351538769 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/laion/laion1B-nolang/resolve/main/README.md | ---
license: cc-by-4.0
---
|
drAbreu | null | @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... | 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... | false | 318 | false | drAbreu/bc4chemd_ner | 2022-10-25T10:02:51.000Z | bc4chemd | false | 2615416d7c8cd65fbd6b2b7094f4136d4f8d9515 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:GitHub",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/drAbreu/bc4chemd_ner/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- GitHub
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: bc4chemd
pretty_name... |
Non-Residual-Prompting | null | TODO | The task of C2Gen is to both generate commonsensical text which include the given words, and also have the generated text adhere to the given context. | false | 28 | false | Non-Residual-Prompting/C2Gen | 2022-10-25T10:02:58.000Z | null | false | f1cb70125a6b1ad5dd0cc97501476309cf540b3d | [] | [
"arxiv:1911.03705",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:<100K",
"task_categories:text-generation"
] | https://huggingface.co/datasets/Non-Residual-Prompting/C2Gen/resolve/main/README.md | ---
language:
- en
license:
- cc-by-sa-4.0
size_categories:
- <100K
task_categories:
- text-generation
---
# Dataset Card for Contextualized CommonGen(C2Gen)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dat... |
CLUTRR | null | @article{sinha2019clutrr,
Author = {Koustuv Sinha and Shagun Sodhani and Jin Dong and Joelle Pineau and William L. Hamilton},
Title = {CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text},
Year = {2019},
journal = {Empirical Methods of Natural Language Processing (EMNLP)},
arxiv = {1908.06177}
} | CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning),
a diagnostic benchmark suite, is first introduced in (https://arxiv.org/abs/1908.06177)
to test the systematic generalization and inductive reasoning capabilities of NLU systems. | false | 3 | false | CLUTRR/v1 | 2022-10-25T10:03:19.000Z | null | false | a8158d1fac10864c3424d53662fe63bf7d82dd87 | [] | [
"arxiv:1908.06177",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K"
] | https://huggingface.co/datasets/CLUTRR/v1/resolve/main/README.md | ---
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
---
# Dataset Card for CLUTRR
## Table of Contents
## Dataset Description
### Dataset Summary
**CLUTRR** (**C**ompositional **L**anguage **U**nderstanding and **T**ext-based **R**elational **R**easoning), a diagnostic... |
damlab | null | null | null | false | 4 | false | damlab/uniprot | 2022-03-12T12:08:29.000Z | null | false | 095f98c5853b271b00c05bbe4f2167ecdbe8951f | [] | [
"liscence:mit"
] | https://huggingface.co/datasets/damlab/uniprot/resolve/main/README.md | ---
liscence: mit
---
# Dataset Description
## Dataset Summary
This dataset is a mirror of the Uniprot/SwissProt database. It contains the names and sequences of >500K proteins.
This dataset was parsed from the FASTA file at https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uni... |
juched | null | null | null | false | 2 | false | juched/spotifinders | 2022-03-10T01:46:51.000Z | null | false | 4887946743ee9325f7597ddadb72ece8b74a8105 | [] | [] | https://huggingface.co/datasets/juched/spotifinders/resolve/main/README.md | annotations_creators:
- Parth Parekh
languages:
- en
licenses:
- MIT
multilinguality:
- monolingual
size_categories:
- 0<n<100
source_datasets:
- original
task_categories:
- sentence-categorization
# Dataset Card for spotifinders
## Table of Contents
- [Dataset Description](#dataset-description)
... |
juched | null | @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}... | Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. | false | 2 | false | juched/spotifinders-dataset | 2022-03-29T00:42:18.000Z | null | false | 29429c80610b9f235148694561358a1bd092c927 | [] | [
"license:mit"
] | https://huggingface.co/datasets/juched/spotifinders-dataset/resolve/main/README.md | ---
license: mit
---
|
PaddlePaddle | null | null | DureaderRobust is a chinese reading comprehension dataset, designed to evaluate the MRC models from three aspects: over-sensitivity, over-stability and generalization. | false | 858 | false | PaddlePaddle/dureader_robust | 2022-03-10T05:14:18.000Z | null | false | 142e3e33e59f6c13239b5b743f16e5bfcfbc9abf | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/PaddlePaddle/dureader_robust/resolve/main/README.md | ---
license: apache-2.0
---
|
kyleinincubated | null | null | null | false | 1 | false | kyleinincubated/autonlp-data-cat33 | 2022-10-25T10:03:04.000Z | null | false | 51f31e2aa96a98b68b3595acca660904a3ffca33 | [] | [
"language:zh",
"task_categories:text-classification"
] | https://huggingface.co/datasets/kyleinincubated/autonlp-data-cat33/resolve/main/README.md | ---
language:
- zh
task_categories:
- text-classification
---
# AutoNLP Dataset for project: cat33
## Table of content
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Sp... |
Georgii | null | null | null | false | 8 | false | Georgii/poetry-genre | 2022-03-10T08:12:23.000Z | null | false | ad1f65afa83d161c5860ad126ab75c4287fb6cbe | [] | [] | https://huggingface.co/datasets/Georgii/poetry-genre/resolve/main/README.md | en poems and genres test |
ai4bharat | null | @inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
u... | This is the new headline generation dataset released as part of IndicNLG Suite. Each
input document is paired an output title. We create this dataset in eleven
languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The total
size of the dataset is 1.43M. | false | 3 | false | ai4bharat/IndicHeadlineGeneration | 2022-10-13T06:08:20.000Z | null | false | d9845634dc0f9cb48d4a26c9f6d8986fb87d2027 | [] | [
"arxiv:2203.05437",
"annotations_creators:no-annotation",
"language_creators:found",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"license:cc-by-nc-4.0",
"multilingua... | https://huggingface.co/datasets/ai4bharat/IndicHeadlineGeneration/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: IndicHeadlineGeneration
size_categories:
- 27K<n<341K
source_datasets:
- original for Hindi, and modified [IndicGLUE]... |
ai4bharat | null | @inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
u... | This is the sentence summarization dataset released as part of IndicNLG Suite. Each
input sentence is paired with an output summary. We create this dataset in eleven
languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta and te. The total
size of the dataset is 431K. | false | 16 | false | ai4bharat/IndicSentenceSummarization | 2022-10-13T06:08:31.000Z | null | false | 53cfce5e0ca8da828ee1b6223dcf3ea986582812 | [] | [
"arxiv:2203.05437",
"annotations_creators:no-annotation",
"language_creators:found",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"license:cc-by-nc-4.0",
"multilingua... | https://huggingface.co/datasets/ai4bharat/IndicSentenceSummarization/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: IndicSentenceSummarization
size_categories:
- 5K<n<112K
source_datasets:
- original for Hindi, and modified [IndicGLU... |
ai4bharat | null | @inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
u... | This is the WikiBio dataset released as part of IndicNLG Suite. Each
example has four fields: id, infobox, serialized infobox and summary. We create this dataset in nine
languages including as, bn, hi, kn, ml, or, pa, ta, te. The total
size of the dataset is 57,426. | false | 2 | false | ai4bharat/IndicWikiBio | 2022-10-13T06:08:34.000Z | null | false | 9b177ff8d3eeaf8d07d2918546e9b79ee655e29b | [] | [
"arxiv:2203.05437",
"annotations_creators:no-annotation",
"language_creators:found",
"language:as",
"language:bn",
"language:hi",
"language:kn",
"language:ml",
"language:or",
"language:pa",
"language:ta",
"language:te",
"license:cc-by-nc-4.0",
"multilinguality:multilingual",
"size_catego... | https://huggingface.co/datasets/ai4bharat/IndicWikiBio/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- as
- bn
- hi
- kn
- ml
- or
- pa
- ta
- te
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: IndicWikiBio
size_categories:
- 1960<n<11,502
source_datasets:
- none. Originally generated from www.wikimedia.org.
task_catego... |
ai4bharat | null | @inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
u... | This is the Question Generation dataset released as part of IndicNLG Suite. Each
example has five fields: id, squad_id, answer, context and question. We create this dataset in eleven
languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. This is a translated data. The examples in each language are exactly si... | false | 13 | false | ai4bharat/IndicQuestionGeneration | 2022-10-13T06:08:25.000Z | null | false | 3c9cfa7c513097aa3e475ad34d8578c52b48514f | [] | [
"arxiv:2203.05437",
"annotations_creators:no-annotation",
"language_creators:found",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"license:cc-by-nc-4.0",
"multilingua... | https://huggingface.co/datasets/ai4bharat/IndicQuestionGeneration/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: IndicQuestionGeneration
size_categories:
- 98K<n<98K
source_datasets:
- we start with the SQuAD question answering da... |
aasd291809733 | null | null | null | false | 2 | false | aasd291809733/myself | 2022-03-10T13:46:37.000Z | null | false | bb3d15353a87a2b256ffb6abc5fa0436b4333b30 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/aasd291809733/myself/resolve/main/README.md | ---
license: apache-2.0
---
|
Mulin | null | null | This news dataset is a holiday information of singapore from 2017 to 2022. | false | 1 | false | Mulin/sg-holiday | 2022-03-14T10:44:11.000Z | null | false | 9e3533eec643aebede8aaa7ea781c9b58f721dd8 | [] | [
"license:mit"
] | https://huggingface.co/datasets/Mulin/sg-holiday/resolve/main/README.md | ---
license: mit
---
Singapore's holiday data from 2017 to 2022. |
Biomedical-TeMU | null | null | null | false | 3 | false | Biomedical-TeMU/ProfNER_corpus_classification | 2022-03-10T21:24:30.000Z | null | false | f5ee87052fbba38c7e0a49a4dad24724ed97302f | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Biomedical-TeMU/ProfNER_corpus_classification/resolve/main/README.md | ---
license: cc-by-4.0
---
|
Biomedical-TeMU | null | null | null | false | 3 | false | Biomedical-TeMU/ProfNER_corpus_NER | 2022-03-10T21:50:30.000Z | null | false | de9bf1404880f4b7225e1cc0e9268192e57fefca | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Biomedical-TeMU/ProfNER_corpus_NER/resolve/main/README.md | ---
license: cc-by-4.0
---
## Description
**Gold standard annotations for profession detection in Spanish COVID-19 tweets**
The entire corpus contains 10,000 annotated tweets. It has been split into training, validation, and test (60-20-20). The current version contains the training and development set of th... |
McGill-NLP | null | FeedbackQA is a retrieval-based QA dataset that contains interactive feedback from users. It has two parts: the first part contains a conventional RQA dataset, whilst this repo contains the second part, which contains feedback(ratings and natural language explanations) for QA pairs. | false | 2 | false | McGill-NLP/feedbackQA | 2022-07-01T15:40:36.000Z | null | false | 413f7e57035e5610593b51c74a9a21364cc29498 | [] | [
"arxiv:2204.03025",
"license:apache-2.0"
] | https://huggingface.co/datasets/McGill-NLP/feedbackQA/resolve/main/README.md | ---
license: apache-2.0
---
# Dataset Card for FeedbackQA
[📄 Read](https://arxiv.org/abs/2204.03025)<br>
[💾 Code](https://github.com/McGill-NLP/feedbackqa)<br>
[🔗 Webpage](https://mcgill-nlp.github.io/feedbackqa/)<br>
[💻 Demo](http://206.12.100.48:8080/)<br>
[🤗 Huggingface Dataset](https://huggingface.co/dataset... | |
Biomedical-TeMU | null | null | null | false | 3 | false | Biomedical-TeMU/SPACCC_Sentence-Splitter | 2022-03-11T02:09:00.000Z | null | false | 393badffe34773d1536cfedfdc2abe14317d38e7 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Biomedical-TeMU/SPACCC_Sentence-Splitter/resolve/main/README.md | ---
license: cc-by-4.0
---
# The Sentence Splitter (SS) for Clinical Cases Written in Spanish
## Introduction
This repository contains the sentence splitting model trained using the SPACCC_SPLIT corpus (https://github.com/PlanTL-SANIDAD/SPACCC_SPLIT). The model was trained using the 90% of the corpus (900 clin... |
Biomedical-TeMU | null | null | null | false | 2 | false | Biomedical-TeMU/SPACCC_Tokenizer | 2022-03-11T02:18:16.000Z | null | false | b80bc1594c34c07cee7888a0c741ae41ac06b274 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Biomedical-TeMU/SPACCC_Tokenizer/resolve/main/README.md | ---
license: cc-by-4.0
---
# The Tokenizer for Clinical Cases Written in Spanish
## Introduction
This repository contains the tokenization model trained using the SPACCC_TOKEN corpus (https://github.com/PlanTL-SANIDAD/SPACCC_TOKEN). The model was trained using the 90% of the corpus (900 clinical cases) and tes... |
Biomedical-TeMU | null | null | null | false | 3 | false | Biomedical-TeMU/CodiEsp_corpus | 2022-03-11T02:24:53.000Z | null | false | 5ff2b006ea74699eccd393a5a0f3b99396d01e0c | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Biomedical-TeMU/CodiEsp_corpus/resolve/main/README.md | ---
license: cc-by-4.0
---
## Introduction
These are the train, development, test and background sets of the CodiEsp corpus. Train and development have gold standard annotations. The unannotated background and test sets are distributed together. All documents are released in the context of the CodiEsp track for C... |
Mulin | null | null | null | false | 3 | false | Mulin/weather-data | 2022-03-11T06:41:03.000Z | null | false | b80b8e1442d843ab1f02050ef297b13be4fb4a72 | [] | [
"license:mit"
] | https://huggingface.co/datasets/Mulin/weather-data/resolve/main/README.md | ---
license: mit
---
|
lstynerl | null | null | null | false | 2 | false | lstynerl/M1a1d | 2022-03-11T03:32:56.000Z | null | false | 7e37d9d97bbdc47fbd710913a75c355e878b343e | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/lstynerl/M1a1d/resolve/main/README.md | ---
license: apache-2.0
---
|
Khedesh | null | null | null | false | 3 | false | Khedesh/ArmanNER | 2022-03-11T10:42:30.000Z | null | false | 38ccb945600346d52580891d6d77f5c2bfaae069 | [] | [] | https://huggingface.co/datasets/Khedesh/ArmanNER/resolve/main/README.md | # PersianNER
Named-Entity Recognition in Persian Language
## ArmanPersoNERCorpus
This is the first manually-annotated Persian named-entity (NE) dataset (ISLRN 399-379-640-828-6). We are releasing it only for academic research use.
The dataset includes 250,015 tokens and 7,682 Persian sentences in total. It is availa... |
gigant | null | @inproceedings{CycleGAN2017,
title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
year={2017}
} | Two unpaired sets of photos of respectively horses and zebras, designed for unpaired image-to-image translation, as seen in the paper introducing CycleGAN | false | 3 | false | gigant/horse2zebra | 2022-10-24T17:37:53.000Z | null | false | 04bb1414d14d63bffc026c6f12d047b7a3232930 | [] | [
"arxiv:1703.10593",
"license:cc",
"task_categories:image-to-image",
"tags:GAN",
"tags:unpaired-image-to-image-translation"
] | https://huggingface.co/datasets/gigant/horse2zebra/resolve/main/README.md | ---
license: cc
task_categories:
- image-to-image
task_ids: []
pretty_name: Horse2Zebra
tags:
- GAN
- unpaired-image-to-image-translation
---
## Dataset Description
- **Homepage:** https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
- **Paper:** https://arxiv.org/abs/1703.10593
### Dataset Summary
This ... |
GEM-submissions | null | null | null | false | 1 | false | GEM-submissions/ratishsp__macro__1646998904 | 2022-03-11T11:41:47.000Z | null | false | f90b0fced2b6b7d1fb3fcdb04cb5b754eafab378 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Macro",
"tags:evaluation",
"tags:benchmark"
] | https://huggingface.co/datasets/GEM-submissions/ratishsp__macro__1646998904/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Macro
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: Macro
|
Zeel | null | null | null | false | 1 | false | Zeel/common | 2022-10-25T10:22:40.000Z | null | false | b8e66595f3f7e20f5c2a6f69be3504d2e97d790b | [] | [
"language:en"
] | https://huggingface.co/datasets/Zeel/common/resolve/main/README.md | ---
language:
- en
pretty_name: common
---
# Dataset Card for Zeel/common
|
microsoft | null | null | null | false | 2 | false | microsoft/CLUES | 2022-03-25T22:05:58.000Z | null | false | ce7b8f1a30bfae5184e554a5bf44b76b9e8fc011 | [] | [
"license:mit"
] | https://huggingface.co/datasets/microsoft/CLUES/resolve/main/README.md | ---
license: mit
---
# CLUES: Few-Shot Learning Evaluation in Natural Language Understanding
This repo contains the data for the NeurIPS 2021 benchmark [Constrained Language Understanding Evaluation Standard (CLUES)](https://openreview.net/pdf?id=VhIIQBm00VI).
## Leaderboard
We maintain a [Leaderboard](ht... |
rakkaalhazimi | null | null | null | false | 2 | false | rakkaalhazimi/hotel-review | 2022-03-12T07:23:47.000Z | null | false | 3c70f2fe25f7c73d2460f77a4c3f8b1aa8a6e819 | [] | [
"license:gpl-3.0"
] | https://huggingface.co/datasets/rakkaalhazimi/hotel-review/resolve/main/README.md | ---
license: gpl-3.0
---
# Review Hotel in Indonesia
### Dataset Summary
Data about reviews of hotels in Indonesia
### Languages
Indonesia
## Dataset Structure
### Data Fields
- review_id : unique identification code of each review
- review_text : the main review of text
- category : label f... |
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