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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
sinandraide/hotpot_qa_spread | 2023-10-30T20:36:25.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | sinandraide | null | null | 0 | 191 | 2023-10-30T18:13:11 | ---
task_categories:
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
This dataset is a spread version of the HotpotQA dataset. This version allows it to be compatible with Langchain's HuggingfaceLoader.
This dataset card aims to be a base template for new datasets.... | 4,535 | [
[
-0.039886474609375,
-0.0452880859375,
0.00545501708984375,
0.0262908935546875,
-0.024932861328125,
-0.005619049072265625,
-0.005771636962890625,
-0.039703369140625,
0.043243408203125,
0.06396484375,
-0.05352783203125,
-0.0716552734375,
-0.04180908203125,
0.0... |
HuggingFaceM4/NoCaps | 2022-12-14T04:08:38.000Z | [
"license:cc-by-2.0",
"region:us"
] | HuggingFaceM4 | Dubbed NoCaps, for novel object captioning at scale, NoCaps consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets.
The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes.
Since Open Im... | @inproceedings{agrawal2019nocaps,
title={nocaps: novel object captioning at scale},
author={Agrawal, Harsh and Desai, Karan and Wang, Yufei and Chen, Xinlei and Jain, Rishabh and Johnson, Mark and Batra, Dhruv and Parikh, Devi and Lee, Stefan and Anderson, Peter},
booktitle={Proceedings of the IEEE International ... | 1 | 190 | 2022-12-08T17:11:21 | ---
license: cc-by-2.0
---
# Dataset Card for NoCaps
## 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)
- [Languages](#languages)
- [Dataset Stru... | 4,857 | [
[
-0.044830322265625,
-0.030975341796875,
0.006084442138671875,
0.0140380859375,
-0.0137939453125,
0.0164031982421875,
-0.0054779052734375,
-0.04608154296875,
0.029449462890625,
0.045135498046875,
-0.057037353515625,
-0.067138671875,
-0.043182373046875,
0.0191... |
BioDEX/BioDEX-ICSR | 2023-05-30T15:20:25.000Z | [
"region:us"
] | BioDEX | null | null | 2 | 190 | 2023-04-19T11:10:45 | ---
dataset_info:
features:
- name: title
dtype: string
- name: abstract
dtype: string
- name: fulltext
dtype: string
- name: target
dtype: string
- name: pmid
dtype: string
- name: fulltext_license
dtype: string
- name: title_normalized
dtype: string
- name: issue
dtyp... | 1,524 | [
[
-0.045654296875,
0.00811767578125,
0.0224151611328125,
0.0079193115234375,
-0.0301971435546875,
0.02130126953125,
0.0382080078125,
-0.0229949951171875,
0.0643310546875,
0.0215606689453125,
-0.058074951171875,
-0.057861328125,
-0.041107177734375,
0.0020427703... |
SinKove/synthetic_mammography_csaw | 2023-10-11T21:04:10.000Z | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"license:openrail",
"medical",
"arxiv:2112.01330",
"arxiv:2307.15208",
"doi:10.57967/hf/1254",
"region:us"
] | SinKove | null | null | 16 | 190 | 2023-10-11T18:50:12 | ---
task_categories:
- image-classification
tags:
- medical
pretty_name: C
size_categories:
- 10K<n<100K
license: openrail
---
# Dataset Card for Synthetic CSAW 100k Mammograms
## Dataset Description
This is a synthetic mammogram dataset created with the latent diffusion model from *Generative AI for Medical Imaging:... | 2,079 | [
[
-0.0225677490234375,
-0.041015625,
0.04400634765625,
-0.003345489501953125,
-0.0193939208984375,
0.00824737548828125,
0.032470703125,
-0.020416259765625,
0.02337646484375,
0.049041748046875,
-0.0643310546875,
-0.0650634765625,
-0.056640625,
-0.01258087158203... |
nus-yam/bigfixes | 2023-11-01T14:56:26.000Z | [
"region:us"
] | nus-yam | null | null | 1 | 190 | 2023-10-26T05:55:10 | ---
---
pretty_name: BigFixes
description: A clean union of BigVul and CVE-Fixes.
configs:
- config_name: default
data_files:
- split: train
path: train.csv
- split: cleantest
path: clean_test.csv
- split: test
path: test.csv
---
---
# Information
This is a clean vers... | 968 | [
[
-0.03436279296875,
-0.0338134765625,
0.0089111328125,
0.0012331008911132812,
-0.032745361328125,
0.006839752197265625,
0.015411376953125,
-0.026214599609375,
0.0237579345703125,
0.04779052734375,
-0.05548095703125,
-0.04644775390625,
-0.0275726318359375,
0.0... |
turk | 2022-11-18T21:56:55.000Z | [
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region:us"
] | null | TURKCorpus is a dataset for evaluating sentence simplification systems that focus on lexical paraphrasing,
as described in "Optimizing Statistical Machine Translation for Text Simplification". The corpus is composed of 2000 validation and 359 test original sentences that were each simplified 8 times by different annota... | @article{Xu-EtAl:2016:TACL,
author = {Wei Xu and Courtney Napoles and Ellie Pavlick and Quanze Chen and Chris Callison-Burch},
title = {Optimizing Statistical Machine Translation for Text Simplification},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year = {2016},
url ... | 3 | 189 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
paperswithcode_id: null
pretty_name: TURK
dataset_info... | 8,143 | [
[
-0.04510498046875,
-0.05853271484375,
0.0231475830078125,
0.0193939208984375,
-0.033477783203125,
-0.028167724609375,
-0.026641845703125,
-0.01824951171875,
0.029266357421875,
0.0264892578125,
-0.0556640625,
-0.051666259765625,
-0.04644775390625,
0.042663574... |
nielsr/FUNSD_layoutlmv2 | 2022-10-25T09:51:20.000Z | [
"language:en",
"arxiv:1905.13538",
"region:us"
] | nielsr | https://guillaumejaume.github.io/FUNSD/ | @article{Jaume2019FUNSDAD,
title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
year={2019},
volume={2},
pages={1-6}
} | 4 | 189 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: funsd
---
# Dataset Card for "FUNSD"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-str... | 5,642 | [
[
-0.036407470703125,
-0.0345458984375,
0.01552581787109375,
0.0134124755859375,
-0.0188140869140625,
-0.0072479248046875,
-0.0199737548828125,
-0.0294036865234375,
0.046478271484375,
0.03326416015625,
-0.06280517578125,
-0.06298828125,
-0.03277587890625,
-0.0... |
GEM/FairytaleQA | 2022-10-25T12:58:30.000Z | [
"task_categories:other",
"annotations_creators:expert-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:unknown",
"question-generation",
"arxiv:2203.13947",
"region:us"
] | GEM | \
The FairytaleQA dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of n... | \
@inproceedings{xu2022fairytaleqa,
author={Xu, Ying and Wang, Dakuo and Yu, Mo and Ritchie, Daniel and Yao, Bingsheng and Wu, Tongshuang and Zhang, Zheng and Li, Toby Jia-Jun and Bradford, Nora and Sun, Branda and Hoang, Tran Bao and Sang, Yisi and Hou, Yufang and Ma, Xiaojuan and Yang, Diyi and Peng, Nanyun and... | 4 | 189 | 2022-05-19T15:51:16 | ---
annotations_creators:
- expert-created
language_creators:
- unknown
language:
- en
license:
- unknown
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: FairytaleQA
tags:
- question-generation
---
# Dataset Card for GEM/FairytaleQA
... | 30,712 | [
[
-0.0300445556640625,
-0.067626953125,
0.029754638671875,
-0.00347900390625,
0.005558013916015625,
-0.00719451904296875,
0.0175018310546875,
-0.0208587646484375,
0.004276275634765625,
0.035247802734375,
-0.0670166015625,
-0.043914794921875,
-0.017913818359375,
... |
bigbio/gnormplus | 2023-02-17T14:55:04.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | We re-annotated two existing gene corpora. The BioCreative II GN corpus is a widely used data set for benchmarking GN
tools and includes document-level annotations for a total of 543 articles (281 in its training set; and 262 in test).
The Citation GIA Test Collection was recently created for gene indexing at the NLM a... | @Article{Wei2015,
author={Wei, Chih-Hsuan and Kao, Hung-Yu and Lu, Zhiyong},
title={GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains},
journal={BioMed Research International},
year={2015},
month={Aug},
day={25},
publisher={Hindawi Publishing Corporation},
volume={2015},
pages={91... | 2 | 189 | 2022-11-13T22:08:50 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: GNormPlus
homepage: https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY... | 1,751 | [
[
-0.032379150390625,
-0.01352691650390625,
0.01280975341796875,
-0.005970001220703125,
-0.040313720703125,
0.0082244873046875,
-0.0150299072265625,
-0.04351806640625,
0.0445556640625,
0.02655029296875,
-0.0188751220703125,
-0.05438232421875,
-0.050201416015625,
... |
seungheondoh/LP-MusicCaps-MC | 2023-08-01T03:52:24.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"music",
"text-to-music",
"music-to-text",
"art",
"arxiv:2307.16372",
"region:us"
] | seungheondoh | null | null | 5 | 189 | 2023-07-26T04:19:27 | ---
license: mit
language:
- en
tags:
- music
- text-to-music
- music-to-text
- art
pretty_name: LP-MusicCaps-MC
size_categories:
- 1K<n<10K
---
======================================
**!important**: Be careful when using `caption_attribute_prediction` (We don't recommend to use)!
===================================... | 6,788 | [
[
-0.04119873046875,
-0.0306243896484375,
0.0217132568359375,
0.02679443359375,
-0.0277557373046875,
0.006145477294921875,
-0.0264892578125,
-0.02252197265625,
0.0433349609375,
0.0550537109375,
-0.08367919921875,
-0.06951904296875,
-0.029876708984375,
0.008888... |
TheBritishLibrary/blbooksgenre | 2023-06-01T14:59:51.000Z | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:topic-classification",
"task_ids:multi-label-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creator... | TheBritishLibrary | This dataset contains metadata for resources belonging to the British Library’s digitised printed books (18th-19th century) collection (bl.uk/collection-guides/digitised-printed-books).
This metadata has been extracted from British Library catalogue records.
The metadata held within our main catalogue is updated regula... | @misc{british library_genre,
title={ 19th Century Books - metadata with additional crowdsourced annotations},
url={https://doi.org/10.23636/BKHQ-0312},
author={{British Library} and Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annel... | 4 | 188 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- en
- fr
- nl
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text-generation
- fill-mask
tas... | 25,266 | [
[
-0.0321044921875,
-0.01666259765625,
-0.00505828857421875,
0.00411224365234375,
-0.01428985595703125,
-0.01068115234375,
-0.01800537109375,
-0.040985107421875,
0.02557373046875,
0.06201171875,
-0.045196533203125,
-0.06451416015625,
-0.040374755859375,
0.0306... |
HumanCompatibleAI/ppo-seals-Hopper-v1 | 2023-09-27T07:06:10.000Z | [
"region:us"
] | HumanCompatibleAI | null | null | 0 | 188 | 2023-09-26T14:42:54 | ---
dataset_info:
features:
- name: obs
sequence:
sequence: float64
- name: acts
sequence:
sequence: float32
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float32
splits:
- name: train
num_bytes: 57153894
num_examples: 104
do... | 544 | [
[
-0.034942626953125,
-0.00031065940856933594,
0.00431060791015625,
0.01332855224609375,
-0.02557373046875,
-0.01213836669921875,
0.057525634765625,
-0.007358551025390625,
0.059783935546875,
0.05279541015625,
-0.057098388671875,
-0.047210693359375,
-0.061126708984... |
sam1120/terrain-jackal-morning-100_v0.1 | 2023-09-28T04:12:08.000Z | [
"region:us"
] | sam1120 | null | null | 0 | 188 | 2023-09-28T04:04:00 | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: labels
dtype: image
splits:
- name: train
num_bytes: 275219262.0
num_examples: 100
download_size: 77868688
dataset_size: 275219262.0
---
# Dataset Card for "terrain-jackal-morning-100_v0.1"
[More Information needed](htt... | 422 | [
[
-0.0506591796875,
-0.03167724609375,
0.004695892333984375,
0.046600341796875,
-0.0206756591796875,
-0.00925445556640625,
0.0239715576171875,
-0.004856109619140625,
0.06610107421875,
0.045318603515625,
-0.06536865234375,
-0.0570068359375,
-0.040191650390625,
... |
EduardoPacheco/gpt4v-LAION-discord | 2023-10-16T16:05:32.000Z | [
"region:us"
] | EduardoPacheco | null | null | 0 | 188 | 2023-10-08T22:47:05 | ---
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
num_bytes: 36014887.0
num_examples: 136
download_size: 0
dataset_size: 36... | 592 | [
[
-0.03472900390625,
-0.01113128662109375,
0.011505126953125,
0.007232666015625,
-0.023681640625,
0.00039386749267578125,
0.0284576416015625,
0.00927734375,
0.04583740234375,
0.0264892578125,
-0.0635986328125,
-0.056396484375,
-0.0309295654296875,
-0.013008117... |
flores | 2023-06-01T14:59:47.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|wikipedia",
"source_datasets:extended|opus_gnome",
"source_datasets:extended|opus_ubuntu",
"source_datasets:extended|open_subti... | null | Evaluation datasets for low-resource machine translation: Nepali-English and Sinhala-English. | @misc{guzmn2019new,
title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English},
author={Francisco Guzman and Peng-Jen Chen and Myle Ott and Juan Pino and Guillaume Lample and Philipp Koehn and Vishrav Chaudhary and Marc'Aurelio Ranzato},
year={2019},
epr... | 3 | 187 | 2022-03-02T23:29:22 | ---
pretty_name: Flores
annotations_creators:
- found
language_creators:
- found
language:
- en
- ne
- si
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|wikipedia
- extended|opus_gnome
- extended|opus_ubuntu
- extended|open_subtitles
- extended|paracrawl
- ex... | 7,223 | [
[
-0.032928466796875,
-0.035430908203125,
0.003627777099609375,
0.021514892578125,
-0.01983642578125,
-0.005626678466796875,
-0.033966064453125,
-0.030517578125,
0.049957275390625,
0.026153564453125,
-0.057373046875,
-0.061920166015625,
-0.03668212890625,
0.01... |
nid989/FNC-1 | 2021-12-27T11:04:06.000Z | [
"region:us"
] | nid989 | null | null | 3 | 187 | 2022-03-02T23:29:22 | ### Dataset Summary
The data provided is (headline, body, stance) instances, where the stance is one of {unrelated, discuss, agree, disagree}.
**Input**
* A headline and a body text - either from the same news article or from two different articles.
**Output**
* Classify the stance of the body text relative to the ... | 1,364 | [
[
-0.0296783447265625,
-0.04766845703125,
0.0172271728515625,
0.0293731689453125,
-0.0171051025390625,
-0.00873565673828125,
-0.01611328125,
-0.0206756591796875,
0.0323486328125,
0.021514892578125,
-0.032562255859375,
-0.07110595703125,
-0.052947998046875,
0.0... |
Chinese-Vicuna/guanaco_belle_merge_v1.0 | 2023-03-30T07:49:30.000Z | [
"language:zh",
"language:en",
"language:ja",
"license:gpl-3.0",
"region:us"
] | Chinese-Vicuna | null | null | 79 | 187 | 2023-03-30T07:29:07 | ---
license: gpl-3.0
language:
- zh
- en
- ja
---
Thanks for [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) and [Belle Dataset](https://huggingface.co/datasets/BelleGroup/generated_train_0.5M_CN)
This dataset was created by merging the above two datasets in a certain format so that t... | 416 | [
[
-0.010772705078125,
0.004123687744140625,
0.01873779296875,
0.0391845703125,
-0.00885772705078125,
-0.0283050537109375,
0.00403594970703125,
-0.036346435546875,
0.037811279296875,
0.037628173828125,
-0.04351806640625,
-0.050048828125,
-0.0212249755859375,
-0... |
GATE-engine/mini_imagenet | 2023-06-06T11:44:26.000Z | [
"region:us"
] | GATE-engine | null | null | 1 | 187 | 2023-06-05T10:59:59 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 2533332667.0
num_examples: 38400
- name: validation
num_bytes: 623452894.0
num_examples: 9600
- name: test
num_bytes: 781497663.0
num_examples: 12000
downloa... | 539 | [
[
-0.0556640625,
-0.01546478271484375,
0.004581451416015625,
0.0028324127197265625,
-0.02325439453125,
-0.01453399658203125,
0.0281219482421875,
-0.01178741455078125,
0.07354736328125,
0.02947998046875,
-0.055633544921875,
-0.04052734375,
-0.044586181640625,
-... |
ibm-nasa-geospatial/hls_burn_scars | 2023-09-26T16:08:32.000Z | [
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"doi:10.57967/hf/0956",
"region:us"
] | ibm-nasa-geospatial | This dataset contains Harmonized Landsat and Sentinel-2 imagery of burn scars and the associated masks for the years 2018-2021 over the contiguous United States. There are 804 512x512 scenes. Its primary purpose is for training geospatial machine learning models. | @software{HLS_Foundation_2023,
author = {Phillips, Christopher and Roy, Sujit and Ankur, Kumar and Ramachandran, Rahul},
doi = {10.57967/hf/0956},
month = aug,
title = {{HLS Foundation Burnscars Dataset}},
url = {https://huggingface.co/ibm-nasa-geospatial/hls_burn_scars},
year = {2023}... | 9 | 187 | 2023-06-14T02:23:32 | ---
size_categories:
- n<1K
license: cc-by-4.0
language:
- en
---
# Dataset Card for HLS Burn Scar Scenes
## Dataset Description
- **Homepage: https://huggingface.co/datasets/nasa-impact/hls_burn_scars**
- **Point of Contact: Dr. Christopher Phillips (cep0013@uah.edu)**
### Dataset Summary
This dataset contains H... | 2,390 | [
[
-0.0135650634765625,
-0.014556884765625,
0.0360107421875,
0.00908660888671875,
-0.0144195556640625,
0.0198822021484375,
0.01392364501953125,
-0.0156707763671875,
0.038818359375,
0.0589599609375,
-0.03564453125,
-0.054290771484375,
-0.0281982421875,
-0.020217... |
Sp1786/multiclass-sentiment-analysis-dataset | 2023-06-25T08:01:27.000Z | [
"task_categories:text-classification",
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] | Sp1786 | null | null | 0 | 187 | 2023-06-21T11:21:31 | ---
license: apache-2.0
task_categories:
- text-classification
- translation
language:
- en
tags:
- code
pretty_name: multiclass-sentiment-analysis-dataset
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- *... | 1,721 | [
[
-0.038177490234375,
-0.0298004150390625,
-0.003589630126953125,
0.027130126953125,
-0.0323486328125,
0.0037860870361328125,
-0.0172271728515625,
-0.020172119140625,
0.049041748046875,
0.04052734375,
-0.0634765625,
-0.08062744140625,
-0.052947998046875,
0.002... |
hdparmar/irish-tunes-spectrograms | 2023-10-15T02:37:32.000Z | [
"task_categories:text-to-image",
"task_categories:text-to-audio",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | hdparmar | null | null | 0 | 187 | 2023-10-12T21:06:15 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 16031533765.152
num_examples: 51217
download_size: 15902802902
dataset_size: 16031533765.152
license: apache-2.0
task_categories:
- text-to-image
- text-to-audio
language:
- e... | 2,827 | [
[
-0.040008544921875,
0.008056640625,
0.007404327392578125,
0.017669677734375,
-0.045989990234375,
0.0035953521728515625,
-0.03485107421875,
-0.0450439453125,
0.037750244140625,
0.05963134765625,
-0.034423828125,
-0.0748291015625,
-0.017425537109375,
0.0031833... |
kelm | 2022-11-18T20:16:37.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"data-to-text-generation",
"arxiv:2010.12688",
"region:us"
] | null | Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 dis... | @misc{agarwal2020large,
title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
year={2020},
eprint={2010.12688},
archivePrefix={arXiv},
primary... | 6 | 186 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: kelm
pretty_name: Corpus for Knowledge-Enhanced Language Model Pre-training ... | 5,092 | [
[
-0.026275634765625,
-0.058441162109375,
0.012359619140625,
-0.0025424957275390625,
-0.0139007568359375,
0.00601959228515625,
-0.0311126708984375,
-0.0233306884765625,
0.009033203125,
0.03973388671875,
-0.0256805419921875,
-0.06109619140625,
-0.045318603515625,
... |
qed | 2022-11-03T16:31:09.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|natural_questions",
"language:en",
"license:unknown",
"explanations-in-question-a... | null | QED, is a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. It is an expertannotated dataset of QED explanations... | @misc{lamm2020qed,
title={QED: A Framework and Dataset for Explanations in Question Answering},
author={Matthew Lamm and Jennimaria Palomaki and Chris Alberti and Daniel Andor and Eunsol Choi and Livio Baldini Soares and Michael Collins},
year={2020},
eprint={2009.06354},
archivePrefix={arXiv},
... | 2 | 186 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|natural_questions
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: qed
pretty_name: QED
tags:... | 4,785 | [
[
-0.032989501953125,
-0.033447265625,
0.0185089111328125,
0.00311279296875,
-0.0159454345703125,
0.01313018798828125,
-0.001224517822265625,
-0.0097808837890625,
0.032257080078125,
0.040130615234375,
-0.0750732421875,
-0.065185546875,
-0.029876708984375,
0.00... |
khalidalt/model-written-evals | 2023-07-02T20:24:29.000Z | [
"task_categories:multiple-choice",
"task_categories:zero-shot-classification",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:multiple-choice-coreference-resolution",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monol... | khalidalt | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @misc{perez2022discovering,
doi = {10.48550/ARXIV.2212.09251},
url = {https://arxiv.org/abs/2212.09251},
author = {Perez, Ethan and Ringer, Sam and Lukošiūtė, Kamilė and Nguyen, Karina and Chen, Edwin and Heiner, Scott and Pettit, Craig and Olsson, Catherine and Kundu, Sandipan and Kadavath, Saurav and Jones, And... | 0 | 186 | 2023-03-17T18:42:09 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Evaluations from "Discovering Language Model Behaviors with Model-Written
Evaluations"
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- g... | 4,134 | [
[
-0.018798828125,
-0.033966064453125,
0.035308837890625,
-0.00036907196044921875,
0.0236663818359375,
0.0024929046630859375,
0.0016460418701171875,
-0.0262603759765625,
0.01102447509765625,
0.034332275390625,
-0.042755126953125,
-0.0478515625,
-0.035125732421875,... |
the_pile_books3 | 2023-11-02T15:05:12.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",... | null | This dataset is Shawn Presser's work and is part of EleutherAi/The Pile dataset. This dataset contains all of bibliotik in plain .txt form, aka 197,000 books processed in exactly the same way as did for bookcorpusopen (a.k.a. books1). seems to be similar to OpenAI's mysterious "books2" dataset referenced in their paper... | @article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
... | 125 | 185 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Books3
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
viewer: f... | 5,740 | [
[
-0.03961181640625,
-0.036590576171875,
-0.0071258544921875,
-0.0007724761962890625,
-0.013397216796875,
0.0031795501708984375,
-0.01137542724609375,
-0.024383544921875,
0.027587890625,
0.053680419921875,
-0.051605224609375,
-0.054351806640625,
-0.03375244140625,... |
zest | 2022-11-18T22:05:40.000Z | [
"task_categories:question-answering",
"task_categories:token-classification",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"lang... | null | ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of
the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include
classification, typed entity extraction and relationship extraction, and each task is paired... | @inproceedings{weller-etal-2020-learning,
title = "Learning from Task Descriptions",
author = "Weller, Orion and
Lourie, Nicholas and
Gardner, Matt and
Peters, Matthew",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
mon... | 1 | 185 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- token-classification
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode... | 6,378 | [
[
-0.003936767578125,
-0.048675537109375,
0.029510498046875,
0.00852203369140625,
-0.00484466552734375,
-0.01465606689453125,
-0.0281219482421875,
-0.04144287109375,
0.018829345703125,
0.035064697265625,
-0.06298828125,
-0.0543212890625,
-0.043853759765625,
0.... |
nlphuji/whoops | 2023-08-18T23:06:45.000Z | [
"annotations_creators:crowdsourced",
"language_creators:found",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"commonsense-reasoning",
"explanation-generation",
"visual-commonsense-reasoning",
"compositionality",
"image-generation",
"visual-question-answering(VQA)",
... | nlphuji | null | null | 11 | 185 | 2023-01-28T22:04:03 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
paperswithcode_id: whoops
pretty_name: WHOOPS!
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- commonsense-reasoning
- explanation-generation
- visual-commonsense-reasoning
- compositionality
- image-generation
- visual... | 7,812 | [
[
-0.037567138671875,
-0.022705078125,
0.0212554931640625,
0.023468017578125,
-0.0246124267578125,
0.0011892318725585938,
-0.0055694580078125,
-0.052703857421875,
0.015625,
0.029876708984375,
-0.058929443359375,
-0.05010986328125,
-0.052032470703125,
0.0213317... |
C-MTEB/CMNLI | 2023-07-27T17:35:51.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 185 | 2023-07-27T17:35:44 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
dataset_info:
features:
- name: sent1
sequence: string
- name: sent2
sequence: string
- name: labels
sequence: int64
splits:
- name: validation
num_bytes: 1349125
num_examples: 1
downloa... | 522 | [
[
-0.04296875,
-0.00884246826171875,
0.022369384765625,
0.00514984130859375,
-0.01462554931640625,
0.0009632110595703125,
0.00879669189453125,
-0.0137176513671875,
0.06854248046875,
0.027679443359375,
-0.07659912109375,
-0.058624267578125,
-0.03173828125,
-0.0... |
M-A-D/Mixed-Arabic-Datasets-Repo | 2023-10-16T21:25:35.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:translation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:fill-mask",
"size_categories:1B<n... | M-A-D | null | null | 12 | 185 | 2023-08-27T01:19:21 | ---
language:
- ar
size_categories:
- 1B<n<10B
task_categories:
- text-classification
- question-answering
- translation
- summarization
- conversational
- text-generation
- text2text-generation
- fill-mask
pretty_name: Mixed Arabic Datasets (MAD) Corpus
dataset_info:
- config_name: Ara--Ali-C137--Hindawi-Books-dataset... | 15,953 | [
[
-0.04974365234375,
-0.035736083984375,
-0.01329803466796875,
0.021148681640625,
-0.01751708984375,
0.0235595703125,
-0.01241302490234375,
-0.039825439453125,
0.03240966796875,
0.0130462646484375,
-0.03582763671875,
-0.064697265625,
-0.05218505859375,
0.01789... |
eduagarcia/OSCAR-2301-pt_dedup | 2023-08-28T16:55:02.000Z | [
"region:us"
] | eduagarcia | null | null | 0 | 185 | 2023-08-27T23:52:48 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 61846407893
num_examples: 10888966
download_size: 28809168123
dataset_size: 61846407893
---
# Dataset Card for "OSCAR-2301_dedup"
[More Information needed](https://github.com/hu... | 404 | [
[
-0.046844482421875,
-0.0026187896728515625,
0.0098724365234375,
0.01175689697265625,
-0.02166748046875,
0.00022363662719726562,
0.0362548828125,
-0.0096588134765625,
0.0657958984375,
0.039337158203125,
-0.045135498046875,
-0.03826904296875,
-0.055877685546875,
... |
TearGosling/limarp_standardized | 2023-09-05T01:01:28.000Z | [
"region:us"
] | TearGosling | null | null | 1 | 185 | 2023-09-05T00:59:45 | Entry not found | 15 | [
[
-0.02142333984375,
-0.014984130859375,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.04656982421875,
0.052520751953125,
0.00506591796875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
definite_pronoun_resolution | 2023-04-05T10:04:44.000Z | [
"task_categories:token-classification",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Composed by 30 students from one of the author's undergraduate classes. These
sentence pairs cover topics ranging from real events (e.g., Iran's plan to
attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
Batman) and purely imaginary situations, largely reflecting the pop culture as
perceived... | @inproceedings{rahman2012resolving,
title={Resolving complex cases of definite pronouns: the winograd schema challenge},
author={Rahman, Altaf and Ng, Vincent},
booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
p... | 3 | 184 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- word-sense-disambiguation
paperswithcode_id: definite-pronoun-resolu... | 7,185 | [
[
-0.040618896484375,
-0.05078125,
0.01413726806640625,
0.007747650146484375,
-0.0174560546875,
-0.0077667236328125,
-0.0389404296875,
-0.03228759765625,
0.041046142578125,
0.037200927734375,
-0.052093505859375,
-0.060943603515625,
-0.039215087890625,
0.019317... |
sanchit-gandhi/whisper-jax-test-files | 2023-04-19T12:07:08.000Z | [
"region:us"
] | sanchit-gandhi | null | null | 2 | 184 | 2023-04-19T11:49:16 | ---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 271658381.0
num_examples: 2
download_size: 113444578
dataset_size: 271658381.0
---
# Dataset Card for "whisper-jax-test-files"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONT... | 371 | [
[
-0.034210205078125,
-0.0082855224609375,
0.0272979736328125,
0.0293121337890625,
-0.00434112548828125,
0.01041412353515625,
0.00872802734375,
-0.01666259765625,
0.041778564453125,
0.0289459228515625,
-0.0694580078125,
-0.053466796875,
-0.0394287109375,
-0.00... |
flaviagiammarino/path-vqa | 2023-06-03T19:02:04.000Z | [
"task_categories:visual-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"medical",
"arxiv:2003.10286",
"region:us"
] | flaviagiammarino | null | null | 5 | 184 | 2023-06-02T12:03:51 | ---
license: mit
task_categories:
- visual-question-answering
language:
- en
tags:
- medical
pretty_name: PathVQA
paperswithcode_id: pathvqa
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name... | 4,290 | [
[
-0.021728515625,
-0.05560302734375,
0.0258636474609375,
-0.0112457275390625,
-0.0154876708984375,
-0.0165863037109375,
0.02191162109375,
-0.01904296875,
0.00911712646484375,
0.042877197265625,
-0.053863525390625,
-0.05072021484375,
-0.020782470703125,
0.0049... |
liyucheng/arxiv-march-2023 | 2023-06-02T17:59:35.000Z | [
"region:us"
] | liyucheng | null | null | 0 | 184 | 2023-06-02T17:59:27 | ---
dataset_info:
features:
- name: entry_id
dtype: string
- name: published
dtype: string
- name: title
dtype: string
- name: authors
sequence: string
- name: primary_category
dtype: string
- name: categories
sequence: string
- name: text
dtype: string
splits:
- name: tr... | 595 | [
[
-0.043365478515625,
-0.00384521484375,
0.019683837890625,
0.029571533203125,
-0.022918701171875,
-0.0201263427734375,
0.049224853515625,
-0.0165252685546875,
0.04638671875,
0.044891357421875,
-0.055145263671875,
-0.0526123046875,
-0.03924560546875,
-0.007106... |
explodinggradients/WikiEval | 2023-09-18T15:12:16.000Z | [
"region:us"
] | explodinggradients | null | null | 0 | 184 | 2023-08-24T10:01:45 | ---
dataset_info:
features:
- name: answer
dtype: string
- name: question
dtype: string
- name: context_v1
sequence: string
- name: context_v2
sequence: string
- name: ungrounded_answer
dtype: string
- name: source
dtype: string
- name: poor_answer
dtype: string
splits:
-... | 1,173 | [
[
-0.05499267578125,
-0.06402587890625,
0.02252197265625,
0.008544921875,
-0.0198211669921875,
-0.017364501953125,
0.00991058349609375,
-0.0183563232421875,
0.0504150390625,
0.040191650390625,
-0.04876708984375,
-0.03533935546875,
-0.037017822265625,
-0.007495... |
giganion/pippa_roleplay_standardized | 2023-09-04T20:07:55.000Z | [
"region:us"
] | giganion | null | null | 1 | 184 | 2023-09-04T20:04:59 | Entry not found | 15 | [
[
-0.021392822265625,
-0.01494598388671875,
0.05718994140625,
0.028839111328125,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.01702880859375,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.03790... |
jjonhwa/SECOND_KQ_V2 | 2023-09-13T07:04:47.000Z | [
"region:us"
] | jjonhwa | null | null | 0 | 184 | 2023-09-13T01:44:49 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: score
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 686780736
num_examples: 86975
download_size: 276955064
dataset_s... | 505 | [
[
-0.019287109375,
-0.0027523040771484375,
0.018646240234375,
0.0087127685546875,
-0.02838134765625,
0.00830078125,
0.042510986328125,
-0.0161285400390625,
0.043792724609375,
0.041015625,
-0.0528564453125,
-0.043731689453125,
-0.04229736328125,
-0.035736083984... |
medal | 2023-06-13T12:39:11.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:unknown",
"disambiguation",
"region:us"
] | null | A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate | @inproceedings{wen-etal-2020-medal,
title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
author = "Wen, Zhi and
Lu, Xing Han and
Reddy, Siva",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
mon... | 10 | 183 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: medal
pretty_name: MeDAL
tags:
- disambiguation
dataset_i... | 10,886 | [
[
-0.023101806640625,
-0.038726806640625,
0.021484375,
0.00933837890625,
-0.032806396484375,
0.0015506744384765625,
0.003047943115234375,
-0.039825439453125,
0.0478515625,
0.037200927734375,
-0.04241943359375,
-0.06689453125,
-0.052825927734375,
0.028610229492... |
tweets_ar_en_parallel | 2023-01-25T14:54:55.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"language:en",
"license:apache-2.0",
"tweets-translation... | null | Twitter users often post parallel tweets—tweets that contain the same content but are
written in different languages. Parallel tweets can be an important resource for developing
machine translation (MT) systems among other natural language processing (NLP) tasks. This
resource is a result of a generic m... | @inproceedings{Mubarak2020bilingualtweets,
title={Constructing a Bilingual Corpus of Parallel Tweets},
author={Mubarak, Hamdy and Hassan, Sabit and Abdelali, Ahmed},
booktitle={Proceedings of 13th Workshop on Building and Using Comparable Corpora (BUCC)},
address={Marseille, France},
year={2020}
} | 3 | 183 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- no-annotation
language_creators:
- found
language:
- ar
- en
license:
- apache-2.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: bilingual-corpus-of-arabic-english-para... | 6,119 | [
[
-0.037322998046875,
-0.03302001953125,
0.005786895751953125,
0.041656494140625,
-0.0221710205078125,
0.04498291015625,
-0.031219482421875,
-0.019287109375,
0.037017822265625,
0.020263671875,
-0.042816162109375,
-0.0772705078125,
-0.06695556640625,
0.02319335... |
Dahoas/static-hh | 2023-03-06T00:11:55.000Z | [
"region:us"
] | Dahoas | null | null | 14 | 183 | 2023-02-15T03:53:36 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 143664651
num_examples: 96256
- name: test
num_bytes: 7649255
num_examples: 5103
download... | 471 | [
[
-0.059051513671875,
-0.057037353515625,
0.004302978515625,
-0.01445770263671875,
-0.037109375,
0.01213836669921875,
0.011016845703125,
-0.047119140625,
0.059844970703125,
0.044708251953125,
-0.07159423828125,
-0.0165557861328125,
-0.0117645263671875,
0.01338... |
GSQA/speech-alpaca-gpt4-unit | 2023-08-09T15:29:24.000Z | [
"region:us"
] | GSQA | null | null | 1 | 183 | 2023-08-08T18:13:35 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: speech_input
dtype: string
- name: input_speaker
dtype: string
- name: output_speaker
dtype: string
- name: mhubert_layer11_code1000_input_code
dtype... | 830 | [
[
-0.0484619140625,
-0.03662109375,
0.018951416015625,
0.0166473388671875,
-0.0218048095703125,
-0.0005254745483398438,
0.00379180908203125,
-0.017974853515625,
0.06439208984375,
0.0224151611328125,
-0.054473876953125,
-0.055694580078125,
-0.048187255859375,
-... |
result-kand2-sdxl-wuerst-karlo/2ddeba07 | 2023-10-09T21:37:39.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 183 | 2023-10-09T21:37:38 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 200
num_examples: 10
download_size: 1374
dataset_size: 200
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "2ddeba0... | 455 | [
[
-0.049072265625,
-0.01528167724609375,
0.02117919921875,
0.0271453857421875,
-0.01873779296875,
-0.004650115966796875,
0.0418701171875,
-0.01451873779296875,
0.056549072265625,
0.035552978515625,
-0.046875,
-0.04864501953125,
-0.045166015625,
-0.010391235351... |
covid_qa_castorini | 2022-11-03T16:30:54.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2004.1... | null | CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. | @article{tang2020rapidly,
title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},
author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},
journal={arXiv preprint arXiv:2004.11339},
year={2020}
} | 0 | 182 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id: covidqa
pretty_name: Cov... | 6,911 | [
[
-0.024139404296875,
-0.0621337890625,
-0.00977325439453125,
0.00998687744140625,
-0.00887298583984375,
0.015869140625,
-0.014007568359375,
-0.026519775390625,
0.02862548828125,
0.0090789794921875,
-0.053802490234375,
-0.045867919921875,
-0.0239410400390625,
... |
pn_summary | 2023-01-25T14:42:36.000Z | [
"task_categories:summarization",
"task_categories:text-classification",
"task_ids:news-articles-summarization",
"task_ids:news-articles-headline-generation",
"task_ids:text-simplification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:mon... | null | A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification.
It is imperative to consider that t... | @article{pnSummary, title={Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization},
author={Mehrdad Farahani, Mohammad Gharachorloo, Mohammad Manthouri},
year={2020},
eprint={2012.11204},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 4 | 182 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- fa
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
- text-classification
task_ids:
- news-articles-summarization
- news-articles-headline-generation
- text-si... | 9,315 | [
[
-0.050048828125,
-0.0460205078125,
0.013397216796875,
0.0276641845703125,
-0.042388916015625,
-0.0005660057067871094,
-0.01332855224609375,
-0.0181121826171875,
0.04901123046875,
0.026336669921875,
-0.0296783447265625,
-0.06243896484375,
-0.04638671875,
0.03... |
species_800 | 2023-06-16T11:33:29.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition,
which we here use to identifynames of species and other taxa in text. The tool, SPECIES, is more than an order of
magnitude faster and as accurate as existing tools. The precision and recall was asses... | @article{pafilis2013species,
title={The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text},
author={Pafilis, Evangelos and Frankild, Sune P and Fanini, Lucia and Faulwetter, Sarah and Pavloudi, Christina and Vasileiadou, Aikaterini and Arvanitidis, Christo... | 2 | 182 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: species800
dataset_info:
... | 5,802 | [
[
-0.02618408203125,
-0.03155517578125,
0.0235137939453125,
0.021148681640625,
-0.02032470703125,
0.0027523040771484375,
-0.0265350341796875,
-0.0286712646484375,
0.05316162109375,
0.025421142578125,
-0.03167724609375,
-0.0714111328125,
-0.051788330078125,
0.0... |
Fraser/short-jokes | 2021-02-24T08:31:31.000Z | [
"region:us"
] | Fraser | Copy of [Kaggle dataset](https://www.kaggle.com/abhinavmoudgil95/short-jokes), adding to Huggingface for ease of use.
Description from Kaggle:
Context
Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generat... | null | 5 | 182 | 2022-03-02T23:29:22 | Copy of [Kaggle dataset](https://www.kaggle.com/abhinavmoudgil95/short-jokes), adding to Huggingface for ease of use.
Description from Kaggle:
Context
Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generat... | 1,123 | [
[
-0.026123046875,
-0.07037353515625,
0.0298004150390625,
0.051666259765625,
-0.047882080078125,
-0.024932861328125,
-0.02178955078125,
-0.0333251953125,
0.03912353515625,
0.040740966796875,
-0.05029296875,
-0.0269775390625,
-0.036407470703125,
0.0283355712890... |
Zaid/coqa_expanded | 2021-10-04T18:48:15.000Z | [
"region:us"
] | Zaid | \\nCoQA: A Conversational Question Answering Challenge | \\n@InProceedings{SivaAndAl:Coca,
author = {Siva, Reddy and Danqi, Chen and Christopher D., Manning},
title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering},
journal = { arXiv},
year = {2018},
} | 2 | 182 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.021392822265625,
-0.01494598388671875,
0.05718994140625,
0.028839111328125,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.01702880859375,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.03790... |
qwant/squad_fr | 2023-04-19T14:37:09.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:extended|squad",
... | qwant | SQuAD-fr is a French translated version of the Stanford Question Answering Dataset (SQuAD), the reference corpus to evaluate question answering models' performances in English.
It consists of 100K question-answer pairs on 500+ articles derived from the original English dataset and represents a large-scale dataset for c... | @inproceedings{cattan:hal-03336060,
TITLE = {{On the Usability of Transformers-based models for a French Question-Answering task}},
AUTHOR = {Cattan, Oralie and Servan, Christophe and Rosset, Sophie},
URL = {https://hal.archives-ouvertes.fr/hal-03336060},
BOOKTITLE = {{Recent Advances in Natural Language Proces... | 6 | 182 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- fr
license:
- cc-by-4.0
multilinguality:
- monolingual
- translation
paperswithcode_id: squad
pretty_name: SQuAD-fr
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad
task_categories:
- question-answering
task_... | 5,765 | [
[
-0.05126953125,
-0.054656982421875,
0.006969451904296875,
0.016815185546875,
-0.0023860931396484375,
0.0017881393432617188,
-0.0228271484375,
-0.025543212890625,
0.0202484130859375,
0.039459228515625,
-0.07049560546875,
-0.057647705078125,
-0.022613525390625,
... |
tner/mit_restaurant | 2022-08-10T11:25:17.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"region:us"
] | tner | [mit_restaurant NER dataset](https://groups.csail.mit.edu/sls/downloads/) | null | 2 | 182 | 2022-07-16T11:12:45 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: MIT Restaurant
---
# Dataset Card for "tner/mit_restaurant"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asah... | 1,539 | [
[
-0.02618408203125,
-0.0279693603515625,
0.004634857177734375,
-0.004489898681640625,
-0.007541656494140625,
-0.01305389404296875,
-0.0037288665771484375,
0.0013370513916015625,
0.0301055908203125,
0.033935546875,
-0.0219879150390625,
-0.07147216796875,
-0.033905... |
cdminix/libritts-aligned | 2023-10-11T19:46:28.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"annotations_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"speech",
"audio",
"automatic-speech-recognition",
"text-to-speech",
"arxiv:1904.02882",
"arxiv:2211.16049",
"region:us"
] | cdminix | Dataset used for loading TTS spectrograms and waveform audio with alignments and a number of configurable "measures", which are extracted from the raw audio. | @article{zen2019libritts,
title={LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech},
author={Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and Weiss, Ron J and Jia, Ye and Chen, Zhifeng and Wu, Yonghui},
journal={Interspeech},
year={2019}
}
@article{https://doi.org/10.48550/arxiv.2211.1604... | 4 | 182 | 2023-05-14T10:29:46 | ---
pretty_name: LibriTTS Corpus with Forced Alignments
annotations_creators:
- crowdsourced
language: en
tags:
- speech
- audio
- automatic-speech-recognition
- text-to-speech
license:
- cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
extra_gated_prompt: "When using this dataset to download ... | 6,442 | [
[
-0.0198516845703125,
-0.0258636474609375,
0.0033512115478515625,
-0.0033779144287109375,
-0.005611419677734375,
-0.002899169921875,
-0.023406982421875,
-0.0124664306640625,
0.023284912109375,
0.022796630859375,
-0.04949951171875,
-0.0435791015625,
-0.01429748535... |
allenai/peS2o | 2023-07-18T20:01:34.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:10B<n<100B",
"source_datasets:allenai/s2orc",
"language:en",
"license:odc-by",
"biology",
"chemistry",
"engineering",
"computer science",
"physics",
"material science",
"math",
"psychology",
"economics",
"... | allenai | null | @techreport{peS2o,
author = {Luca Soldaini and Kyle Lo},
year = 2023,
title = {{peS2o (Pretraining Efficiently on S2ORC) Dataset}},
institution = {{Allen Institute for AI}},
note = {ODC-By, \\url{https://github.com/allenai/pes2o}}
} | 90 | 182 | 2023-06-29T04:54:16 | ---
license:
- odc-by
task_categories:
- text-generation
- fill-mask
language:
- en
tags:
- biology
- chemistry
- engineering
- computer science
- physics
- material science
- math
- psychology
- economics
- political science
- business
- geology
- sociology
- geography
- environmental science
- art
- history
- philoso... | 6,929 | [
[
-0.0122833251953125,
-0.044342041015625,
0.047515869140625,
0.0011577606201171875,
-0.01318359375,
-0.018218994140625,
-0.018096923828125,
-0.051513671875,
0.0198516845703125,
0.0154876708984375,
-0.0189666748046875,
-0.039093017578125,
-0.063232421875,
0.02... |
composite/pauq | 2023-10-28T09:35:31.000Z | [
"region:us"
] | composite | Pauq is a first Russian text-to-SQL dataset translated from original Spider dataset
with corrections and refinements of question, queries and databases. | @inproceedings{bakshandaeva-etal-2022-pauq,
title = "{PAUQ}: Text-to-{SQL} in {R}ussian",
author = "Bakshandaeva, Daria and
Somov, Oleg and
Dmitrieva, Ekaterina and
Davydova, Vera and
Tutubalina, Elena",
booktitle = "Findings of the Association for Computational Linguistics: EMNL... | 2 | 182 | 2023-07-17T09:45:17 | ---
dataset_info:
- config_name: ru_os
features:
- name: id
dtype: string
- name: db_id
dtype: string
- name: source
dtype: string
- name: type
dtype: string
- name: question
dtype: string
- name: query
dtype: string
- name: sql
sequence: string
- name: question_toks
se... | 5,809 | [
[
-0.03363037109375,
-0.035736083984375,
0.01107025146484375,
0.0168914794921875,
-0.0126953125,
0.016815185546875,
-0.0235137939453125,
-0.0254058837890625,
0.045318603515625,
0.045074462890625,
-0.0633544921875,
-0.08258056640625,
-0.051971435546875,
0.00584... |
manu/project_gutenberg | 2023-09-07T15:33:32.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:fr",
"language:en",
"language:zh",
"language:pt",
"language:pl",
"language:nl",
"language:ru",
"language:sv",
"language:it",
"language:de",
"language:es",
"region:us"
] | manu | null | null | 2 | 182 | 2023-09-07T14:14:10 | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: de
num_bytes: 1070196924
num_examples: 3131
- name: en
num_bytes: 25616345280
num_examples: 61340
- name: es
num_bytes: 496728508
num_examples: 1202
- name: fr
num_bytes: 2... | 2,927 | [
[
-0.021942138671875,
-0.001529693603515625,
-0.00366973876953125,
0.01293182373046875,
-0.039154052734375,
-0.00946044921875,
0.0087738037109375,
-0.0218658447265625,
0.0006318092346191406,
0.07177734375,
-0.02459716796875,
-0.05804443359375,
-0.03240966796875,
... |
vlsp-2023-vllm/grade_12_exams | 2023-09-30T08:28:29.000Z | [
"region:us"
] | vlsp-2023-vllm | null | null | 0 | 182 | 2023-09-10T19:54:48 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: metadata
struct:
- name: grade
dtype: int64
- name: language
dtype: string
- name: subject
dtype: string
- name: choices
struct:
- name: label
sequence: string
... | 805 | [
[
-0.021575927734375,
-0.0164031982421875,
-0.00394439697265625,
0.042022705078125,
-0.0145416259765625,
-0.01161956787109375,
0.025726318359375,
-0.0012187957763671875,
0.0264434814453125,
0.022247314453125,
-0.042633056640625,
-0.067138671875,
-0.021804809570312... |
great_code | 2022-11-18T20:05:00.000Z | [
"task_categories:table-to-text",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | The dataset for the variable-misuse task, described in the ICLR 2020 paper 'Global Relational Models of Source Code' [https://openreview.net/forum?id=B1lnbRNtwr]
This is the public version of the dataset used in that paper. The original, used to produce the graphs in the paper, could not be open-sourced due to licensi... | @inproceedings{DBLP:conf/iclr/HellendoornSSMB20,
author = {Vincent J. Hellendoorn and
Charles Sutton and
Rishabh Singh and
Petros Maniatis and
David Bieber},
title = {Global Relational Models of Source Code},
booktitle = {8th International Confere... | 1 | 181 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: null
pretty_name: GREAT
dataset_info:
features:
- nam... | 4,054 | [
[
-0.041229248046875,
-0.0347900390625,
0.01189422607421875,
0.014739990234375,
-0.0199737548828125,
0.006320953369140625,
-0.01947021484375,
-0.0330810546875,
0.044921875,
0.04693603515625,
-0.05657958984375,
-0.07562255859375,
-0.04510498046875,
-0.005992889... |
webnlg/challenge-2023 | 2023-03-10T11:22:40.000Z | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-db_pedia",
"source_datasets:original",
"language:br",
"language:cy",
"language:... | webnlg | The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_... | @inproceedings{web_nlg,
author = {Claire Gardent and
Anastasia Shimorina and
Shashi Narayan and
Laura Perez{-}Beltrachini},
editor = {Regina Barzilay and
Min{-}Yen Kan},
title = {Creating Training Corpora for {NLG} Micro-Planners},
booktitle ... | 3 | 181 | 2023-03-10T08:30:03 | ---
annotations_creators:
- found
language_creators:
- crowdsourced
language:
- br
- cy
- ga
- mt
- ru
license:
- cc-by-sa-3.0
- cc-by-nc-sa-4.0
- gfdl
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-db_pedia
- original
task_categories:
- tabular-to-text
task_ids:
- rdf-t... | 16,781 | [
[
-0.030548095703125,
-0.050689697265625,
0.002628326416015625,
0.03076171875,
-0.01036834716796875,
-0.006412506103515625,
-0.031036376953125,
-0.0406494140625,
0.01079559326171875,
0.03582763671875,
-0.0589599609375,
-0.069580078125,
-0.0229339599609375,
0.0... |
instruction-tuning-sd/cartoonization | 2023-05-11T15:16:08.000Z | [
"task_categories:image-to-image",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | instruction-tuning-sd | null | null | 5 | 181 | 2023-03-17T09:13:34 | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: cartoonized_image
dtype: image
splits:
- name: train
num_bytes: 3257571330
num_examples: 5000
download_size: 3296272284
dataset_size: 3257571330
size_categories:
- 1K<n<10K
langu... | 1,315 | [
[
-0.0443115234375,
-0.02679443359375,
0.01071929931640625,
0.0199127197265625,
-0.01226806640625,
-0.0160369873046875,
-0.002105712890625,
-0.018402099609375,
0.046661376953125,
0.057220458984375,
-0.06695556640625,
-0.03350830078125,
-0.042266845703125,
0.00... |
Babelscape/multinerd | 2023-04-20T12:43:31.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"... | Babelscape | null | null | 9 | 181 | 2023-04-20T11:49:21 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name... | 5,661 | [
[
-0.057952880859375,
-0.038116455078125,
-0.0026493072509765625,
0.0124053955078125,
0.00302886962890625,
0.00031113624572753906,
-0.03582763671875,
-0.05279541015625,
0.04193115234375,
0.01458740234375,
-0.0401611328125,
-0.06103515625,
-0.03338623046875,
0.... |
pie/tacred | 2023-09-27T14:43:54.000Z | [
"region:us"
] | pie | null | null | 0 | 181 | 2023-07-06T15:44:15 | Entry not found | 15 | [
[
-0.021392822265625,
-0.01494598388671875,
0.05718994140625,
0.028839111328125,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.01702880859375,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.03790... |
teknium/openhermes | 2023-09-07T20:41:05.000Z | [
"task_categories:text-generation",
"language:eng",
"distillation",
"synthetic data",
"gpt",
"region:us"
] | teknium | null | null | 57 | 181 | 2023-09-04T01:31:26 | ---
language:
- eng
pretty_name: "OpenHermes-v1.0"
tags:
- distillation
- synthetic data
- gpt
task_categories:
- text-generation
---
# OpenHermes Dataset
The OpenHermes dataset is composed of 242,... | 1,227 | [
[
-0.045867919921875,
-0.0253143310546875,
0.0222320556640625,
-0.00652313232421875,
-0.003398895263671875,
-0.0205535888671875,
-0.01041412353515625,
-0.037933349609375,
-0.00696563720703125,
0.06304931640625,
-0.047882080078125,
-0.06317138671875,
-0.03013610839... |
warshakhan/donut_vqa_ISynHMP_all_labels_modified | 2023-09-28T08:29:22.000Z | [
"region:us"
] | warshakhan | null | null | 0 | 181 | 2023-09-28T07:48:17 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 583333339.0... | 722 | [
[
-0.01013946533203125,
-0.01514434814453125,
0.01522064208984375,
0.004070281982421875,
-0.0027523040771484375,
0.014007568359375,
0.00499725341796875,
-0.01837158203125,
0.0782470703125,
0.046630859375,
-0.0576171875,
-0.0556640625,
-0.049346923828125,
-0.01... |
hope_edi | 2023-06-01T14:59:49.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:ml",
"la... | null | A Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. | @inproceedings{chakravarthi-2020-hopeedi,
title = "{H}ope{EDI}: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion",
author = "Chakravarthi, Bharathi Raja",
booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Socia... | 1 | 180 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- ml
- ta
license:
- cc-by-4.0
multilinguality:
- monolingual
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hopeedi
p... | 11,187 | [
[
-0.0293121337890625,
-0.0404052734375,
-0.0000756978988647461,
0.0153656005859375,
-0.034698486328125,
0.0171051025390625,
-0.0225830078125,
-0.028961181640625,
0.045806884765625,
0.00954437255859375,
-0.050201416015625,
-0.061309814453125,
-0.0535888671875,
... |
wongnai_reviews | 2023-01-25T15:02:56.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:th",
"license:lgpl-3.0",
"region:us"
] | null | Wongnai's review dataset contains restaurant reviews and ratings, mainly in Thai language.
The reviews are in 5 classes ranging from 1 to 5 stars. | null | 2 | 180 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- th
license:
- lgpl-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: WongnaiReviews
dataset_info:
features:
- n... | 2,036 | [
[
-0.02783203125,
-0.0352783203125,
0.01207733154296875,
0.0251007080078125,
-0.021331787109375,
-0.0178985595703125,
-0.0237274169921875,
-0.0208587646484375,
0.04376220703125,
0.060302734375,
-0.02783203125,
-0.057281494140625,
-0.03350830078125,
0.020446777... |
nanyy1025/covid_fake_news | 2023-02-24T01:36:24.000Z | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"language:en",
"arxiv:2011.03327",
"region:us"
] | nanyy1025 | null | null | 2 | 180 | 2023-02-24T01:01:04 | ---
task_categories:
- text-classification
- zero-shot-classification
language:
- en
---
Constraint@AAAI2021 - COVID19 Fake News Detection in English
```
@misc{patwa2020fighting,
title={Fighting an Infodemic: COVID-19 Fake News Dataset},
author={Parth Patwa and Shivam Sharma and Srinivas PYKL and Vineeth Guptha and ... | 495 | [
[
-0.0159454345703125,
-0.044952392578125,
-0.00257110595703125,
0.0280303955078125,
-0.01763916015625,
0.00650787353515625,
-0.0023021697998046875,
-0.041473388671875,
0.0263519287109375,
0.00994873046875,
-0.057525634765625,
-0.036956787109375,
-0.02764892578125... |
lmsys/mt_bench_human_judgments | 2023-07-20T18:28:15.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"arxiv:2306.05685",
"region:us"
] | lmsys | null | null | 37 | 180 | 2023-07-04T14:03:03 | ---
dataset_info:
features:
- name: question_id
dtype: int64
- name: model_a
dtype: string
- name: model_b
dtype: string
- name: winner
dtype: string
- name: judge
dtype: string
- name: conversation_a
list:
- name: content
dtype: string
- name: role
dtype: strin... | 2,000 | [
[
-0.054290771484375,
-0.020843505859375,
0.0423583984375,
-0.00012564659118652344,
-0.0210113525390625,
-0.01493072509765625,
-0.012908935546875,
-0.045928955078125,
0.00891876220703125,
0.03045654296875,
-0.00264739990234375,
-0.026092529296875,
-0.0428771972656... |
result-kand2-sdxl-wuerst-karlo/02dd1f44 | 2023-10-10T00:35:21.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 180 | 2023-10-10T00:35:20 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 158
num_examples: 10
download_size: 1302
dataset_size: 158
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "02dd1f4... | 455 | [
[
-0.04888916015625,
-0.01126861572265625,
0.01401519775390625,
0.02325439453125,
-0.0172882080078125,
-0.01496124267578125,
0.03265380859375,
-0.01273345947265625,
0.048187255859375,
0.03515625,
-0.0709228515625,
-0.043304443359375,
-0.0399169921875,
-0.00218... |
yoruba_bbc_topics | 2023-01-25T15:03:35.000Z | [
"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:yo",
"license:unknown",
"region:us"
] | null | A collection of news article headlines in Yoruba from BBC Yoruba.
Each headline is labeled with one of the following classes: africa,
entertainment, health, nigeria, politics, sport or world.
The dataset was presented in the paper:
Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and
Distant Supervisi... | @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 | 179 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- yo
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: Yoruba Bbc News Topic Classification Datas... | 4,156 | [
[
-0.050628662109375,
-0.0537109375,
0.004177093505859375,
0.017730712890625,
-0.035400390625,
-0.00711822509765625,
-0.0265045166015625,
-0.021636962890625,
0.05621337890625,
0.0364990234375,
-0.0609130859375,
-0.0570068359375,
-0.06292724609375,
0.0138015747... |
taln-ls2n/semeval-2010-pre | 2022-09-23T07:37:43.000Z | [
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"region:us"
] | taln-ls2n | Preprocessed SemEval-2010 Benchmark dataset for Keyphrase Generation. | @inproceedings{boudin-etal-2016-document,
title = "How Document Pre-processing affects Keyphrase Extraction Performance",
author = "Boudin, Florian and
Mougard, Hugo and
Cram, Damien",
booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
month = dec,
yea... | 1 | 179 | 2022-04-22T12:10:54 | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-mining
- text-generation
task_ids:
- keyphrase-generation
- keyphrase-extraction
size_categories:
- n<1K
pretty_name: Preprocessed SemEval-2010 Benchmark dataset
---... | 5,921 | [
[
-0.01751708984375,
-0.029632568359375,
0.033935546875,
0.0142364501953125,
-0.041717529296875,
0.0032634735107421875,
-0.008209228515625,
-0.0167236328125,
0.01145172119140625,
0.031829833984375,
-0.0220184326171875,
-0.05059814453125,
-0.042510986328125,
0.... |
pythainlp/thainer-corpus-v2 | 2023-03-23T05:23:46.000Z | [
"task_categories:token-classification",
"language:th",
"license:cc-by-3.0",
"region:us"
] | pythainlp | null | null | 0 | 179 | 2023-03-22T16:12:10 | ---
dataset_info:
features:
- name: words
sequence: string
- name: ner
sequence:
class_label:
names:
'0': B-PERSON
'1': I-PERSON
'2': O
'3': B-ORGANIZATION
'4': B-LOCATION
'5': I-ORGANIZATION
'6': I-LOCATION
'7':... | 3,158 | [
[
-0.028167724609375,
-0.017547607421875,
-0.00002586841583251953,
0.01424407958984375,
-0.034820556640625,
-0.00713348388671875,
-0.0275421142578125,
-0.03717041015625,
0.03668212890625,
0.045867919921875,
-0.01016998291015625,
-0.04052734375,
-0.03204345703125,
... |
Thaweewat/alpaca-cleaned-52k-th | 2023-05-09T16:18:02.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:th",
"license:cc-by-sa-3.0",
"instruction-finetuning",
"region:us"
] | Thaweewat | null | null | 3 | 179 | 2023-05-09T15:45:46 | ---
license: cc-by-sa-3.0
task_categories:
- question-answering
- summarization
tags:
- instruction-finetuning
language:
- th
size_categories:
- 10K<n<100K
---
# Summary
This is a Thai 🇹🇭-instructed dataset translated from cleaned version of the original Alpaca Dataset released by Stanford using Google Cloud Transla... | 4,488 | [
[
-0.0272216796875,
-0.064208984375,
0.027252197265625,
-0.002643585205078125,
-0.0166168212890625,
-0.032745361328125,
0.004241943359375,
-0.0201568603515625,
0.0005412101745605469,
0.059326171875,
-0.0670166015625,
-0.034881591796875,
-0.039031982421875,
0.0... |
yxchng/cc15m_yfcc15m | 2023-06-27T01:54:21.000Z | [
"region:us"
] | yxchng | null | null | 0 | 179 | 2023-06-26T07:52:11 | Entry not found | 15 | [
[
-0.02142333984375,
-0.014984130859375,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.04656982421875,
0.052520751953125,
0.00506591796875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
AILab-CVC/SEED-Bench | 2023-08-02T03:02:59.000Z | [
"task_categories:visual-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | AILab-CVC | null | null | 11 | 179 | 2023-07-28T08:12:52 | ---
license: cc-by-nc-4.0
task_categories:
- visual-question-answering
language:
- en
pretty_name: SEED-Bench
size_categories:
- 10K<n<100K
---
# SEED-Bench Card
## Benchmark details
**Benchmark type:**
SEED-Bench is a large-scale benchmark to evaluate Multimodal Large Language Models (MLLMs).
It consists of 19K m... | 1,986 | [
[
-0.029754638671875,
-0.043121337890625,
0.0211334228515625,
0.05413818359375,
0.0037021636962890625,
-0.01256561279296875,
-0.01534271240234375,
-0.01922607421875,
-0.02264404296875,
0.0100860595703125,
-0.0418701171875,
-0.037872314453125,
-0.03515625,
0.00... |
nampdn-ai/tiny-orca-textbooks | 2023-09-28T02:15:06.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2309.05463",
"arxiv:2305.07759",
"region:us"
] | nampdn-ai | null | null | 11 | 179 | 2023-08-04T09:44:37 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Orca Textbooks
size_categories:
- 100K<n<1M
license: cc-by-nc-sa-4.0
---
# Textbook-like Dataset: A Comprehensive Resource for Text-Based Skills Development in Small Language Models
This dataset is a collection of **147k synthetic textbooks** des... | 3,288 | [
[
-0.0256195068359375,
-0.039031982421875,
0.0172882080078125,
-0.017181396484375,
-0.00005888938903808594,
-0.01158905029296875,
-0.02374267578125,
-0.0223236083984375,
-0.005767822265625,
0.028594970703125,
-0.031951904296875,
-0.038421630859375,
-0.004039764404... |
TaylorAI/RLCD-generated-preference-data-split | 2023-08-30T20:16:20.000Z | [
"region:us"
] | TaylorAI | null | null | 0 | 179 | 2023-08-30T20:06:24 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: float64
- name: output_1
dtype: string
- name: output_2
dtype: string
- n... | 787 | [
[
-0.057647705078125,
-0.035369873046875,
0.00556182861328125,
0.007671356201171875,
-0.028594970703125,
0.01067352294921875,
-0.00032782554626464844,
-0.007602691650390625,
0.0709228515625,
0.059844970703125,
-0.07537841796875,
-0.044281005859375,
-0.028549194335... |
eurlex | 2022-11-18T20:01:34.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"legal-topic-classification",
"re... | null | EURLEX57K contains 57k legislative documents in English from EUR-Lex portal, annotated with EUROVOC concepts. | @inproceedings{chalkidis-etal-2019-large,
title = "Large-Scale Multi-Label Text Classification on {EU} Legislation",
author = "Chalkidis, Ilias and Fergadiotis, Emmanouil and Malakasiotis, Prodromos and Androutsopoulos, Ion",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Comp... | 4 | 178 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: eurlex57k
pretty_name: the EUR-Lex... | 10,874 | [
[
-0.046173095703125,
-0.034210205078125,
0.011383056640625,
0.0005245208740234375,
-0.00942230224609375,
-0.00818634033203125,
-0.0200347900390625,
-0.042755126953125,
0.03369140625,
0.039581298828125,
-0.027984619140625,
-0.07427978515625,
-0.03057861328125,
... |
harem | 2023-01-25T14:31:29.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:pt",
"license:unknown",
"region:us"
] | null | The HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts,
from several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM
documents are the validation set and the mini... | @inproceedings{santos2006harem,
title={Harem: An advanced ner evaluation contest for portuguese},
author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui},
booktitle={quot; In Nicoletta Calzolari; Khalid Choukri; Aldo Gangemi; Bente Maegaard; Joseph Mariani; Jan Odjik; Daniel Tapias (ed) Proceeding... | 5 | 178 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: HAREM
dataset_info:
- config_name: defaul... | 7,080 | [
[
-0.033935546875,
-0.044342041015625,
-0.0166168212890625,
0.0267486572265625,
-0.0147857666015625,
-0.01311492919921875,
-0.0292816162109375,
-0.0304412841796875,
0.03643798828125,
0.037261962890625,
-0.058563232421875,
-0.0595703125,
-0.056365966796875,
0.0... |
um005 | 2022-11-18T21:58:09.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:ur",
"license:unknown",
"region:us"
] | null | UMC005 English-Urdu is a parallel corpus of texts in English and Urdu language with sentence alignments. The corpus can be used for experiments with statistical machine translation.
The texts come from four different sources:
- Quran
- Bible
- Penn Treebank (Wall Street Journal)
- Emille corpus
The authors provide th... | @unpublished{JaZeWordOrderIssues2011,
author = {Bushra Jawaid and Daniel Zeman},
title = {Word-Order Issues in {English}-to-{Urdu} Statistical Machine Translation},
year = {2011},
journal = {The Prague Bulletin of Mathematical Linguistics},
number = {95},
institution = {Univerzita Karlova},
a... | 0 | 178 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- other
language:
- en
- ur
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: umc005-english-urdu
pretty_name: UMC005 English-Urdu
dataset_... | 4,306 | [
[
-0.031341552734375,
-0.017242431640625,
0.0013914108276367188,
0.03900146484375,
-0.01551055908203125,
0.0148773193359375,
-0.0184783935546875,
-0.01213836669921875,
0.0199127197265625,
0.049163818359375,
-0.050384521484375,
-0.07763671875,
-0.0494384765625,
... |
biu-nlp/abstract-sim | 2023-05-29T09:33:17.000Z | [
"region:us"
] | biu-nlp | null | null | 2 | 178 | 2023-05-13T16:43:12 | A dataset of Wikipedia sentences accompannied by valid and invalid abstract descriptions. | 89 | [
[
-0.0254974365234375,
-0.048675537109375,
0.0340576171875,
0.01120758056640625,
-0.006500244140625,
-0.0255279541015625,
-0.004421234130859375,
-0.0212249755859375,
0.023193359375,
0.027008056640625,
-0.039154052734375,
-0.0163116455078125,
-0.024139404296875,
... |
cryptom/ceval-exam | 2023-06-24T00:40:14.000Z | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-nc-sa-4.0",
"arxiv:2305.08322",
"region:us"
] | cryptom | C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. | @article{huang2023ceval,
title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models},
author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and ... | 0 | 178 | 2023-06-23T18:40:37 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
- multiple-choice
- question-answering
language:
- zh
pretty_name: C-Eval
size_categories:
- 10K<n<100K
---
C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disci... | 1,897 | [
[
-0.030914306640625,
-0.08428955078125,
0.0212860107421875,
0.0183258056640625,
0.01099395751953125,
0.01067352294921875,
-0.0254974365234375,
-0.023345947265625,
-0.0086517333984375,
0.0276641845703125,
-0.0230865478515625,
-0.033477783203125,
-0.00634765625,
... |
AlignmentLab-AI/QualityControl | 2023-10-11T08:07:03.000Z | [
"region:us"
] | AlignmentLab-AI | null | null | 0 | 178 | 2023-10-11T04:53:07 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01497650146484375,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.046478271484375,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.0170135498046875,
-0.052093505859375,
-0.01497650146484375,
-0.0604248046875,
0.0379028... |
europa_eac_tm | 2023-01-25T14:30:11.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"lang... | null | In October 2012, the European Union's (EU) Directorate General for Education and Culture ( DG EAC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-six languages. This resource bears the name EAC Translation Memory, short EAC-TM.
EAC-TM covers... | @Article{Steinberger2014,
author={Steinberger, Ralf
and Ebrahim, Mohamed
and Poulis, Alexandros
and Carrasco-Benitez, Manuel
and Schl{\"u}ter, Patrick
and Przybyszewski, Marek
and Gilbro, Signe},
title={An ov... | 2 | 177 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hr
- hu
- is
- it
- lt
- lv
- mt
- nl
- 'no'
- pl
- pt
- ro
- sk
- sl
- sv
- tr
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets... | 18,124 | [
[
-0.0186920166015625,
-0.044708251953125,
0.0218048095703125,
0.0049285888671875,
-0.0170135498046875,
-0.0004401206970214844,
-0.030364990234375,
-0.0364990234375,
0.0202484130859375,
0.0362548828125,
-0.049407958984375,
-0.06744384765625,
-0.05157470703125,
... |
pec | 2023-06-01T14:59:50.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-retrieval",
"task_ids:dialogue-modeling",
"task_ids:utterance-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:orig... | null | \
A dataset of around 350K persona-based empathetic conversations. Each speaker is associated with a persona, which comprises multiple persona sentences. The response of each conversation is empathetic. | \
@inproceedings{zhong2020towards,
title = "Towards Persona-Based Empathetic Conversational Models",
author = "Zhong, Peixiang and
Zhang, Chen and
Wang, Hao and
Liu, Yong and
Miao, Chunyan",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural L... | 4 | 177 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-retrieval
task_ids:
- dialogue-modeling
- utterance-retrieval
paperswithcode_id: pe... | 8,559 | [
[
-0.036468505859375,
-0.07330322265625,
0.03558349609375,
0.0285186767578125,
-0.001712799072265625,
-0.00624847412109375,
-0.025390625,
-0.01898193359375,
0.0341796875,
0.032379150390625,
-0.065673828125,
-0.056121826171875,
-0.0292205810546875,
0.0128326416... |
wiki_summary | 2022-11-18T22:00:55.000Z | [
"task_categories:text2text-generation",
"task_categories:translation",
"task_categories:question-answering",
"task_categories:summarization",
"task_ids:abstractive-qa",
"task_ids:explanation-generation",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:open-domain-abstractive-qa",
"t... | null | \
The dataset extracted from Persian Wikipedia into the form of articles and highlights and cleaned the dataset into pairs of articles and highlights and reduced the articles' length (only version 1.0.0) and highlights' length to a maximum of 512 and 128, respectively, suitable for parsBERT. | \
@misc{Bert2BertWikiSummaryPersian,
author = {Mehrdad Farahani},
title = {Summarization using Bert2Bert model on WikiSummary dataset},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {https://github.com/m3hrdadfi/wiki-summary},
} | 5 | 177 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- fa
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
- translation
- question-answering
- summarization
task_ids:
- abstractive-qa
... | 7,708 | [
[
-0.053863525390625,
-0.041168212890625,
0.024566650390625,
0.0274810791015625,
-0.030517578125,
-0.02117919921875,
0.00438690185546875,
-0.036102294921875,
0.0572509765625,
0.0304412841796875,
-0.030120849609375,
-0.053924560546875,
-0.056671142578125,
0.026... |
nielsr/rvlcdip-demo | 2022-03-08T12:11:13.000Z | [
"region:us"
] | nielsr | null | null | 0 | 177 | 2022-03-08T12:11:11 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01497650146484375,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.046478271484375,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.0170135498046875,
-0.052093505859375,
-0.01497650146484375,
-0.0604248046875,
0.0379028... |
Tuana/presidents | 2023-02-28T01:06:47.000Z | [
"region:us"
] | Tuana | null | null | 1 | 177 | 2023-02-28T00:51:03 | ---
dataset_info:
features:
- name: id
dtype: string
- name: content
dtype: string
- name: content_type
dtype: string
- name: meta
struct:
- name: url
dtype: string
- name: _split_id
dtype: int64
- name: id_hash_keys
sequence: string
- name: score
dtype: 'null'
... | 647 | [
[
-0.0416259765625,
-0.0309295654296875,
0.02386474609375,
0.01377105712890625,
-0.01342010498046875,
0.01251220703125,
0.012359619140625,
-0.005710601806640625,
0.0606689453125,
0.042388916015625,
-0.060943603515625,
-0.04949951171875,
-0.043426513671875,
-0.... |
scielo | 2023-06-01T14:59:47.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:es",
"language:pt",
"license:unknown",
"arxiv:1905.01852",
"region:us"
] | null | A parallel corpus of full-text scientific articles collected from Scielo database in the following languages: English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences. Alignment was carried out using the Hunalign algorithm. | @inproceedings{soares2018large,
title={A Large Parallel Corpus of Full-Text Scientific Articles},
author={Soares, Felipe and Moreira, Viviane and Becker, Karin},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018)},
year={2018}
} | 1 | 176 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- es
- pt
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: SciELO
dataset_info:
- config_name: en-es
f... | 4,316 | [
[
-0.011016845703125,
-0.017059326171875,
0.014373779296875,
0.04119873046875,
-0.0180206298828125,
0.020172119140625,
-0.0229034423828125,
-0.035797119140625,
0.051605224609375,
0.02886962890625,
-0.041595458984375,
-0.072265625,
-0.04815673828125,
0.03945922... |
MonoHime/ru_sentiment_dataset | 2021-05-20T00:57:22.000Z | [
"language:ru",
"sentiment",
"text-classification",
"region:us"
] | MonoHime | null | null | 3 | 176 | 2022-03-02T23:29:22 | ---
language:
- ru
tags:
- sentiment
- text-classification
---
# Dataset with sentiment of Russian text
Contains aggregated dataset of Russian texts from 6 datasets.
## Labels meaning
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## Datasets
**[Sentiment Analysis in Russian](https://www.kaggle.com/c/sentiment-anal... | 1,551 | [
[
-0.033416748046875,
-0.0291748046875,
0.0186614990234375,
0.017059326171875,
-0.038909912109375,
-0.004985809326171875,
-0.014495849609375,
-0.0095367431640625,
0.0223541259765625,
0.01548004150390625,
-0.04559326171875,
-0.07159423828125,
-0.038299560546875,
... |
bigbio/osiris | 2022-12-22T15:46:10.000Z | [
"multilinguality:monolingual",
"language:en",
"license:cc-by-3.0",
"region:us"
] | bigbio | The OSIRIS corpus is a set of MEDLINE abstracts manually annotated
with human variation mentions. The corpus is distributed under the terms
of the Creative Commons Attribution License
Creative Commons Attribution 3.0 Unported License,
which permits unrestricted use, distribution, and reproduction in any medium,
provide... | @ARTICLE{Furlong2008,
author = {Laura I Furlong and Holger Dach and Martin Hofmann-Apitius and Ferran Sanz},
title = {OSIRISv1.2: a named entity recognition system for sequence variants
of genes in biomedical literature.},
journal = {BMC Bioinformatics},
year = {2008},
volume = {9},
pages = {84},
doi = ... | 1 | 176 | 2022-11-13T22:11:10 |
---
language:
- en
bigbio_language:
- English
license: cc-by-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_3p0
pretty_name: OSIRIS
homepage: https://sites.google.com/site/laurafurlongweb/databases-and-tools/corpora/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
... | 1,476 | [
[
-0.03619384765625,
-0.0140380859375,
0.0183868408203125,
0.00038743019104003906,
-0.01593017578125,
-0.01413726806640625,
-0.01114654541015625,
-0.03839111328125,
0.04620361328125,
0.046783447265625,
-0.04302978515625,
-0.056793212890625,
-0.055145263671875,
... |
GEM/xsum | 2022-10-24T15:31:30.000Z | [
"task_categories:summarization",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | GEM | This is the XSUM subset of the GEM benchmark. | @inproceedings{narayan-etal-2018-dont,
title = "Don{'}t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization",
author = "Narayan, Shashi and
Cohen, Shay B. and
Lapata, Mirella",
booktitle = "Proceedings of the 2018 Conference on Empirical M... | 0 | 175 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: xsum
---
# Dataset Card for GEM/xsum
## Dataset Description
- **Homepage:**... | 13,964 | [
[
-0.03106689453125,
-0.048858642578125,
0.020172119140625,
-0.004886627197265625,
-0.0278167724609375,
-0.01016998291015625,
-0.0146484375,
-0.03167724609375,
0.045562744140625,
0.032440185546875,
-0.04071044921875,
-0.056884765625,
-0.04248046875,
0.01251220... |
yhavinga/mc4_nl_cleaned | 2022-12-16T09:24:34.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:en-nl",
"source_datasets:extended",
"language:nl",
"language:en",
"license:odc-by",
"arxiv:1910.10683",
"region:us"
... | yhavinga | 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... | @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... | 7 | 175 | 2022-03-02T23:29:22 | ---
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... | 9,514 | [
[
-0.0518798828125,
-0.0455322265625,
0.0308074951171875,
0.01555633544921875,
-0.0234222412109375,
-0.003993988037109375,
-0.026397705078125,
-0.043365478515625,
0.045867919921875,
0.040924072265625,
-0.0307159423828125,
-0.057281494140625,
-0.0406494140625,
... |
severo/flores_101 | 2022-10-27T08:37:36.000Z | [
"task_categories:text-generation",
"task_categories:translation",
"annotations_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|flores",
"language:af",
"language:am",
"langua... | severo | One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the
lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource
languages, consider only restricted domains, or are low quality because they are constructed using
s... | @inproceedings{,
title={The {FLORES}-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
author={
Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and
Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francis... | 0 | 175 | 2023-06-20T21:40:23 | ---
annotations_creators:
- found
language_creators:
- expert-generated
language:
- af
- am
- ar
- hy
- as
- ast
- az
- be
- bn
- bs
- bg
- my
- ca
- ceb
- zho
- hr
- cs
- da
- nl
- en
- et
- tl
- fi
- fr
- ff
- gl
- lg
- ka
- de
- el
- gu
- ha
- he
- hi
- hu
- is
- ig
- id
- ga
- it
- ja
- jv
- kea
- kam
- kn
- kk
- k... | 6,979 | [
[
-0.0222015380859375,
-0.038970947265625,
0.0259552001953125,
0.038909912109375,
-0.0012722015380859375,
-0.01380157470703125,
-0.0467529296875,
-0.018646240234375,
0.02728271484375,
0.0049896240234375,
-0.048309326171875,
-0.056884765625,
-0.0374755859375,
0... |
proto_qa | 2022-11-03T16:31:01.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
... | null | This dataset is for studying computational models trained to reason about prototypical situations. Using deterministic filtering a sampling from a larger set of all transcriptions was built. It contains 9789 instances where each instance represents a survey question from Family Feud game. Each instance exactly is a que... | @InProceedings{huggingface:dataset,
title = {ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning},
authors={Michael Boratko, Xiang Lorraine Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum},
year={2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://gi... | 1 | 174 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- other
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
- open-domain-qa
paperswithcode_id: protoqa
p... | 11,882 | [
[
-0.045379638671875,
-0.04730224609375,
0.0124359130859375,
0.0005450248718261719,
0.0010232925415039062,
0.0099029541015625,
-0.01032257080078125,
-0.0157318115234375,
0.0300445556640625,
0.03900146484375,
-0.053955078125,
-0.04766845703125,
-0.033966064453125,
... |
wiki_movies | 2022-11-18T22:00:27.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"arxiv:1606.03126",
"region:us"
] | null | The WikiMovies dataset consists of roughly 100k (templated) questions over 75k entities based on questions with answers in the open movie database (OMDb). | @misc{miller2016keyvalue,
title={Key-Value Memory Networks for Directly Reading Documents},
author={Alexander Miller and Adam Fisch and Jesse Dodge and Amir-Hossein Karimi and Antoine Bordes and Jason Weston},
year={2016},
eprint={1606.03126},
archivePrefix={arXiv},
primaryClass={cs.... | 3 | 174 | 2022-03-02T23:29:22 | ---
pretty_name: WikiMovies
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: wikimovies
... | 5,004 | [
[
-0.0352783203125,
-0.038299560546875,
0.0097808837890625,
-0.01458740234375,
-0.0198974609375,
0.0088958740234375,
-0.0135345458984375,
-0.01006317138671875,
0.0300750732421875,
0.033447265625,
-0.057861328125,
-0.05523681640625,
-0.05059814453125,
0.0143203... |
asi/wikitext_fr | 2022-10-21T16:23:07.000Z | [
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:fr",
"license:cc-by-sa-4.0",
"arxiv:1609.07843",
"region:us"
] | asi | Wikitext-fr language modeling dataset consists of over 70 million tokens
extracted from the set of french Wikipedia articles that are classified as
"quality articles" or "good articles.". The aim is to replicate the English
benchmark. | @inproceedings{simoulin:hal-03265900,
TITLE = {{Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}},
AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit},
URL = {https://hal.archives-ouvertes.fr/hal-03265900},
BOOKTITLE = {{Traitement Automatique des Langues Natu... | 4 | 174 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- fr
language_bcp47:
- fr-FR
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Wikitext-fr
size_categories:
- unknown
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
# Dat... | 5,598 | [
[
-0.04290771484375,
-0.0489501953125,
0.01153564453125,
0.0209503173828125,
-0.0142059326171875,
-0.00868988037109375,
-0.0228118896484375,
-0.0228271484375,
0.0201568603515625,
0.0282440185546875,
-0.03814697265625,
-0.053863525390625,
-0.04638671875,
0.0184... |
mozilla-foundation/common_voice_9_0 | 2023-07-29T16:00:12.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | mozilla-foundation | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Lang... | 11 | 174 | 2022-04-29T16:49:21 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- 10K<n<100K
ar:
- 100K<n<1M
as:
- n<1K
az:
- n<1K
ba:
- 100K<n<1M
bas:
- 1K<n<10K
be:
- 100K<n<1M
bg:
- 1K<n<10K
bn:
- 100K<n<1M
br:
... | 11,950 | [
[
-0.039215087890625,
-0.052886962890625,
0.01177215576171875,
0.03173828125,
-0.02032470703125,
0.0050506591796875,
-0.042877197265625,
-0.015777587890625,
0.03411865234375,
0.04071044921875,
-0.056732177734375,
-0.0732421875,
-0.03253173828125,
0.01831054687... |
GATE-engine/describable_textures | 2023-06-05T17:13:02.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 174 | 2023-06-04T23:57:38 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 350355304.0
num_examples: 3960
- name: validation
num_bytes: 72331220.0
num_examples: 840
- name: test
num_bytes: 73428430.0
num_examples: 840
download_size:... | 529 | [
[
-0.04058837890625,
-0.03631591796875,
0.0205535888671875,
0.05242919921875,
-0.020477294921875,
0.0101776123046875,
0.0168914794921875,
-0.025726318359375,
0.066650390625,
0.0308837890625,
-0.052337646484375,
-0.053619384765625,
-0.0443115234375,
-0.02565002... |
GATE-engine/omniglot | 2023-06-05T18:58:27.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 174 | 2023-06-05T18:13:32 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: full
num_bytes: 11924141.5
num_examples: 32460
download_size: 10520482
dataset_size: 11924141.5
---
# Dataset Card for "omniglot"
[More Information needed](https://github.com/huggingface/data... | 390 | [
[
-0.05230712890625,
-0.0207977294921875,
0.0223236083984375,
0.01090240478515625,
-0.00445556640625,
-0.01552581787109375,
0.01025390625,
-0.019195556640625,
0.06591796875,
0.044281005859375,
-0.05657958984375,
-0.0552978515625,
-0.022796630859375,
-0.0231323... |
C-MTEB/BQ | 2023-07-28T13:52:50.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 174 | 2023-07-28T13:52:31 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int32
split... | 724 | [
[
-0.027618408203125,
-0.01153564453125,
0.010955810546875,
0.01396942138671875,
-0.01605224609375,
0.009552001953125,
0.0286407470703125,
-0.0057830810546875,
0.047210693359375,
0.0372314453125,
-0.056121826171875,
-0.05108642578125,
-0.02874755859375,
-0.016... |
dansbecker/hackernews_hiring_posts | 2021-12-07T13:46:20.000Z | [
"region:us"
] | dansbecker | null | null | 0 | 173 | 2022-03-02T23:29:22 | This dataset contains postings and comments from the following recurring threads on [Hacker News](http://news.ycombinator.com/)
1. Ask HN: Who is hiring?
2. Ask HN: Who wants to be hired?
3. Freelancer? Seeking freelancer?
These post types are stored in datasets called `hiring`, `wants_to_be_hired` and `freelancer` r... | 1,098 | [
[
-0.0146026611328125,
-0.0635986328125,
0.042236328125,
0.0212860107421875,
-0.0237579345703125,
-0.0025653839111328125,
-0.0005097389221191406,
-0.0213470458984375,
0.05810546875,
0.05792236328125,
-0.0760498046875,
-0.03729248046875,
-0.0265960693359375,
0.... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.