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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
izumi-lab/wikinews-ja-20230728 | 2023-07-29T03:06:48.000Z | [
"language:ja",
"license:cc-by-2.5",
"region:us"
] | izumi-lab | null | null | 3 | 162 | 2023-07-28T07:01:06 | ---
dataset_info:
features:
- name: text
dtype: string
- name: title
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 7998861
num_examples: 4283
download_size: 4086208
dataset_size: 7998861
license: cc-by-2.5
language:
- ja
---
# Dataset Card for "wikinews-ja-... | 462 | [
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C-MTEB/QBQTC | 2023-07-28T13:38:12.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 162 | 2023-07-28T13:38:05 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
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splits:
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download_size: 387552
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yashnbx/l27b-E02-large-b10-1314-3 | 2023-09-30T16:29:18.000Z | [
"region:us"
] | yashnbx | null | null | 0 | 162 | 2023-09-30T16:28:57 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: conversations
list:
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splits:
- name: test
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download_size: 16... | 534 | [
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chrisgru/commonsense-dialogues2 | 2023-10-19T13:07:25.000Z | [
"region:us"
] | chrisgru | null | null | 0 | 162 | 2023-10-18T20:41:13 | ---
dataset_info:
features:
- name: conversations
list:
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splits:
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n... | 742 | [
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PlanTL-GOB-ES/SQAC | 2023-10-12T23:35:38.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:es",
"license:cc-by-sa-4.0",
"arxiv:1606.05250",
"region:us"
] | PlanTL-GOB-ES | This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment.
The sources of the contexts are:
* Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
* News fro... | bibtex
@article{DBLP:journals/corr/abs-2107-07253,
author = {Asier Guti{\'{e}}rrez{-}Fandi{\~{n}}o and
Jordi Armengol{-}Estap{\'{e}} and
Marc P{\`{a}}mies and
Joan Llop{-}Palao and
Joaqu{\'{\i}}n Silveira{-}Ocampo and
Casimiro Pio Carrino a... | 7 | 161 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- es
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Spanish Question Answering Corpus (SQAC)
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# SQAC (Spanish Question-A... | 6,428 | [
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addy88/nq-question-answeronly | 2021-12-14T13:59:58.000Z | [
"region:us"
] | addy88 | null | null | 1 | 161 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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ceyda/smithsonian_butterflies | 2022-07-13T09:32:27.000Z | [
"task_categories:image-classification",
"task_ids:multi-label-image-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
] | ceyda | null | null | 6 | 161 | 2022-04-09T00:38:13 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Smithsonian Butterflies
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-label-image-classificatio... | 4,520 | [
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language-and-voice-lab/samromur_children | 2023-10-15T16:02:44.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:is",
"license:cc-by-4.0",
"samromur",
"children's speech",
"icelandic: iceland"... | language-and-voice-lab | The Samrómur Children corpus contains more than 137000 validated speech-recordings uttered by Icelandic children. | @misc{menasamromurchildren2022,
title={Samrómur Children Icelandic Speech 1.0},
ldc_catalog_no={LDC2022S11},
DOI={https://doi.org/10.35111/frrj-qd60},
author={Hernández Mena, Carlos Daniel and Borsky, Michal and Mollberg, David Erik and Guðmundsson, Smári Freyr and Hedström, Staffan and Pálsso... | 1 | 161 | 2022-11-26T03:15:54 | ---
annotations_creators:
- crowdsourced
language:
- is
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: "Samrómur Children Icelandic Speech 1.0"
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- "samromur"
- children's speech
- 'icelandic: iceland'
- ice... | 11,576 | [
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findzebra/corpus.latest.vod-retriever-medical-v1.1 | 2023-09-05T05:17:16.000Z | [
"region:us"
] | findzebra | null | null | 0 | 161 | 2023-09-05T04:42:19 | Entry not found | 15 | [
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SetFit/amazon_polarity | 2022-01-19T20:49:58.000Z | [
"region:us"
] | SetFit | null | null | 0 | 160 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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medalpaca/medical_meadow_mmmlu | 2023-04-06T17:49:48.000Z | [
"region:us"
] | medalpaca | null | null | 0 | 160 | 2023-04-06T17:49:34 | Entry not found | 15 | [
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ura-hcmut/synthetic_reasoning | 2023-09-19T02:37:10.000Z | [
"task_categories:text2text-generation",
"language:vi",
"license:cc-by-nc-sa-4.0",
"region:us"
] | ura-hcmut | null | null | 0 | 160 | 2023-09-19T02:01:51 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text2text-generation
language:
- vi
configs:
- config_name: induction_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_induction_training.csv
- split: test
path: synthetic_reasoning_gcp_induction.csv
- config_name: induction... | 1,622 | [
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lucasmccabe/logiqa | 2023-02-08T01:51:31.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | lucasmccabe | LogiQA is constructed from the logical comprehension problems from publically available questions of the National Civil Servants Examination of China, which are designed to test the civil servant candidates’ critical thinking and problem solving. This dataset includes the English versions only; the Chinese versions are... | @article{liu2020logiqa,
title={Logiqa: A challenge dataset for machine reading comprehension with logical reasoning},
author={Liu, Jian and Cui, Leyang and Liu, Hanmeng and Huang, Dandan and Wang, Yile and Zhang, Yue},
journal={arXiv preprint arXiv:2007.08124},
year={2020}
} | 3 | 159 | 2023-01-12T04:14:53 | ---
task_categories:
- question-answering
language:
- en
pretty_name: LogiQA
size_categories:
- 1K<n<10K
paperswithcode_id: logiqa
dataset_info:
features:
- name: context
dtype: string
- name: query
dtype: string
- name: options
sequence:
dtype: string
- name: correct_optio... | 2,729 | [
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kotzeje/lamini_docs.jsonl | 2023-08-24T12:35:32.000Z | [
"region:us"
] | kotzeje | null | null | 2 | 159 | 2023-08-24T12:35:29 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 573589
num_examples: 1400
download_size: 283465
dataset_size: 573589
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset ... | 481 | [
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Nbardy/renamed_waves | 2023-10-19T19:27:20.000Z | [
"region:us"
] | Nbardy | null | null | 0 | 159 | 2023-10-19T19:21:43 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 498961211.25
num_examples: 1306
download_size: 497509644
dataset_size: 498961211.25
---
#... | 486 | [
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metaeval/imppres | 2023-06-21T12:52:43.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"license:apache-2.0",
"region:us"
] | metaeval | Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize se... | @inproceedings{jeretic-etal-2020-natural,
title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlicature} and {PRESupposition}",
author = "Jereti\v{c}, Paloma and
Warstadt, Alex and
Bhooshan, Suvrat and
Williams, Adina",
booktitle = "Proceedings of the 58th Annual... | 0 | 158 | 2023-01-05T20:14:45 | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
task_ids:
- natural-language-inference
---
Imppres, but it works
https://github.com/facebookresearch/Imppres
```
@inproceedings{jeretic-etal-2020-natural,
title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlic... | 2,031 | [
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jjzha/skillspan | 2023-09-07T12:12:10.000Z | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | jjzha | null | null | 0 | 158 | 2023-07-04T13:37:04 | ---
license: cc-by-4.0
language: en
---
This is the SkillSpan dataset created by:
```
@inproceedings{zhang-etal-2022-skillspan,
title = "{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings",
author = "Zhang, Mike and
Jensen, Kristian and
Sonniks, Sif and
Plank, Barba... | 1,291 | [
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0.0390625,
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0.0271... |
SatwikKambham/ex-dark | 2023-10-13T10:58:40.000Z | [
"license:bsd-3-clause",
"region:us"
] | SatwikKambham | The Exclusively Dark (ExDARK) dataset is a collection of low-light
images from very low-light environments to twilight (i.e 10 different
conditions) with 12 object classes (similar to PASCAL VOC) annotated on both
image class level and local object bounding boxes.
The object classes are as follows:
- Dog
- Motorbike
... | @article{Exdark,
title = {Getting to Know Low-light Images with The Exclusively Dark Dataset},
author = {Loh, Yuen Peng and Chan, Chee Seng},
journal = {Computer Vision and Image Understanding},
volume = {178},
pages = {30-42},
year = {2019},
doi = {https://doi.org/10.1016/j.cviu.2018.10.010}
} | 0 | 158 | 2023-10-12T07:54:37 | ---
license: bsd-3-clause
dataset_info:
config_name: exdark
features:
- name: img
dtype: image
- name: labels
sequence:
class_label:
names:
'0': Dog
'1': Motorbike
'2': People
'3': Cat
'4': Chair
'5': Table
'6': Car
... | 1,396 | [
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joelniklaus/Multi_Legal_Pile_Commercial | 2023-10-18T20:40:00.000Z | [
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language... | joelniklaus | Multi Legal Pile is a dataset of legal documents in the 24 EU languages. | 0 | 158 | 2023-10-18T20:23:08 | ---
annotations_creators:
- other
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license: cc-by-sa-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: 'MultiLegalPile: A Large-Scale Multili... | 24,250 | [
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indonli | 2023-01-25T14:33:00.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:id",
... | null | IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian.
IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set.
It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various ... | @inproceedings{mahendra-etal-2021-indonli,
title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian",
author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natu... | 6 | 157 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- expert-generated
language:
- id
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_i... | 7,615 | [
[
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kor_hate | 2023-01-25T14:33:47.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ko",
"license:cc-b... | null | Human-annotated Korean corpus collected from a popular domestic entertainment news aggregation platform
for toxic speech detection. Comments are annotated for gender bias, social bias and hate speech. | @inproceedings{moon-etal-2020-beep,
title = "{BEEP}! {K}orean Corpus of Online News Comments for Toxic Speech Detection",
author = "Moon, Jihyung and
Cho, Won Ik and
Lee, Junbum",
booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
... | 4 | 157 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: korean-hat... | 9,443 | [
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0.00698471069335937... |
jimregan/clarinpl_studio | 2023-01-21T12:27:08.000Z | [
"task_categories:other",
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:other",
"arxiv:1706.00245",
"region:us"
] | jimregan | The corpus consists of 317 speakers recorded in 554
sessions, where each session consists of 20 read sentences and 10 phonetically rich words. The size of
the audio portion of the corpus amounts to around 56 hours, with transcriptions containing 356674 words
from a vocabulary of size 46361.
Note that in order to limit... | @article{korvzinek2017polish,
title={Polish read speech corpus for speech tools and services},
author={Kor{\v{z}}inek, Danijel and Marasek, Krzysztof and Brocki, {\L}ukasz and Wo{\l}k, Krzysztof},
journal={arXiv preprint arXiv:1706.00245},
year={2017}
} | 1 | 157 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language:
- pl
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for ClarinPL Studio Speech Corpus
## Table of Contents
- [Datase... | 4,481 | [
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0.... |
linxinyuan/cola | 2022-06-08T07:26:13.000Z | [
"region:us"
] | linxinyuan | null | null | 1 | 157 | 2022-06-08T07:24:26 | Entry not found | 15 | [
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adsabs/FOCAL | 2023-10-18T19:15:03.000Z | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"astronomy",
"region:us"
] | adsabs | null | null | 1 | 157 | 2023-05-17T19:09:34 | ---
annotations_creators:
- expert-generated
license: cc-by-4.0
task_categories:
- token-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
tags:
- astronomy
dataset_info:
features:
- name: Identifier
dtype: string
- name: Paragraph
dtype: string
- name: Citation Te... | 3,958 | [
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0.... |
pankajmathur/orca_mini_v1_dataset | 2023-08-15T20:26:46.000Z | [
"license:apache-2.0",
"region:us"
] | pankajmathur | null | null | 8 | 157 | 2023-07-30T22:15:20 | ---
license: apache-2.0
---
An Orca Style dataset, which can be used to fine tuned base models with the following prompt format.
```
### System:
<system>
### User:
<instruction>
### Assistant:
<output>
```
More details coming soon.. | 238 | [
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0.002429962158203125,... |
cawoylel/FulaSpeechCorpora-splited-noise_augmented | 2023-10-25T22:56:22.000Z | [
"region:us"
] | cawoylel | null | null | 0 | 157 | 2023-10-25T22:21:41 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: dialect
dtype: string
splits:
- name:... | 789 | [
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cail2018 | 2022-11-18T19:24:58.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:zh",
"license:unknown",
"judgement-prediction",
"arxiv:1807.02478",
"region:us"
] | null | In this paper, we introduce Chinese AI and Law challenge dataset (CAIL2018),
the first large-scale Chinese legal dataset for judgment prediction. CAIL contains more than 2.6 million
criminal cases published by the Supreme People's Court of China, which are several times larger than other
datasets in existing works on j... | @misc{xiao2018cail2018,
title={CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction},
author={Chaojun Xiao and Haoxi Zhong and Zhipeng Guo and Cunchao Tu and Zhiyuan Liu and Maosong Sun and Yansong Feng and Xianpei Han and Zhen Hu and Heng Wang and Jianfeng Xu},
year={2018},
eprint={180... | 7 | 156 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: chinese-ai-and-law-cail-2018
pretty_name: CAIL 2018
tags:
- judgement-prediction
... | 3,793 | [
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-0.00621... |
cmu_hinglish_dog | 2023-03-17T10:14:14.000Z | [
"task_categories:translation",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:hi",
"license:cc-by-sa-3.0",
"license:gfdl",
... | null | This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English only versions. Can be used for Translating between the two. | @inproceedings{cmu_dog_emnlp18,
title={A Dataset for Document Grounded Conversations},
author={Zhou, Kangyan and Prabhumoye, Shrimai and Black, Alan W},
year={2018},
booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}
}
@inproceedings{khanuja-etal-2020-glu... | 4 | 156 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
- hi
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- multilingual
- translation
pretty_name: CMU Document Grounded Conversations
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_id... | 8,126 | [
[
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-0.060211181640625,
0.02569580078125,
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0... |
code_x_glue_cc_code_completion_line | 2023-06-01T14:59:47.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:code",
"license:c-uda",
"re... | null | Complete the unfinished line given previous context. Models are evaluated by exact match and edit similarity.
We propose line completion task to test model's ability to autocomplete a line. Majority code completion systems behave well in token level completion, but fail in completing an unfinished line like a method ca... | @article{raychev2016probabilistic,
title={Probabilistic Model for Code with Decision Trees},
author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
journal={ACM SIGPLAN Notices},
pages={731--747},
year={2016},
publisher={ACM New York, NY, USA}
}
@inproceedings{allamanis2013mining,
title={Mining Source Code Rep... | 1 | 156 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: CodeXGlueCcCodeCompletionLine
dataset_info:
- ... | 12,887 | [
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-0.035247802734375,
0.00419235... |
HugoLaurencon/libri_light | 2022-05-10T15:51:37.000Z | [
"region:us"
] | HugoLaurencon | Libri-light is a large dataset of 60K hours of unlabelled speech from audiobooks in English.
It is a benchmark for the training of automatic speech recognition (ASR) systems with limited or no supervision. | @INPROCEEDINGS{librilight,
author={J. Kahn and M. Rivière and W. Zheng and E. Kharitonov and Q. Xu and P. E. Mazaré and J. Karadayi and V. Liptchinsky and R. Collobert and C. Fuegen and T. Likhomanenko and G. Synnaeve and A. Joulin and A. Mohamed and E. Dupoux},
booktitle={ICASSP 2020 - 2020 IEEE International Conf... | 2 | 156 | 2022-05-09T14:31:34 | Entry not found | 15 | [
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0.016998291015625,
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0.0379... |
allenai/prosocial-dialog | 2023-02-03T07:58:29.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:dialogue-generation",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categorie... | allenai | null | null | 67 | 156 | 2022-10-30T04:24:12 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- machine-generated
license: cc-by-4.0
multilinguality:
- monolingual
pretty_name: ProsocialDialog
size_categories:
- 10K<n<100K
- 100K<n<1M
source_datasets:
- original
- extended|social_bias_frames
tags:
- dialogue
- dialogue saf... | 3,762 | [
[
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0.044219970703125,
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-0.050201416015625,
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bigbio/pdr | 2022-12-22T15:46:14.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | The corpus of plant-disease relation consists of plants and diseases and their relation to PubMed abstract.
The corpus consists of about 2400 plant and disease entities and 300 annotated relations from 179 abstracts. | @article{kim2019corpus,
title={A corpus of plant--disease relations in the biomedical domain},
author={Kim, Baeksoo and Choi, Wonjun and Lee, Hyunju},
journal={PLoS One},
volume={14},
number={8},
pages={e0221582},
year={2019},
publisher={Public Library of Science San Francisco, CA USA}
} | 1 | 156 | 2022-11-13T22:11:20 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: PDR
homepage: http://gcancer.org/pdr/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- EVENT_EXTRACTION
- COREFERENCE_RESOLUTION
---
# Datase... | 1,026 | [
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olm/olm-wikipedia-20221220 | 2022-12-29T03:12:35.000Z | [
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"pretraining",
"language modelling",
"wikipedia",
"web",
"region:us"
] | olm | null | null | 2 | 156 | 2022-12-22T17:38:13 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: OLM December 2022 Wikipedia
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- pretraining
- language modelling
- wikipedia
- web
task_categories: []
task_ids: []
---
# Datas... | 500 | [
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atokforps/chunk-t1 | 2023-03-09T20:48:30.000Z | [
"region:us"
] | atokforps | null | null | 1 | 156 | 2023-02-25T11:01:46 | Entry not found | 15 | [
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0.0379... |
skeskinen/TinyStories-GPT4 | 2023-05-20T19:00:22.000Z | [
"region:us"
] | skeskinen | null | null | 12 | 156 | 2023-05-20T18:58:41 | ---
dataset_info:
features:
- name: story
dtype: string
- name: summary
dtype: string
- name: source
dtype: string
- name: prompt
dtype: string
- name: words
sequence: string
- name: features
sequence: string
splits:
- name: train
num_bytes: 3680196493
num_examples: 274... | 554 | [
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... |
ascent_kb | 2022-11-03T16:30:39.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"knowledge-base",
"arxiv:2011.00905",
"region:us"
] | null | This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline (https://ascent.mpi-inf.mpg.de/). | @InProceedings{nguyen2021www,
title={Advanced Semantics for Commonsense Knowledge Extraction},
author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},
year={2021},
booktitle={The Web Conference 2021},
} | 2 | 155 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: ascentkb
pretty_name: Ascent KB
tags:
- knowledge-base
dataset_info:
- config_n... | 8,478 | [
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... |
conll2000 | 2023-04-05T10:02:23.000Z | [
"language:en",
"region:us"
] | null | Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence
He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:
[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP onl... | @inproceedings{tksbuchholz2000conll,
author = "Tjong Kim Sang, Erik F. and Sabine Buchholz",
title = "Introduction to the CoNLL-2000 Shared Task: Chunking",
editor = "Claire Cardie and Walter Daelemans and Claire
Nedellec and Tjong Kim Sang, Erik",
booktitle = "Proceedings of ... | 2 | 155 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: conll-2000-1
pretty_name: CoNLL-2000
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': ''''''
'1': '#'
'2': $
'3': (
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SetFit/imdb | 2022-01-19T20:49:40.000Z | [
"region:us"
] | SetFit | null | null | 2 | 155 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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ywchoi/pubmed_abstract_5 | 2022-09-13T01:07:12.000Z | [
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TREC-AToMiC/AToMiC-Qrels-v0.2 | 2023-02-14T21:31:18.000Z | [
"license:cc-by-sa-4.0",
"region:us"
] | TREC-AToMiC | null | null | 1 | 155 | 2023-01-24T13:11:24 | ---
dataset_info:
features:
- name: text_id
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splits:
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num_bytes: 789840
num_examples: 9873
- name: validation
num_bytes: 1424080
num_examples: 17801
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yuan-yang/MALLS-v0 | 2023-10-25T20:16:00.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | yuan-yang | null | null | 0 | 155 | 2023-05-31T19:01:19 | ---
license: cc-by-nc-4.0
viewer: true
task_categories:
- text-generation
language:
- en
pretty_name: MALLS NL-FOL Pairs 34K
size_categories:
- 10K<n<100K
---
# MALLS NL-FOL Pairs
## Dataset details
MALLS (large language **M**odel gener**A**ted natural-**L**anguage-to-first-order-**L**ogic pair**S**)
consists of ... | 2,268 | [
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lampent/IRFL | 2023-06-02T15:02:05.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"figurative-language",
"multimodal-figurative-language",
" commonsense-reasoning",
"visual-reasoning",
"arxiv:2303.15445",
"region:us"
] | lampent | null | null | 1 | 155 | 2023-06-01T09:34:13 | ---
license: cc-by-4.0
language:
- en
tags:
- figurative-language
- multimodal-figurative-language
- ' commonsense-reasoning'
- visual-reasoning
size_categories:
- 1K<n<10K
---
# Dataset Card for IRFL
- [Dataset Description](#dataset-description)
- [Leaderboards](#leaderboards)
- [Colab notebook code for IRFL eva... | 4,948 | [
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jondurbin/airoboros-2.2 | 2023-10-03T19:01:21.000Z | [
"license:other",
"region:us"
] | jondurbin | null | null | 2 | 155 | 2023-10-03T18:46:53 | ---
license: other
---
## Overview
This dataset is mostly a continuation of https://hf.co/datasets/jondurbin/airoboros-2.1, with some notable additions and fixes.
- Some of the content is "toxic"/"harmful", and contains profanity and other types of sensitive content.
- None of the content or views contained in text ... | 4,615 | [
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tyzhu/synpre_set_1M | 2023-10-04T13:26:19.000Z | [
"region:us"
] | tyzhu | null | null | 0 | 155 | 2023-10-04T13:12:37 | ---
dataset_info:
features:
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num_bytes: 1218382220
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num_bytes: 12163626
num_examples: 10000
download_size: 8496414
dataset_size: 1230545846
---
# Dataset Card f... | 471 | [
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germaner | 2023-01-25T14:30:52.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"license:apache-2.0",
"region:us"
] | null | GermaNER is a freely available statistical German Named Entity Tagger based on conditional random fields(CRF). The tagger is trained and evaluated on the NoSta-D Named Entity dataset, which was used in the GermEval 2014 for named entity recognition. The tagger comes close to the performance of the best (proprietary) sy... | @inproceedings{Benikova2015GermaNERFO,
title={GermaNER: Free Open German Named Entity Recognition Tool},
author={Darina Benikova and S. Yimam and Prabhakaran Santhanam and Chris Biemann},
booktitle={GSCL},
year={2015}
} | 0 | 154 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: GermaNER
dataset_info:
features:
... | 13,508 | [
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OGB/ogbg-molhiv | 2023-02-07T16:39:46.000Z | [
"task_categories:graph-ml",
"license:mit",
"region:us"
] | OGB | null | null | 2 | 154 | 2022-07-06T15:28:13 | ---
license: mit
task_categories:
- graph-ml
---
# Dataset Card for ogbg-molhiv
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [External Use... | 4,486 | [
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ywchoi/pubmed_abstract_7 | 2022-09-13T01:12:17.000Z | [
"region:us"
] | ywchoi | null | null | 0 | 154 | 2022-09-13T01:10:37 | Entry not found | 15 | [
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bigbio/anat_em | 2022-12-22T15:43:16.000Z | [
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | bigbio | The extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 documents (approx. 250,000 words) manually annotated to identify over 13,000 mentions of anatomical entities. Each annotation is assigned one of 12 granularity-based types such as Cellular component, Tissue and Organ, defined with reference to the ... | @article{pyysalo2014anatomical,
title={Anatomical entity mention recognition at literature scale},
author={Pyysalo, Sampo and Ananiadou, Sophia},
journal={Bioinformatics},
volume={30},
number={6},
pages={868--875},
year={2014},
publisher={Oxford University Press}
} | 0 | 154 | 2022-11-13T18:26:03 |
---
language:
- en
bigbio_language:
- English
license: cc-by-sa-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_SA_3p0
pretty_name: AnatEM
homepage: http://nactem.ac.uk/anatomytagger/#AnatEM
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for An... | 1,139 | [
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metaeval/reclor | 2023-05-31T09:59:42.000Z | [
"language:en",
"license:other",
"region:us"
] | metaeval | null | null | 2 | 154 | 2023-03-23T16:31:55 | ---
license: other
language:
- en
---
https://whyu.me/reclor/
```bib
@inproceedings{yu2020reclor,
author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
booktitle = {International Conference on Lea... | 407 | [
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BelleGroup/school_math_0.25M | 2023-04-08T03:55:03.000Z | [
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:gpl-3.0",
"region:us"
] | BelleGroup | null | null | 65 | 154 | 2023-04-02T06:57:09 | ---
license: gpl-3.0
task_categories:
- text2text-generation
language:
- zh
size_categories:
- 100K<n<1M
---
# School Math 0.25M
## 内容
包含约25万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的中文数学题数据，包含解题过程。
注意：此数据集是由ChatGPT产生的，未经过严格校验，题目或解题过程可能包含错误。使用过程中请注意这一点。
## 样例
```
{
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vmalperovich/SST5 | 2023-05-25T00:10:29.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | vmalperovich | This data collection contains all the data used in our learning question classification experiments(see [1]), which has question class definitions, the training and testing question sets, examples of preprocessing the questions, feature definition scripts and examples of semantically related word features.
This work h... | """
_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/SST-5/raw/main/train.csv"
_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/SST-5/raw/main/test.csv"
_VALID_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/SST-5/raw/main/validation.csv"
CATEGORY_MAPPING = {'0': 0,
... | 0 | 154 | 2023-05-24T23:31:48 | ---
task_categories:
- text-classification
language:
- en
pretty_name: sst-5
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base t... | 1,640 | [
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jondurbin/airoboros-gpt4-1.4.1 | 2023-06-26T09:56:34.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | jondurbin | null | null | 36 | 154 | 2023-06-25T10:12:03 | ---
license: cc-by-nc-4.0
---
The same as 1.4, but with coding updates:
- rosettacode instructions were removed, due to a few issues found when spot-checking examples
- limited the coding examples to fewer languages, to test if a more focused dataset would produce better results | 281 | [
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argilla/llama-2-banking-fine-tune | 2023-07-28T06:24:22.000Z | [
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | argilla | null | null | 7 | 154 | 2023-07-28T06:24:20 | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for llama-2-banking-fine-tune
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or... | 10,691 | [
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legacy107/cpgQA | 2023-08-27T07:19:43.000Z | [
"region:us"
] | legacy107 | null | null | 0 | 154 | 2023-08-27T07:19:40 | ---
configs:
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data_files:
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path: data/train-*
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dataset_info:
features:
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- name: answer_start
dtype: int64
- name: question
dtype: string
- name: context
dtype: string
splits:
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chiragtubakad/chart-to-table-mix | 2023-09-05T05:48:07.000Z | [
"region:us"
] | chiragtubakad | null | null | 0 | 154 | 2023-09-05T05:47:46 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: image
- name: text
dtype: string
splits:
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num_bytes: 102169807.41570717
num_examples: 2245
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TheAIchemist13/hindi_asr_dataset | 2023-10-18T10:17:02.000Z | [
"region:us"
] | TheAIchemist13 | null | null | 0 | 154 | 2023-10-04T10:24:40 | ---
configs:
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data_files:
- split: train
path: data/train-*
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path: data/test-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcriptions
dtype: string
splits:
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num_bytes: 24441695.0
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HumanCompatibleAI/ppo-Pendulum-v1 | 2023-10-04T16:52:12.000Z | [
"region:us"
] | HumanCompatibleAI | null | null | 0 | 154 | 2023-10-04T16:52:08 | ---
dataset_info:
features:
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sequence:
sequence: float32
- name: acts
sequence:
sequence: float32
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sequence: string
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dtype: bool
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sequence: float32
splits:
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num_bytes: 2575710
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erhwenkuo/wikinews-zhtw | 2023-10-10T04:06:53.000Z | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:zh",
"license:cc-by-sa-3.0",
"region:us"
] | erhwenkuo | null | null | 0 | 154 | 2023-10-10T03:55:49 | ---
dataset_info:
config_name: '20231001'
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 13647957
num_examples: 9827
download_size: 8803739
dataset_size: 13647957
configs:
- ... | 2,466 | [
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DDSC/partial-danish-gigaword-no-twitter | 2023-03-13T14:01:53.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"region:us"
] | DDSC | null | null | 3 | 153 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- da
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: Danish Gigaword Corpus (no Twitter)
language... | 18,937 | [
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GEM/conversational_weather | 2022-10-24T15:30:13.000Z | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"data-to-text",
"region:us"
] | GEM | The Conversational Weather dataset is designed for generation of responses to weather queries based on a structured input data. The input allows specifying data attributes such as dates, times, locations, weather conditions, and errors, and also offers control over structure of response through discourse relations such... | @inproceedings{balakrishnan-etal-2019-constrained,
title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
author = "Balakrishnan, Anusha and
Rao, Jinfeng and
Upasani, Kartikeya and
White, Michael and
Subba, Rajen",
booktitle = "Proceedings... | 1 | 153 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: conversational_weather
tags:
- data-to-text
---
# Dataset Card for GEM/conver... | 19,853 | [
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Chris1/cityscapes_segmentation | 2022-11-03T19:43:00.000Z | [
"region:us"
] | Chris1 | null | null | 1 | 153 | 2022-11-03T19:26:00 | Entry not found | 15 | [
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bigbio/bionlp_st_2013_cg | 2022-12-22T15:43:57.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | bigbio | the Cancer Genetics (CG) is a event extraction task and a main task of the BioNLP Shared Task (ST) 2013.
The CG task is an information extraction task targeting the recognition of events in text,
represented as structured n-ary associations of given physical entities. In addition to
addressing the cancer domain, the CG... | @inproceedings{pyysalo-etal-2013-overview,
title = "Overview of the Cancer Genetics ({CG}) task of {B}io{NLP} Shared Task 2013",
author = "Pyysalo, Sampo and
Ohta, Tomoko and
Ananiadou, Sophia",
booktitle = "Proceedings of the {B}io{NLP} Shared Task 2013 Workshop",
month = aug,
year = ... | 2 | 153 | 2022-11-13T22:07:03 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: GENIA_PROJECT_LICENSE
pretty_name: BioNLP 2013 CG
homepage: https://github.com/openbiocorpora/bionlp-st-2013-cg
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- EVENT_EXTRACTION
- NAMED_ENTITY_... | 1,736 | [
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0... |
shossain/govreport-qa-no-pad-16384 | 2023-10-15T03:15:18.000Z | [
"region:us"
] | shossain | null | null | 0 | 153 | 2023-10-03T22:11:19 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 801880268.1341531
num_examples: 6483
download_size: 86514138
dataset_size: 801880268.1341531
configs:
- config_name: def... | 562 | [
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electricity_load_diagrams | 2022-11-18T20:00:21.000Z | [
"task_categories:time-series-forecasting",
"task_ids:univariate-time-series-forecasting",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"license:unknown",
"region:us"
] | null | This new dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014. | @inproceedings{10.1145/3209978.3210006,
author = {Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao},
title = {Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks},
year = {2018},
isbn = {9781450356572},
publisher = {Association for Computing Machinery},
ad... | 5 | 152 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language: []
license:
- unknown
multilinguality:
- monolingual
pretty_name: Electricity Load Diagrams
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
dat... | 8,823 | [
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hausa_voa_topics | 2023-01-25T14:31:55.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ha",
"license:unknown",
"region:us"
] | null | A collection of news article headlines in Hausa from VOA Hausa.
Each headline is labeled with one of the following classes: Nigeria,
Africa, World, Health or Politics.
The dataset was presented in the paper:
Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and
Distant Supervision for Multilingual Tran... | @inproceedings{hedderich-etal-2020-transfer,
title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages",
author = "Hedderich, Michael A. and
Adelani, David and
Zhu, Dawei and
Alabi, Jesujoba and
Markus, Udia and
Klakow... | 0 | 152 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ha
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: Hausa Voa News Topic Classification Datase... | 3,841 | [
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TheBritishLibrary/EThOS-PhD-metadata | 2022-07-23T21:14:57.000Z | [
"task_categories:text-classification",
"task_categories:fill-mask",
"task_ids:multi-label-classification",
"task_ids:masked-language-modeling",
"multilinguality:monolingual",
"language:en",
"region:us"
] | TheBritishLibrary | The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service.
We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787.
Thesis metadata from every PhD-awarding university in t... | \
@misc{british library_genre,
title={UK Doctoral Thesis Metadata from EThOS},
url={UK Doctoral Thesis Metadata from EThOS},
author={{British Library} and {Rosie, Heather}},
year={2021}} | 1 | 152 | 2022-03-02T23:29:22 | ---
annotations_creators: []
language:
- en
language_creators: []
license: []
multilinguality:
- monolingual
pretty_name: EThOS PhD metadata
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-classification
- fill-mask
task_ids:
- multi-label-classification
- masked-language-modeling
---
# Datase... | 4,729 | [
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anab/copa-sse | 2022-10-26T01:53:17.000Z | [
"task_categories:text2text-generation",
"task_categories:multiple-choice",
"task_ids:explanation-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"commonsense reasoning",
"... | anab | null | null | 3 | 152 | 2022-10-25T07:11:33 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- mit
multilinguality:
- monolingual
pretty_name: Semi-structured Explanations for Commonsense Reasoning
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- commonsense reasoning
- explanation
- graph-based reasoning
... | 8,064 | [
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0.... |
albertvillanova/medmnist-v2 | 2023-05-30T05:40:52.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"task_ids:multi-label-image-classification",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"medical",
"arxiv:2110.14795",
"region:us"... | albertvillanova | MedMNIST v2 is a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. | @article{medmnistv2,
title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
journal={Scientific Data},
volume={10},... | 3 | 152 | 2023-05-29T09:00:40 | ---
language: en
license: cc-by-4.0
multilinguality:
- monolingual
pretty_name: MedMNIST v2
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
- multi-label-image-classification
paperswithcode_id: medmnist-v2
tags:
- medical
---
... | 5,145 | [
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tilyupo/trivia_qa | 2023-08-03T17:00:54.000Z | [
"region:us"
] | tilyupo | null | null | 0 | 152 | 2023-08-02T19:44:00 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: question
dtype: string
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dtype: string
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coastalcph/medical-bios | 2023-10-11T11:56:36.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-sa-4.0",
"medical",
"region:us"
] | coastalcph | NA | NA | 1 | 152 | 2023-10-09T10:54:50 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- en
tags:
- medical
pretty_name: medical-bios
size_categories:
- 1K<n<10K
---
# Dataset Description
The dataset comprises English biographies labeled with occupations and binary genders.
This is an occupation classification task, where bi... | 3,018 | [
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result-kand2-sdxl-wuerst-karlo/1f4e3f67 | 2023-10-11T01:25:02.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 152 | 2023-10-11T01:25:02 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
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num_bytes: 260
num_examples: 10
download_size: 1484
dataset_size: 260
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "1f4e3f6... | 455 | [
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GEM/mlb_data_to_text | 2022-10-24T15:30:20.000Z | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:other",
"data-to-text",
"region:us"
] | GEM | The MLB dataset for data to text generation contains Major League Baseball games statistics and
their human-written summaries. | @inproceedings{puduppully-etal-2019-data,
title = "Data-to-text Generation with Entity Modeling",
author = "Puduppully, Ratish and
Dong, Li and
Lapata, Mirella",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "... | 1 | 151 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- other
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: mlb_data_to_text
tags:
- data-to-text
---
# Dataset Card for GEM/mlb_data_to_text
#... | 46,529 | [
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bigbio/hprd50 | 2022-12-22T15:44:46.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers
referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format,
splitting each abstract into sentences, and in each sentence there may be entities and
interactions between those entities. In this particular da... | @article{fundel2007relex,
title={RelEx—Relation extraction using dependency parse trees},
author={Fundel, Katrin and K{\"u}ffner, Robert and Zimmer, Ralf},
journal={Bioinformatics},
volume={23},
number={3},
pages={365--371},
year={2007},
publisher={Oxford University Press}
} | 1 | 151 | 2022-11-13T22:08:57 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: HPRD50
homepage:
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRACTION
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for HPRD50
## Dataset Description
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logo-wizard/modern-logo-dataset | 2023-05-09T13:40:55.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-3.0",
"doi:10.57967/hf/0592",
"region:us"
] | logo-wizard | null | null | 11 | 151 | 2023-04-27T20:26:59 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
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num_bytes: 209598433
num_examples: 803
download_size: 208886058
dataset_size: 209598433
license: cc-by-nc-3.0
task_categories:
- text-to-image
language:
- en
size_categories:
- n<1K
---
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Docugami/dfm-csl-small-benchmark | 2023-10-04T08:44:17.000Z | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"docugami",
"dfm-csl",
"xml-knowledge-graphs",
"region:us"
] | Docugami | null | null | 4 | 151 | 2023-05-30T01:00:38 | ---
license: mit
language:
- en
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
dataset_info:
features:
- name: Text
dtype: string
- name: Small Chunk
dtype: string
- name: Ground Truth
dtype: string
- name: docugami/dfm-cs-small
... | 1,091 | [
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spacemanidol/product-search-corpus | 2023-08-11T17:15:55.000Z | [
"region:us"
] | spacemanidol | null | 0 | 151 | 2023-08-09T16:19:25 | Entry not found | 15 | [
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eckendoerffer/news_fr | 2023-10-06T02:36:21.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:fr",
"license:cc-by-3.0",
"news",
"media",
"Press",
"region:us"
] | eckendoerffer | null | null | 0 | 151 | 2023-09-26T18:36:19 | ---
license: cc-by-3.0
task_categories:
- text-generation
language:
- fr
tags:
- news
- media
- Press
size_categories:
- 1M<n<10M
---
# NEWS FR
There is an open-access [dataset on BnF / Gallica](https://transfert.bnf.fr/link/3a04ea3f-dbe8-4a4a-a302-913a89c3a7a8) comprising nearly a hundred newspapers from the print med... | 3,485 | [
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Luciya/llama-2-nuv-intent-noE | 2023-10-10T06:04:10.000Z | [
"region:us"
] | Luciya | null | null | 0 | 151 | 2023-10-10T06:02:19 | ---
dataset_info:
features:
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splits:
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num_examples: 1585
download_size: 0
dataset_size: 711010
configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "llama-2-nuv-intent-noE"
[More Inf... | 442 | [
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yentinglin/TC-Eval | 2023-11-02T13:20:32.000Z | [
"task_categories:question-answering",
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:zh",
"region:us"
] | yentinglin | null | null | 1 | 151 | 2023-10-24T15:58:14 | ---
task_categories:
- question-answering
- text-classification
language:
- zh
pretty_name: TMLU
size_categories:
- 1K<n<10K
configs:
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data_files:
- split: test
path: "fgc.jsonl"
- config_name: DRCD
data_files:
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path: "drcd.jsonl"
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hoskinson-center/proof-pile | 2023-08-19T03:24:11.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"math",
"mathematics",
"formal-mathematics",
"region:us"
] | hoskinson-center | A dataset of high quality mathematical text. | @InProceedings{huggingface:dataset,
title = {proof-pile},
author={Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski
},
year={2022}
} | 31 | 150 | 2022-08-08T20:57:56 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: [apache-2.0]
multilinguality:
- monolingual
pretty_name: proof-pile
size_categories: []
source_datasets: []
tags:
- math
- mathematics
- formal-mathematics
task_categories:
- text-generation
task_ids:
- language-modeling
---
#... | 5,045 | [
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TREC-AToMiC/AToMiC-Images-v0.2 | 2023-02-14T21:29:39.000Z | [
"size_categories:100M<n<1B",
"license:cc-by-sa-4.0",
"arxiv:2103.01913",
"region:us"
] | TREC-AToMiC | null | null | 1 | 150 | 2023-01-14T08:12:44 | ---
dataset_info:
features:
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alpayariyak/IAM_Sentences_LLaVA | 2023-05-19T22:04:20.000Z | [
"region:us"
] | alpayariyak | null | null | 0 | 150 | 2023-05-19T21:46:41 | ---
dataset_info:
features:
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dtype: image
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splits:
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num_bytes: 1053875995.077
num_examples: 5663
download_size: 1128902513
dataset_size: 1053875995.077
---
# Dataset Card for "IAM_Sentences_LLaVA"
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llm-book/jawiki-sentences | 2023-10-25T15:22:05.000Z | [
"size_categories:10M<n<100M",
"language:ja",
"license:cc-by-sa-3.0",
"license:gfdl",
"region:us"
] | llm-book | null | null | 1 | 150 | 2023-06-03T03:02:08 | ---
language:
- ja
size_categories:
- 10M<n<100M
license:
- cc-by-sa-3.0
- gfdl
dataset_info:
features:
- name: text
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splits:
- name: train
num_bytes: 3569619848
num_examples: 24387500
download_size: 1297833377
dataset_size: 3569619848
---
# Dataset Card for llm-book/jawiki-sentenc... | 711 | [
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BAAI/COIG-PC-core | 2023-09-25T10:33:33.000Z | [
"language:zh",
"license:unknown",
"region:us"
] | BAAI | null | null | 9 | 150 | 2023-09-19T06:24:01 | ---
extra_gated_heading: "Acknowledge license to accept the repository"
extra_gated_prompt: |
北京智源人工智能研究院（以下简称“我们”或“研究院”）通过BAAI DataHub（data.baai.ac.cn）和COIG-PC HuggingFace仓库（https://huggingface.co/datasets/BAAI/COIG-PC）向您提供开源数据集（以下或称“数据集”），您可通过下载的方式获取您所需的开源数据集，并在遵守各原始数据集使用规则前提下，基于学习、研究、商业等目的使用相关数据... | 11,700 | [
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deal_or_no_dialog | 2022-11-18T19:57:59.000Z | [
"task_categories:conversational",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1706.05125",
"region:us"
] | null | A large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach anagreement (o a deal) via natural language dialogue. | @article{lewis2017deal,
title={Deal or no deal? end-to-end learning for negotiation dialogues},
author={Lewis, Mike and Yarats, Denis and Dauphin, Yann N and Parikh, Devi and Batra, Dhruv},
journal={arXiv preprint arXiv:1706.05125},
year={2017}
} | 5 | 149 | 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:
- conversational
task_ids: []
paperswithcode_id: negotiation-dialogues-dataset
pretty_name: Deal or No ... | 5,332 | [
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eu_regulatory_ir | 2022-11-18T20:01:28.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"document-to-document-retrieval",
"arxiv:21... | null | EURegIR: Regulatory Compliance IR (EU/UK) | @inproceedings{chalkidis-etal-2021-regir,
title = "Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations",
author = "Chalkidis, Ilias and Fergadiotis, Emmanouil and Manginas, Nikos and Katakalou, Eva, and Malakasiotis, Prodromos",
... | 1 | 149 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
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size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
paperswithcode_id: null
pretty_name: the RegIR datasets
tags:
-... | 9,824 | [
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nli_tr | 2023-06-01T14:59:47.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
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"source_datasets:... | null | \
The Natural Language Inference in Turkish (NLI-TR) is a set of two large scale datasets that were obtained by translating the foundational NLI corpora (SNLI and MNLI) using Amazon Translate. | \
@inproceedings{budur-etal-2020-data,
title = "Data and Representation for Turkish Natural Language Inference",
author = "Budur, Emrah and
\"{O}zçelik, Rıza and
G\"{u}ng\"{o}r, Tunga",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMN... | 5 | 149 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- tr
license:
- cc-by-3.0
- cc-by-4.0
- cc-by-sa-3.0
- mit
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|snli
- extended|multi_nli
task_categories:
- text-classification
task_i... | 9,508 | [
[
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0.0226... |
strombergnlp/x-stance | 2022-10-25T21:45:25.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"language:de",
"language:fr",
"license:mit",
"stance-detection",
"arxiv:2003.08385",
"region:us"
] | strombergnlp | The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. The comments are partly German, partly French and Italian. The data have been extracted from the Swiss voting advice platform Smartvote. | @inproceedings{vamvas2020xstance,
author = "Vamvas, Jannis and Sennrich, Rico",
title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection",
booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \& 16th Conference on Natural Language Processing (KONVENS)",... | 1 | 149 | 2022-05-18T09:55:43 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
- fr
license:
- mit
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: X-Stance
tags:
- stance-detection
---
# Dataset Card for... | 4,084 | [
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vietgpt/binhvq_news_vi | 2023-03-30T18:58:53.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:vi",
"LM",
"region:us"
] | vietgpt | null | null | 0 | 149 | 2023-02-21T20:08:06 | ---
dataset_info:
features:
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splits:
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num_bytes: 8211350978.574438
num_examples: 19365593
download_size: 4780706833
dataset_size: 8211350978.574438
task_categories:
- text-generation
language:
- vi
tags:
- LM
size_categories:
- 10M<n<100M
---
# Binhvq News
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andstor/the_pile_github | 2023-03-20T23:39:53.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:2101.00027",
"arxiv:2201.07311... | andstor | The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality
datasets combined together. | @misc{gao2020pile,
title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Leo Gao and Stella Biderman and Sid Black and Laurence Golding and Travis Hoppe and Charles Foster and Jason Phang and Horace He and Anish Thite and Noa Nabeshima and Shawn Presser and Connor Leahy},
y... | 3 | 149 | 2023-03-07T15:53:05 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: The Pile GitHub
size_categories: []
source_datasets:
- original
tags: []
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids: []
---
# Dataset Card ... | 4,286 | [
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gia-project/gia-dataset | 2023-09-05T06:36:39.000Z | [
"task_categories:reinforcement-learning",
"task_categories:text-generation",
"task_categories:question-answering",
"annotations_creators:found",
"annotations_creators:machine-generated",
"source_datasets:conceptual-captions",
"source_datasets:ok-vqa",
"source_datasets:oscar",
"license:apache-2.0",
... | gia-project | GIA dataset. | null | 1 | 149 | 2023-03-09T20:58:36 | ---
license: apache-2.0
tags:
- imitation-learning
- reinforcement-learning
- text-generation
- question-answering
- generalist-agent
annotations_creators:
- found
- machine-generated
pretty_name: GIA-dataset
size_categories:
- {number_of_elements_in_dataset} # Example: n<1K, 100K<n<1M, …
source_datasets:
- conceptual... | 21,448 | [
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lytang/MeetingBank-transcript | 2023-07-17T21:05:12.000Z | [
"task_categories:summarization",
"license:cc-by-nc-sa-4.0",
"arxiv:2305.17529",
"region:us"
] | lytang | null | null | 0 | 149 | 2023-07-15T18:00:10 | ---
license: cc-by-nc-sa-4.0
task_categories:
- summarization
---
This dataset consists of transcripts from the [MeetingBank dataset](https://meetingbank.github.io/).
**Overview**
MeetingBank, a benchmark dataset created from the city councils of 6 major U.S. cities to supplement existing datasets. It contains 1,3... | 2,302 | [
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rashmi035/dataset_whisper | 2023-10-05T05:49:20.000Z | [
"region:us"
] | rashmi035 | null | null | 0 | 149 | 2023-10-05T05:48:14 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: set
dtype: string
splits:
- name: train
num_bytes: 35817014.0
num_examples: 100
- name: validation
num_bytes: 15314681.0
num_examples: 50
- name: test
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result-kand2-sdxl-wuerst-karlo/ddb740fe | 2023-10-11T04:44:46.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 149 | 2023-10-11T04:44:45 | ---
dataset_info:
features:
- name: result
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- name: id
dtype: int64
splits:
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num_bytes: 201
num_examples: 10
download_size: 1398
dataset_size: 201
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ddb740f... | 455 | [
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Arabic-Clip/Arabic_dataset_3M_translated_cleaned_v2_jsonl_format_ViT-B-16-plus-240 | 2023-10-11T16:41:17.000Z | [
"region:us"
] | Arabic-Clip | null | null | 0 | 149 | 2023-10-11T16:25:51 | Entry not found | 15 | [
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insub/imdb_prefix20_forDPO_gpt2-large-imdb-FT_siebert_sentiment-roberta-large-english | 2023-10-22T08:02:45.000Z | [
"arxiv:2305.18290",
"region:us"
] | insub | null | null | 1 | 149 | 2023-10-22T07:33:43 | ---
dataset_info:
features:
- name: text
dtype: string
- name: chosen
dtype: string
- name: rejected
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splits:
- name: train
num_bytes: 23573801
num_examples: 25000
- name: test
num_bytes: 23551578
num_examples: 25000
download_size: 28260315
dataset_size: 471253... | 1,456 | [
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ubaada/booksum-complete-cleaned | 2023-11-02T09:58:39.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"arxiv:2105.08209",
"region:us"
] | ubaada | null | null | 0 | 149 | 2023-10-28T12:13:12 | ---
task_categories:
- summarization
- text-generation
language:
- en
pretty_name: BookSum Summarization Dataset Clean
size_categories:
- 1K<n<10K
configs:
- config_name: books
data_files:
- split: train
path: "books/train.jsonl"
- split: test
path: "books/test.jsonl"
- split: validation
path: "boo... | 3,960 | [
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compguesswhat | 2023-04-05T10:02:19.000Z | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-guesswhat",
"language:en",
"license:unknown",
"region:... | null | CompGuessWhat?! is an instance of a multi-task framework for evaluating the quality of learned neural representations,
in particular concerning attribute grounding. Use this dataset if you want to use the set of games whose reference
scene is an image in VisualGenome. Visit the website for more details:... | @inproceedings{suglia2020compguesswhat,
title={CompGuessWhat?!: a Multi-task Evaluation Framework for Grounded Language Learning},
author={Suglia, Alessandro, Konstas, Ioannis, Vanzo, Andrea, Bastianelli, Emanuele, Desmond Elliott, Stella Frank and Oliver Lemon},
booktitle={Proceed... | 1 | 148 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: CompGuessWhat?!
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-guesswhat
task_categories:
- visual-question-answering
task_ids:
- visual-question-answeri... | 12,849 | [
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fake_news_english | 2023-05-30T04:42:32.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Fake news has become a major societal issue and a technical challenge for social media companies to identify. This content is difficult to identify because the term "fake news" covers intentionally false, deceptive stories as well as factual errors, satire, and sometimes, stories that a person just does not like. Addre... | @inproceedings{inproceedings,
author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussen... | 0 | 148 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Fake News English
dataset_info:
features:
- name: a... | 4,926 | [
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kor_3i4k | 2023-01-25T14:33:43.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"license:cc-by-4.0",
"arxiv:1811.04231",
... | null | This dataset is designed to identify speaker intention based on real-life spoken utterance in Korean into one of
7 categories: fragment, description, question, command, rhetorical question, rhetorical command, utterances. | @article{cho2018speech,
title={Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency},
author={Cho, Won Ik and Lee, Hyeon Seung and Yoon, Ji Won and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1811.04231},
year={2018}
} | 1 | 148 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
pretty_name: 3i4K
dataset_info:
featu... | 6,370 | [
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