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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
indonlp/NusaX-MT | 2023-01-24T17:21:03.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ace",
"language:ban",
"language:bjn",
"language:bug",
"language:en",
"language:id",
... | indonlp | NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-MT is a parallel corpus for training and benchmarking machine tr... | @misc{winata2022nusax,
title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
... | 5 | 320 | 2023-01-24T17:05:31 | ---
pretty_name: NusaX-MT
annotations_creators:
- expert-generated
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
language:
- ace
- ban
- bjn
- bug
- en
- id
- jv
- mad
- min
- nij
- su
- bbc
size_categories:
- 10K<n<100K
source_datasets:
- original
task_catego... | 5,634 | [
[
-0.041839599609375,
-0.02581787109375,
0.003337860107421875,
0.043670654296875,
-0.03759765625,
0.00347137451171875,
-0.024169921875,
-0.01580810546875,
0.053985595703125,
0.047698974609375,
-0.035919189453125,
-0.07073974609375,
-0.05694580078125,
0.0545043... |
result-kand2-sdxl-wuerst-karlo/9a272529 | 2023-10-04T08:54:53.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 320 | 2023-10-04T08:54:52 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 246
num_examples: 10
download_size: 1437
dataset_size: 246
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "9a27252... | 455 | [
[
-0.04254150390625,
-0.01093292236328125,
0.0239715576171875,
0.0250701904296875,
-0.01142120361328125,
-0.00021278858184814453,
0.02423095703125,
-0.0099639892578125,
0.0718994140625,
0.03619384765625,
-0.05657958984375,
-0.041107177734375,
-0.040252685546875,
... |
TJUNLP/M3KE | 2023-06-19T04:07:29.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:multiple-choice",
"size_categories:10K<n<100K",
"language:zh",
"license:apache-2.0",
"arxiv:2305.10263",
"region:us"
] | TJUNLP | A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models. | @misc{liu2023m3ke,
title={M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models},
author={Chuang Liu and Renren Jin and Yuqi Ren and Linhao Yu and Tianyu Dong and Xiaohan Peng and Shuting Zhang and Jianxiang Peng and Peiyi Zhang and Qingqing Lyu and Xiaowen S... | 2 | 319 | 2023-06-16T02:42:59 | ---
license: apache-2.0
task_categories:
- text-classification
- question-answering
- multiple-choice
language:
- zh
size_categories:
- 10K<n<100K
arxiv:
- 2305.10263
---
M3KE, or Massive Multi-Level Multi-Subject Knowledge Evaluation, is a benchmark developed to assess the knowledge acquired by large Chinese languag... | 1,839 | [
[
-0.039703369140625,
-0.056182861328125,
0.0394287109375,
0.006717681884765625,
0.00939178466796875,
-0.01119232177734375,
-0.020904541015625,
-0.00399017333984375,
-0.02545166015625,
0.01554107666015625,
-0.0526123046875,
-0.054168701171875,
-0.04443359375,
... |
germeval_14 | 2023-04-05T10:06:39.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"region:us"
] | null | The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties: - The data was sampled from German Wikipedia and News Corpora as a collection of citations. - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens. - The NER ann... | @inproceedings{benikova-etal-2014-nosta,
title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset},
author = {Benikova, Darina and
Biemann, Chris and
Reznicek, Marc},
booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'... | 3 | 318 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: nosta-d-named-entity-annotation-... | 8,995 | [
[
-0.05841064453125,
-0.046356201171875,
0.015472412109375,
-0.0003604888916015625,
-0.0135040283203125,
-0.01306915283203125,
-0.033355712890625,
-0.034912109375,
0.042510986328125,
0.0226898193359375,
-0.0499267578125,
-0.0750732421875,
-0.044219970703125,
0... |
taeshahn/ko-lima | 2023-06-30T09:21:43.000Z | [
"license:cc-by-nc-sa-4.0",
"arxiv:2305.11206",
"region:us"
] | taeshahn | A high-quality korean dataset for efficient instruction tuning. | @InProceedings{huggingface:dataset,
title = {Ko-LIMA: Korean LIMA Dataset},
author={Hahn, Taeseung},
year={2023}
} | 9 | 318 | 2023-06-13T15:10:24 | ---
license: cc-by-nc-sa-4.0
---
# Dataset Card for Ko-LIMA
## Dataset Description
Ko-LIMA는 Meta에서 공개한 [LIMA: Less Is More for Alignment](https://arxiv.org/abs/2305.11206) (Zhou et al., 2023)의 [학습 데이터](https://huggingface.co/datasets/GAIR/lima)를 한국어로 번역한 데이터셋입니다. 번역에는 [DeepL API](https://www.deepl.com/docs-api)를 활용하... | 4,567 | [
[
-0.043212890625,
-0.037078857421875,
0.0255279541015625,
0.01861572265625,
-0.03497314453125,
-0.006908416748046875,
0.01445770263671875,
-0.017974853515625,
0.047515869140625,
0.0181427001953125,
-0.031829833984375,
-0.042266845703125,
-0.04443359375,
0.012... |
ccaligned_multilingual | 2022-11-03T16:31:56.000Z | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"size_categories:10M<n<100M",
"sourc... | null | CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching ap... | @inproceedings{elkishky_ccaligned_2020,
author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
month = {November},
title = {{CCAligned}: A Massive Collection o... | 3 | 317 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- ak
- am
- ar
- as
- ay
- az
- be
- bg
- bm
- bn
- br
- bs
- ca
- ceb
- ckb
- cs
- cy
- de
- dv
- el
- eo
- es
- fa
- ff
- fi
- fo
- fr
- fy
- ga
- gl
- gn
- gu
- he
- hi
- hr
- hu
- id
- ig
- is
- it
- iu
- ja
- ka
- kac
- kg
- kk
- k... | 13,315 | [
[
-0.0455322265625,
-0.043548583984375,
0.00830841064453125,
0.01374053955078125,
-0.03173828125,
-0.0027866363525390625,
-0.0160980224609375,
-0.030487060546875,
0.035186767578125,
0.02862548828125,
-0.036651611328125,
-0.063232421875,
-0.044708251953125,
0.0... |
Tevatron/beir-corpus | 2022-07-07T23:53:45.000Z | [
"region:us"
] | Tevatron | null | null | 0 | 317 | 2022-06-07T06:00:10 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Muennighoff/flan | 2022-12-23T18:57:00.000Z | [
"task_categories:other",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"language:en",
"arxiv:2109.01652",
"region:us"
] | Muennighoff | null | null | 33 | 317 | 2022-12-12T11:32:26 | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- en
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
task_categories:
- other
---
This is a repreprocessed version of the [FLAN dataset](https://arxiv.org/abs/2109.01652) with any updates that have been made to the FLAN datasets since the... | 2,238 | [
[
-0.0423583984375,
-0.027740478515625,
0.0243072509765625,
-0.0021991729736328125,
-0.0005464553833007812,
0.00624847412109375,
-0.01438140869140625,
-0.0209503173828125,
0.053863525390625,
0.05792236328125,
-0.059295654296875,
-0.061798095703125,
-0.037139892578... |
d0rj/curation-corpus | 2023-06-13T13:25:32.000Z | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"news",
"summarization",
"region:us"
] | d0rj | null | null | 1 | 317 | 2023-06-12T19:22:21 | ---
dataset_info:
features:
- name: title
dtype: string
- name: summary
dtype: string
- name: url
dtype: string
- name: date
dtype: string
- name: article_content
dtype: string
splits:
- name: train
num_bytes: 127948910
num_examples: 30455
download_size: 76620775
dataset_... | 1,151 | [
[
-0.0294647216796875,
-0.034698486328125,
0.015777587890625,
0.024810791015625,
-0.026763916015625,
0.032501220703125,
-0.019439697265625,
0.002002716064453125,
0.038909912109375,
0.047210693359375,
-0.0306854248046875,
-0.06939697265625,
-0.052337646484375,
... |
Short-Answer-Feedback/saf_communication_networks_english | 2023-03-31T11:46:04.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"short answer feedback",
"communication networks",
"region:us"
] | Short-Answer-Feedback | null | null | 6 | 316 | 2022-11-10T21:22:13 | ---
pretty_name: SAF - Communication Networks - English
annotations_creators:
- expert-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- short answer feedback
- communication networks
task_categories:
- text2text-generation... | 6,685 | [
[
-0.053558349609375,
-0.06427001953125,
0.00811767578125,
0.0328369140625,
-0.00893402099609375,
-0.01102447509765625,
-0.032135009765625,
-0.037841796875,
0.04193115234375,
0.0302276611328125,
-0.0732421875,
-0.03558349609375,
-0.0347900390625,
0.03671264648... |
sidhq/email-thread-summary | 2023-07-17T03:19:09.000Z | [
"task_categories:summarization",
"language:en",
"region:us"
] | sidhq | null | null | 2 | 316 | 2023-07-17T01:08:40 | ---
dataset_info:
features:
- name: thread
struct:
- name: subject
dtype: string
- name: messages
list:
- name: timestamp
dtype: timestamp[s]
- name: from
dtype: string
- name: to
sequence: string
- name: body
dtype: string
- name: su... | 788 | [
[
-0.0479736328125,
-0.024749755859375,
0.00981903076171875,
0.01415252685546875,
-0.0134124755859375,
-0.0018978118896484375,
0.0159759521484375,
-0.0057830810546875,
0.07733154296875,
0.036224365234375,
-0.07135009765625,
-0.042327880859375,
-0.0487060546875,
... |
result-kand2-sdxl-wuerst-karlo/53decd51 | 2023-10-04T12:24:28.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 316 | 2023-10-04T12:24:27 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 190
num_examples: 10
download_size: 1351
dataset_size: 190
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "53decd5... | 455 | [
[
-0.05029296875,
-0.00612640380859375,
0.0203857421875,
0.0242919921875,
-0.02081298828125,
0.01201629638671875,
0.028839111328125,
-0.00794219970703125,
0.06396484375,
0.0240020751953125,
-0.07177734375,
-0.05511474609375,
-0.0316162109375,
-0.00391006469726... |
result-kand2-sdxl-wuerst-karlo/5f48a05c | 2023-10-04T12:28:37.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 316 | 2023-10-04T12:28:36 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 176
num_examples: 10
download_size: 1365
dataset_size: 176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "5f48a05... | 455 | [
[
-0.060546875,
0.002777099609375,
0.0114898681640625,
0.0216217041015625,
-0.01495361328125,
0.00009870529174804688,
0.036529541015625,
-0.021209716796875,
0.05340576171875,
0.022247314453125,
-0.0611572265625,
-0.05511474609375,
-0.038604736328125,
0.0041046... |
edinburghcstr/ami | 2023-01-16T18:11:05.000Z | [
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"language:en",
"license:cc-by-4.0",
"arxiv:1906.11047",
"region:us"
] | edinburghcstr | The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
synchronized to a common timeline. These include close-talking and far-field microphones, individual and
room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,... | @inproceedings{10.1007/11677482_3,
author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain ... | 17 | 314 | 2022-08-17T22:02:08 | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: AMI
size_categories: []
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
---
# Dataset Card for AMI
## Table of Contents
- [Table of Contents](#table-of-conten... | 5,790 | [
[
-0.04400634765625,
-0.045318603515625,
0.016357421875,
0.007358551025390625,
-0.00832366943359375,
-0.0077362060546875,
-0.041900634765625,
-0.045745849609375,
0.0290679931640625,
0.0131988525390625,
-0.051513671875,
-0.05859375,
-0.03948974609375,
0.0020370... |
awettig/Pile-Gutenberg-0.5B-6K-opt | 2023-07-10T19:44:26.000Z | [
"region:us"
] | awettig | null | null | 0 | 314 | 2023-07-10T19:42:59 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6500959920
num_examples: 81380
- name: test
num_bytes: 64945692
num_examples: 813
download_size: 1706776857
da... | 527 | [
[
-0.052581787109375,
-0.009185791015625,
0.0040435791015625,
0.01361083984375,
-0.0298919677734375,
-0.003757476806640625,
0.0203399658203125,
-0.0195465087890625,
0.046417236328125,
0.049163818359375,
-0.0452880859375,
-0.054656982421875,
-0.0438232421875,
-... |
bigcode/commits_ft | 2023-07-11T04:31:12.000Z | [
"region:us"
] | bigcode | Code Commits for Instruction Tuning | @InProceedings{huggingface:dataset,
title = {Code Commits for Instruction Tuning},
author={BigCode},
year={2023}
} | 0 | 314 | 2023-07-11T04:00:41 | 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... |
result-kand2-sdxl-wuerst-karlo/ff0ba7a6 | 2023-10-04T13:47:35.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 314 | 2023-10-04T13:47:34 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 224
num_examples: 10
download_size: 1359
dataset_size: 224
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ff0ba7a... | 455 | [
[
-0.048095703125,
-0.0029964447021484375,
0.01280975341796875,
0.0273895263671875,
-0.015838623046875,
-0.00823211669921875,
0.04052734375,
-0.018646240234375,
0.051849365234375,
0.039031982421875,
-0.062347412109375,
-0.038604736328125,
-0.03509521484375,
-0... |
msra_ner | 2023-01-25T14:40:51.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:unknown",
"region:us"
] | null | The Third International Chinese Language
Processing Bakeoff was held in Spring
2006 to assess the state of the art in two
important tasks: word segmentation and
named entity recognition. Twenty-nine
groups submitted result sets in the two
tasks across two tracks and a total of five
corpora. We found strong results in b... | @inproceedings{levow2006third,
author = {Gina{-}Anne Levow},
title = {The Third International Chinese Language Processing Bakeoff: Word
Segmentation and Named Entity Recognition},
booktitle = {SIGHAN@COLING/ACL},
pages = {108--117},
publisher = {Association for Computational Linguist... | 18 | 313 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: MSRA NER
dataset_info:
features:
- ... | 3,608 | [
[
-0.0193634033203125,
-0.026214599609375,
-0.0003364086151123047,
0.0093841552734375,
-0.0167388916015625,
0.0102081298828125,
-0.01641845703125,
-0.0223541259765625,
0.055084228515625,
0.038421630859375,
-0.05255126953125,
-0.059234619140625,
-0.0390625,
0.0... |
shailja/Verilog_GitHub | 2023-09-20T17:14:18.000Z | [
"license:mit",
"arxiv:2212.11140",
"region:us"
] | shailja | null | null | 3 | 313 | 2022-12-19T15:19:55 | ---
license: mit
---
---
pipeline_tag: text-generation
tags:
- code
model-index:
- name: VeriGen
results:
- task:
type: text-generation
dataset:
type:
name:
extra_gated_prompt: >-
## Model License Agreement
Please read the BigCode [OpenRAIL-M
license](https://huggingface.co/space... | 2,782 | [
[
-0.0182952880859375,
-0.039154052734375,
0.0311279296875,
0.01678466796875,
-0.0178985595703125,
-0.0131988525390625,
-0.0292510986328125,
-0.0205535888671875,
-0.0126495361328125,
0.048004150390625,
-0.04052734375,
-0.055572509765625,
-0.0537109375,
0.00474... |
sanchit-gandhi/concatenated_librispeech | 2023-01-26T11:45:39.000Z | [
"region:us"
] | sanchit-gandhi | null | null | 0 | 313 | 2023-01-26T10:26:12 | ---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 707889.0
num_examples: 1
download_size: 0
dataset_size: 707889.0
---
# Dataset Card for "concatenated_librispeech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#... | 359 | [
[
-0.041717529296875,
-0.0210418701171875,
0.0012302398681640625,
0.016632080078125,
-0.03448486328125,
0.0008134841918945312,
0.0035839080810546875,
-0.025115966796875,
0.06610107421875,
0.039337158203125,
-0.064697265625,
-0.048004150390625,
-0.036529541015625,
... |
Polyglot-or-Not/Fact-Completion | 2023-06-14T03:05:21.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text2text-generation",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:en",
"language:fr",
"language:es",
"language... | Polyglot-or-Not | null | null | 10 | 313 | 2023-03-22T23:42:30 | ---
license: apache-2.0
tags:
- natural-language-understanding
language_creators:
- expert-generated
- machine-generated
multilinguality:
- multilingual
pretty_name: Polyglot or Not? Fact-Completion Benchmark
size_categories:
- 100K<n<1M
task_categories:
- text-generation
- fill-mask
- text2text-generation
dataset_info... | 5,729 | [
[
-0.0299072265625,
-0.0477294921875,
0.037811279296875,
0.01004791259765625,
-0.00647735595703125,
0.006130218505859375,
-0.042510986328125,
-0.02581787109375,
0.004909515380859375,
0.0323486328125,
-0.037078857421875,
-0.055999755859375,
-0.045928955078125,
... |
DFKI-SLT/cdcp | 2023-08-08T12:47:42.000Z | [
"region:us"
] | DFKI-SLT | null | @inproceedings{niculae-etal-2017-argument,
title = "Argument Mining with Structured {SVM}s and {RNN}s",
author = "Niculae, Vlad and
Park, Joonsuk and
Cardie, Claire",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
... | 0 | 312 | 2023-06-26T10:07:42 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.014984130859375,
0.05718994140625,
0.0288543701171875,
-0.0350341796875,
0.046478271484375,
0.052520751953125,
0.005062103271484375,
0.051361083984375,
0.016998291015625,
-0.0521240234375,
-0.01496124267578125,
-0.0604248046875,
0.037... |
arielnlee/Superimposed-Masked-Dataset | 2023-08-01T18:08:45.000Z | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"occlusion",
"arxiv:2306.17848",
"region:us"
] | arielnlee | SMD is an occluded ImageNet-1K validation set, created to be an additional way to evaluate the impact of occlusion on model performance. This experiment used a variety of occluder objects that are not in the ImageNet-1K label space and are unambiguous in relationship to objects that reside in the label space. | @misc{lee2023hardwiring,
title={Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing},
author={Ariel N. Lee and Sarah Adel Bargal and Janavi Kasera and Stan Sclaroff and Kate Saenko and Nataniel Ruiz},
year={2023},
eprint={2306.17848},
archivePrefix={arXiv},
primaryClass={c... | 1 | 312 | 2023-06-28T05:07:48 | ---
license: other
task_categories:
- image-classification
language:
- en
tags:
- occlusion
size_categories:
- 10K<n<100K
---
# Superimposed Masked Dataset (SMD)
SMD is an occluded version of the ImageNet-1K validation set, created to serve as an additional way to evaluate the impact of occlusion on model performance.... | 2,086 | [
[
-0.0440673828125,
-0.039947509765625,
0.00478363037109375,
-0.0027618408203125,
-0.02313232421875,
0.00003266334533691406,
0.01190185546875,
-0.0196685791015625,
0.024871826171875,
0.061492919921875,
-0.04931640625,
-0.032928466796875,
-0.04248046875,
-0.011... |
awettig/Pile-ArXiv-0.5B-6K-opt | 2023-07-10T19:42:58.000Z | [
"region:us"
] | awettig | null | null | 0 | 312 | 2023-07-10T19:41:28 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6500959920
num_examples: 81380
- name: test
num_bytes: 64945692
num_examples: 813
download_size: 1581567196
da... | 523 | [
[
-0.0535888671875,
-0.00830841064453125,
-0.0007762908935546875,
0.01312255859375,
-0.036651611328125,
-0.004146575927734375,
0.041229248046875,
-0.01036834716796875,
0.051910400390625,
0.05230712890625,
-0.031707763671875,
-0.047607421875,
-0.044677734375,
-... |
result-kand2-sdxl-wuerst-karlo/4390ae17 | 2023-10-04T16:37:17.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 312 | 2023-10-04T16:37:16 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 175
num_examples: 10
download_size: 1353
dataset_size: 175
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "4390ae1... | 455 | [
[
-0.047271728515625,
0.0015192031860351562,
0.01629638671875,
0.0126953125,
-0.023406982421875,
-0.01451873779296875,
0.0236053466796875,
-0.02777099609375,
0.061981201171875,
0.031768798828125,
-0.06817626953125,
-0.052459716796875,
-0.033660888671875,
-0.00... |
sordonia/facts-text-davinci-003_clen128_maxD100_maxC-1 | 2023-10-13T19:29:48.000Z | [
"region:us"
] | sordonia | null | null | 0 | 312 | 2023-10-13T19:29:35 | ## model_name: text-davinci-003
## max_contexts_per_subject: -1
## max_documents_per_subject: 100
## max_context_length: 128
| 125 | [
[
-0.03289794921875,
-0.041015625,
0.05517578125,
0.031219482421875,
-0.042449951171875,
-0.03485107421875,
0.01561737060546875,
0.0230255126953125,
-0.005054473876953125,
0.035125732421875,
-0.056488037109375,
-0.03759765625,
-0.0689697265625,
0.0054702758789... |
bigbio/bioasq_task_b | 2022-12-22T15:41:12.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | bigbio | The data are intended to be used as training and development data for BioASQ
10, which will take place during 2022. There is one file containing the data:
- training10b.json
The file contains the data of the first nine editions of the challenge: 4234
questions [1] with their relevant documents, snippets, concepts and... | @article{tsatsaronis2015overview,
title = {
An overview of the BIOASQ large-scale biomedical semantic indexing and
question answering competition
},
author = {
Tsatsaronis, George and Balikas, Georgios and Malakasiotis, Prodromos
and Partalas, Ioannis and Zschunke, Matthias and Alvers, Mic... | 3 | 311 | 2022-09-26T04:05:28 | ---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: NLM_LICENSE
pretty_name: BioASQ Task B
homepage: http://participants-area.bioasq.org/datasets/
bigbio_pubmed: true
bigbio_public: false
bigbio_tasks:
- QUESTION_ANSWERING
---
# Dataset Card for BioASQ ... | 1,707 | [
[
-0.016632080078125,
-0.054443359375,
0.04278564453125,
0.004650115966796875,
-0.01236724853515625,
-0.0096435546875,
0.0077056884765625,
-0.03399658203125,
0.019256591796875,
0.0369873046875,
-0.04638671875,
-0.041290283203125,
-0.03173828125,
0.041656494140... |
nielsr/funsd-iob-original | 2022-11-19T13:38:09.000Z | [
"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}
} | 0 | 311 | 2022-11-19T13:30:51 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01494598388671875,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.0465087890625,
0.052490234375,
0.00505828857421875,
0.051361083984375,
0.0170135498046875,
-0.05206298828125,
-0.0149993896484375,
-0.06036376953125,
0.0379028320... |
awettig/Pile-YoutubeSubtitles-0.5B-6K-opt | 2023-07-10T19:35:45.000Z | [
"region:us"
] | awettig | null | null | 0 | 311 | 2023-07-10T19:34:17 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6500643383
num_examples: 81380
- name: test
num_bytes: 64945692
num_examples: 813
download_size: 1594423762
da... | 534 | [
[
-0.058807373046875,
-0.01003265380859375,
-0.01078033447265625,
0.0129547119140625,
-0.035919189453125,
0.00717926025390625,
0.02264404296875,
0.00974273681640625,
0.06829833984375,
0.04510498046875,
-0.055938720703125,
-0.035491943359375,
-0.05218505859375,
... |
awettig/Pile-HackerNews-0.5B-6K-opt | 2023-07-10T19:37:24.000Z | [
"region:us"
] | awettig | null | null | 0 | 311 | 2023-07-10T19:35:46 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6359132637
num_examples: 81380
- name: test
num_bytes: 64945692
num_examples: 813
download_size: 1710629426
da... | 528 | [
[
-0.045623779296875,
-0.01526641845703125,
-0.0003592967987060547,
0.026092529296875,
-0.03472900390625,
0.007110595703125,
0.033294677734375,
-0.01739501953125,
0.06732177734375,
0.047576904296875,
-0.04254150390625,
-0.035369873046875,
-0.040924072265625,
-... |
result-kand2-sdxl-wuerst-karlo/991f2e12 | 2023-10-04T17:42:55.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T17:42:54 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 154
num_examples: 10
download_size: 1300
dataset_size: 154
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "991f2e1... | 455 | [
[
-0.0413818359375,
-0.0173797607421875,
0.015625,
0.0310821533203125,
-0.00164794921875,
-0.0130462646484375,
0.026641845703125,
-0.00867462158203125,
0.0562744140625,
0.0270233154296875,
-0.0703125,
-0.039703369140625,
-0.039276123046875,
-0.008056640625,
... |
result-kand2-sdxl-wuerst-karlo/3ed8d887 | 2023-10-04T17:46:58.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T17:46:57 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 156
num_examples: 10
download_size: 1308
dataset_size: 156
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "3ed8d88... | 455 | [
[
-0.0396728515625,
-0.0004968643188476562,
0.0289459228515625,
0.019073486328125,
-0.0182952880859375,
-0.0099029541015625,
0.03851318359375,
-0.01178741455078125,
0.051025390625,
0.044647216796875,
-0.047210693359375,
-0.04351806640625,
-0.03436279296875,
-0... |
result-kand2-sdxl-wuerst-karlo/c50ece24 | 2023-10-04T17:50:04.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T17:50:03 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 163
num_examples: 10
download_size: 1317
dataset_size: 163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "c50ece2... | 455 | [
[
-0.048370361328125,
-0.004657745361328125,
0.01763916015625,
0.032318115234375,
-0.0039825439453125,
0.00868988037109375,
0.0088958740234375,
-0.025604248046875,
0.055419921875,
0.027374267578125,
-0.065673828125,
-0.0595703125,
-0.03399658203125,
-0.0147781... |
result-kand2-sdxl-wuerst-karlo/4b23c5a8 | 2023-10-04T17:54:41.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T17:54:38 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 178
num_examples: 10
download_size: 1335
dataset_size: 178
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "4b23c5a... | 455 | [
[
-0.04962158203125,
-0.005146026611328125,
0.0225372314453125,
0.0294952392578125,
-0.01214599609375,
0.007106781005859375,
0.0311279296875,
-0.020111083984375,
0.056060791015625,
0.032135009765625,
-0.05780029296875,
-0.05035400390625,
-0.034271240234375,
-0... |
result-kand2-sdxl-wuerst-karlo/c98495e0 | 2023-10-04T17:58:59.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T17:58:57 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 156
num_examples: 10
download_size: 1307
dataset_size: 156
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "c98495e... | 455 | [
[
-0.038543701171875,
0.0039825439453125,
0.0278167724609375,
0.0215911865234375,
-0.011474609375,
0.0017709732055664062,
0.0204010009765625,
-0.0180816650390625,
0.0631103515625,
0.0279998779296875,
-0.053253173828125,
-0.042755126953125,
-0.036651611328125,
... |
result-kand2-sdxl-wuerst-karlo/7709cb1f | 2023-10-04T18:03:33.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T18:03:32 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1331
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7709cb1... | 455 | [
[
-0.0421142578125,
-0.0169830322265625,
0.01515960693359375,
0.01959228515625,
-0.02825927734375,
-0.0079498291015625,
0.0306854248046875,
-0.00846099853515625,
0.068115234375,
0.0357666015625,
-0.043670654296875,
-0.0421142578125,
-0.040771484375,
-0.0106277... |
result-kand2-sdxl-wuerst-karlo/0415e725 | 2023-10-04T18:08:30.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T18:08:29 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 163
num_examples: 10
download_size: 1335
dataset_size: 163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "0415e72... | 455 | [
[
-0.04852294921875,
-0.01284027099609375,
0.0233612060546875,
0.027618408203125,
-0.01280975341796875,
-0.0240020751953125,
0.019989013671875,
-0.020477294921875,
0.06683349609375,
0.031890869140625,
-0.060028076171875,
-0.048309326171875,
-0.0325927734375,
0... |
result-kand2-sdxl-wuerst-karlo/23611323 | 2023-10-04T18:18:01.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T18:18:00 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 166
num_examples: 10
download_size: 1336
dataset_size: 166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "2361132... | 455 | [
[
-0.048828125,
0.0017290115356445312,
0.0185394287109375,
0.0244598388671875,
-0.0157928466796875,
-0.016571044921875,
0.0275421142578125,
-0.007572174072265625,
0.057281494140625,
0.0364990234375,
-0.049591064453125,
-0.0355224609375,
-0.03533935546875,
-0.0... |
lighteval/EntityMatching | 2023-05-09T15:35:01.000Z | [
"region:us"
] | lighteval | null | @inproceedings{mudgal2018deep,
title={Deep learning for entity matching: A design space exploration},
author={Mudgal, Sidharth and Li, Han and Rekatsinas, Theodoros and Doan, AnHai and Park, Youngchoon and Krishnan, Ganesh and Deep, Rohit and Arcaute, Esteban and Raghavendra, Vijay},
booktitle={Proceedings of the... | 2 | 310 | 2023-05-09T14:56:27 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
toughdata/quora-question-answer-dataset | 2023-08-28T13:36:21.000Z | [
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"license:gpl-3.0",
"question",
"answer",
"quora",
"region:us"
] | toughdata | null | null | 0 | 310 | 2023-08-23T22:53:09 | ---
license: gpl-3.0
task_categories:
- question-answering
- conversational
- text2text-generation
language:
- en
tags:
- question
- answer
- quora
pretty_name: Quora Question/Answer Pairs
---
Quora Question Answer Dataset (Quora-QuAD) contains 56,402 question-answer pairs scraped from Quora.
# Usage:
For instructions... | 485 | [
[
-0.06573486328125,
-0.0545654296875,
0.027801513671875,
-0.01910400390625,
-0.016326904296875,
-0.00360107421875,
0.0218658447265625,
-0.0097503662109375,
0.01439666748046875,
0.046356201171875,
-0.04852294921875,
-0.00817108154296875,
-0.0013637542724609375,
... |
yzhuang/autotree_snnxor_n15_l1_10 | 2023-09-18T21:51:32.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 310 | 2023-09-05T17:49:19 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... | 880 | [
[
-0.0312042236328125,
-0.0124359130859375,
0.0019779205322265625,
0.0258026123046875,
-0.007965087890625,
0.00855255126953125,
0.034912109375,
-0.00873565673828125,
0.06353759765625,
0.027740478515625,
-0.0562744140625,
-0.03863525390625,
-0.055877685546875,
... |
result-kand2-sdxl-wuerst-karlo/eacbe536 | 2023-10-04T18:13:45.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:13:44 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 173
num_examples: 10
download_size: 1377
dataset_size: 173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eacbe53... | 455 | [
[
-0.049041748046875,
-0.0030612945556640625,
0.01971435546875,
0.025787353515625,
-0.00740814208984375,
-0.01015472412109375,
0.032196044921875,
-0.0281524658203125,
0.06182861328125,
0.0360107421875,
-0.06915283203125,
-0.0555419921875,
-0.03021240234375,
-0... |
result-kand2-sdxl-wuerst-karlo/b4de2e4d | 2023-10-04T18:26:47.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:26:46 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 193
num_examples: 10
download_size: 1385
dataset_size: 193
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b4de2e4... | 455 | [
[
-0.045166015625,
-0.01427459716796875,
0.0220794677734375,
0.0269622802734375,
-0.00830078125,
-0.0008192062377929688,
0.030029296875,
-0.0234527587890625,
0.04730224609375,
0.036346435546875,
-0.059783935546875,
-0.05316162109375,
-0.038116455078125,
-0.004... |
result-kand2-sdxl-wuerst-karlo/7acd34b3 | 2023-10-04T18:30:20.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:30:19 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 166
num_examples: 10
download_size: 1331
dataset_size: 166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7acd34b... | 455 | [
[
-0.0635986328125,
-0.004726409912109375,
0.0245819091796875,
0.0298614501953125,
-0.015899658203125,
-0.000598907470703125,
0.032012939453125,
-0.0192718505859375,
0.052215576171875,
0.039215087890625,
-0.054107666015625,
-0.049713134765625,
-0.038543701171875,
... |
result-kand2-sdxl-wuerst-karlo/fc5ced1b | 2023-10-04T18:33:32.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:33:31 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 149
num_examples: 10
download_size: 1316
dataset_size: 149
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "fc5ced1... | 455 | [
[
-0.059326171875,
-0.00945281982421875,
0.0164642333984375,
0.0280303955078125,
-0.0145111083984375,
0.0016450881958007812,
0.0231170654296875,
-0.0209197998046875,
0.04864501953125,
0.029144287109375,
-0.0784912109375,
-0.0621337890625,
-0.0335693359375,
-0.... |
result-kand2-sdxl-wuerst-karlo/7f43ba07 | 2023-10-04T18:38:54.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:38:54 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 188
num_examples: 10
download_size: 1352
dataset_size: 188
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7f43ba0... | 455 | [
[
-0.059173583984375,
-0.0021953582763671875,
0.0099945068359375,
0.017791748046875,
-0.0233306884765625,
-0.0113525390625,
0.0307464599609375,
-0.023406982421875,
0.052276611328125,
0.03839111328125,
-0.05560302734375,
-0.04681396484375,
-0.03955078125,
-0.00... |
result-kand2-sdxl-wuerst-karlo/a8072b85 | 2023-10-04T18:47:47.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:47:46 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 170
num_examples: 10
download_size: 1348
dataset_size: 170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a8072b8... | 455 | [
[
-0.041107177734375,
-0.0091400146484375,
0.0103607177734375,
0.01126861572265625,
-0.018341064453125,
-0.005748748779296875,
0.035064697265625,
-0.011383056640625,
0.061065673828125,
0.040863037109375,
-0.044677734375,
-0.039337158203125,
-0.043487548828125,
... |
result-kand2-sdxl-wuerst-karlo/be76ce08 | 2023-10-04T18:52:19.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:52:19 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 178
num_examples: 10
download_size: 1354
dataset_size: 178
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "be76ce0... | 455 | [
[
-0.0361328125,
-0.0005946159362792969,
0.00640106201171875,
0.013702392578125,
-0.02606201171875,
-0.01038360595703125,
0.016265869140625,
-0.0201263427734375,
0.064208984375,
0.0394287109375,
-0.0538330078125,
-0.0474853515625,
-0.051055908203125,
-0.001500... |
mozilla-foundation/common_voice_6_1 | 2023-07-29T16:00:07.000Z | [
"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... | 4 | 309 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- n<1K
ar:
- 10K<n<100K
as:
- n<1K
br:
- 10K<n<100K
ca:
- 100K<n<1M
cnh:
- 1K<n<10K
cs:
- 10K<n<100K
cv:
- 10K<n<100K
cy:
- 10K<n<100K
... | 10,751 | [
[
-0.0401611328125,
-0.054473876953125,
0.00981903076171875,
0.0335693359375,
-0.0188751220703125,
0.0024566650390625,
-0.04266357421875,
-0.017059326171875,
0.03216552734375,
0.040985107421875,
-0.057373046875,
-0.0712890625,
-0.03289794921875,
0.018432617187... |
result-kand2-sdxl-wuerst-karlo/980edb53 | 2023-10-04T18:22:22.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 309 | 2023-10-04T18:22:21 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 156
num_examples: 10
download_size: 1319
dataset_size: 156
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "980edb5... | 455 | [
[
-0.04254150390625,
-0.00428009033203125,
0.0166015625,
0.0298309326171875,
-0.0147247314453125,
-0.004039764404296875,
0.02496337890625,
-0.006862640380859375,
0.059722900390625,
0.02874755859375,
-0.05859375,
-0.04229736328125,
-0.03350830078125,
-0.0235137... |
result-kand2-sdxl-wuerst-karlo/f7f54a55 | 2023-10-04T18:43:41.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 309 | 2023-10-04T18:43:40 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 141
num_examples: 10
download_size: 1325
dataset_size: 141
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f7f54a5... | 455 | [
[
-0.042633056640625,
-0.0103912353515625,
0.00714874267578125,
0.017059326171875,
-0.0207977294921875,
-0.005413055419921875,
0.0303192138671875,
-0.01739501953125,
0.04833984375,
0.03179931640625,
-0.052978515625,
-0.04241943359375,
-0.039764404296875,
-0.00... |
mteb/toxic_conversations_50k | 2022-09-27T19:14:35.000Z | [
"language:en",
"region:us"
] | mteb | null | null | 3 | 307 | 2022-05-26T17:47:49 | ---
language:
- en
---
# Toxic Conversation
This is a version of the [Jigsaw Unintended Bias in Toxicity Classification dataset](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview). It contains comments from the Civil Comments platform together with annotations if the comment is toxic... | 588 | [
[
-0.0236358642578125,
-0.03680419921875,
0.028228759765625,
0.01435089111328125,
-0.033294677734375,
0.0206451416015625,
0.0171966552734375,
-0.0193023681640625,
0.0191497802734375,
0.0499267578125,
-0.05902099609375,
-0.034881591796875,
-0.0479736328125,
-0.... |
casehold/casehold | 2023-10-04T19:55:29.000Z | [
"region:us"
] | casehold | CaseHOLD (Case Holdings On Legal Decisions) is a law dataset comprised of over 53,000+ multiple choice questions to identify the relevant holding of a cited case. | @inproceedings{zhengguha2021,
title={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset},
author={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho},
year={2021},
eprint={2104.08671},
archivePrefix={arXiv},
primary... | 5 | 307 | 2023-03-27T23:04:36 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
dmrau/trec_dl20-qrels | 2023-10-09T08:28:57.000Z | [
"region:us"
] | dmrau | null | null | 0 | 307 | 2023-10-06T11:23:29 | ---
dataset_info:
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: string
splits:
- name: test
num_bytes: 298319
num_examples: 11386
download_size: 0
dataset_size: 298319
configs:
- config_name: default
data_files:
- split: test
pa... | 509 | [
[
-0.04681396484375,
0.007656097412109375,
0.01763916015625,
0.0161590576171875,
-0.0167999267578125,
0.031646728515625,
0.03302001953125,
-0.0099945068359375,
0.03997802734375,
0.034027099609375,
-0.07354736328125,
-0.0667724609375,
-0.029571533203125,
-0.007... |
hmao/reformatted_singleapi_openai | 2023-10-23T23:26:04.000Z | [
"region:us"
] | hmao | null | null | 0 | 307 | 2023-10-21T03:43:10 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: api_name
dtype: string
- name: api_definition
dtype: string
- name: dataset_name
dtype: string
splits:
- name: train
num_bytes: 21189
num_examples: 14
download_size... | 536 | [
[
-0.0316162109375,
-0.00872802734375,
-0.0035839080810546875,
0.0166015625,
-0.0173797607421875,
-0.024200439453125,
0.0044708251953125,
0.00043582916259765625,
0.0697021484375,
0.042236328125,
-0.0645751953125,
-0.0472412109375,
-0.01549530029296875,
-0.0181... |
code_x_glue_cc_cloze_testing_all | 2023-06-01T14:59:51.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:code",
"license:c-uda",... | null | Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing dataset... | @article{CodeXGLUE,
title={CodeXGLUE: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and... | 3 | 306 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: CodeXGlueCcClozeTestingAll
dataset_info:... | 11,892 | [
[
-0.03741455078125,
-0.04962158203125,
0.025848388671875,
0.0165252685546875,
-0.0189208984375,
0.0153961181640625,
-0.0189971923828125,
-0.01165771484375,
0.0440673828125,
0.033477783203125,
-0.064453125,
-0.06207275390625,
-0.037261962890625,
0.004241943359... |
ARTeLab/ilpost | 2022-11-17T02:50:32.000Z | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:it",
"region:us"
] | ARTeLab | null | null | 2 | 306 | 2022-03-02T23:29:22 | ---
language:
- it
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- summarization
---
# Dataset Card for ilpost
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Langua... | 4,121 | [
[
-0.043182373046875,
-0.0322265625,
0.00830841064453125,
0.01226806640625,
-0.0311279296875,
0.0036602020263671875,
-0.0189056396484375,
-0.036468505859375,
0.03948974609375,
0.03350830078125,
-0.03765869140625,
-0.0777587890625,
-0.059051513671875,
0.0349731... |
pietrolesci/stress_tests_nli | 2022-04-25T09:32:28.000Z | [
"region:us"
] | pietrolesci | null | null | 0 | 306 | 2022-04-25T09:21:50 | ## Overview
Original dataset page [here](https://abhilasharavichander.github.io/NLI_StressTest/) and dataset available [here](https://drive.google.com/open?id=1faGA5pHdu5Co8rFhnXn-6jbBYC2R1dhw).
## Dataset curation
Added new column `label` with encoded labels with the following mapping
```
{"entailment": 0, "neutra... | 2,622 | [
[
-0.027496337890625,
-0.059356689453125,
0.010162353515625,
0.033447265625,
-0.01546478271484375,
-0.01319122314453125,
-0.01032257080078125,
-0.004634857177734375,
0.025421142578125,
0.032989501953125,
-0.03509521484375,
-0.0472412109375,
-0.0428466796875,
0... |
pierreguillou/DocLayNet-base | 2023-05-17T08:56:30.000Z | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:token-classification",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:1K<n<10K",
"language:en",
"language:de",
"language:fr",
"language:ja",
"license:other",
"Doc... | pierreguillou | Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adeq... | @article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},
doi = {10.1145/3534678.353904},
url = {https://arxiv.org/abs/2206.01062},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022}
} | 7 | 306 | 2023-01-25T17:53:26 | ---
language:
- en
- de
- fr
- ja
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet base
size_categories:
- 1K<n<10K
tags:
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-de... | 13,860 | [
[
-0.039764404296875,
-0.041839599609375,
0.0204925537109375,
0.022125244140625,
-0.0090179443359375,
-0.02276611328125,
-0.0036220550537109375,
-0.026641845703125,
0.032318115234375,
0.042388916015625,
-0.034393310546875,
-0.04925537109375,
-0.038055419921875,
... |
RIPS-Goog-23/RVL-CDIP | 2023-06-29T06:25:59.000Z | [
"region:us"
] | RIPS-Goog-23 | null | null | 0 | 306 | 2023-06-26T08:50:52 | 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... |
open-source-metrics/stars | 2023-09-06T18:46:39.000Z | [
"region:us"
] | open-source-metrics | null | null | 0 | 305 | 2023-03-23T12:51:59 | ---
dataset_info:
features:
- name: login
dtype: string
- name: dates
dtype: string
splits:
- name: peft
num_bytes: 350334
num_examples: 9427
- name: hub_docs
num_bytes: 6113
num_examples: 163
- name: evaluate
num_bytes: 56836
num_examples: 1517
- name: huggingface_hub
... | 1,868 | [
[
-0.040924072265625,
-0.01561737060546875,
0.0184783935546875,
0.00911712646484375,
-0.010833740234375,
0.006320953369140625,
0.015960693359375,
-0.02508544921875,
0.061370849609375,
0.04156494140625,
-0.06365966796875,
-0.048095703125,
-0.048919677734375,
-0... |
THUDM/webglm-qa | 2023-07-12T17:14:35.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"multilinguality:monolingual",
"size_categories:100M<n<200M",
"language:en",
"arxiv:2306.07906",
"region:us"
] | THUDM | null | null | 19 | 305 | 2023-07-11T16:59:04 | ---
annotations_creators: []
language:
- en
multilinguality:
- monolingual
source_datasets: []
task_categories:
- text-generation
- question-answering
pretty_name: WebGLM-QA
size_categories:
- 100M<n<200M
---
# WebGLM-QA
## Dataset Description
[WebGLM-QA](https://github.com/THUDM/WebGLM) is the dataset used to train t... | 4,264 | [
[
-0.047119140625,
-0.05255126953125,
0.0222320556640625,
0.01334381103515625,
-0.00661468505859375,
-0.00279998779296875,
0.0199127197265625,
-0.0145111083984375,
-0.01406097412109375,
0.039306640625,
-0.034210205078125,
-0.033782958984375,
-0.00952911376953125,
... |
result-kand2-sdxl-wuerst-karlo/ac298fb2 | 2023-10-04T23:40:46.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 305 | 2023-10-04T23:40:45 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 165
num_examples: 10
download_size: 1316
dataset_size: 165
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ac298fb... | 455 | [
[
-0.04827880859375,
-0.01092529296875,
0.01209259033203125,
0.0240631103515625,
-0.006031036376953125,
0.004909515380859375,
0.0272979736328125,
-0.0162811279296875,
0.05377197265625,
0.0313720703125,
-0.059295654296875,
-0.03717041015625,
-0.038055419921875,
... |
code_x_glue_cc_cloze_testing_maxmin | 2023-06-01T14:59:51.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:code",
"license:c-uda",... | null | Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing dataset... | @article{CodeXGLUE,
title={CodeXGLUE: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and... | 1 | 304 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: CodeXGlueCcClozeTestingMaxmin
dataset_in... | 13,565 | [
[
-0.037139892578125,
-0.04437255859375,
0.01091766357421875,
0.031463623046875,
-0.021759033203125,
0.0160064697265625,
-0.0165252685546875,
-0.01068115234375,
0.0439453125,
0.01335906982421875,
-0.050079345703125,
-0.063232421875,
-0.038116455078125,
0.01517... |
aboonaji/alpaca_micro_demo | 2023-08-08T13:57:18.000Z | [
"region:us"
] | aboonaji | null | null | 0 | 304 | 2023-08-08T13:00:10 | 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... |
inkoziev/jokes_dialogues | 2023-02-19T07:07:16.000Z | [
"task_categories:conversational",
"language:ru",
"license:cc-by-nc-4.0",
"region:us"
] | inkoziev | null | null | 1 | 303 | 2023-02-18T11:59:12 | ---
license: cc-by-nc-4.0
task_categories:
- conversational
language:
- ru
---
# Диалоги из анекдотов и шуток
Датасет содержит результат парсинга анекдотов, наскрапленных с разных сайтов.
## Формат
Каждый сэмпл содержит четыре поля:
"context" - контекст диалога, включая все недиалоговые вставки. Обратите внимание... | 839 | [
[
-0.0275726318359375,
-0.05426025390625,
0.0311126708984375,
0.015289306640625,
-0.035797119140625,
0.006954193115234375,
0.00984954833984375,
-0.011871337890625,
0.0413818359375,
0.01486968994140625,
-0.0560302734375,
-0.03863525390625,
-0.033660888671875,
0... |
hate_offensive | 2023-01-25T14:31:32.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"hate-speech-detection",
... | null | null | @article{article,
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
year = {2017},
month = {03},
pages = {},
title = {Automated Hate Speech Detection and the Problem of Offensive Language}
} | 6 | 301 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: hate-speech-and-offensiv... | 4,719 | [
[
-0.029815673828125,
-0.05218505859375,
-0.006404876708984375,
0.01287078857421875,
-0.01340484619140625,
0.02423095703125,
-0.0284576416015625,
-0.03302001953125,
0.028717041015625,
0.01554107666015625,
-0.046234130859375,
-0.07989501953125,
-0.0672607421875,
... |
AlekseyKorshuk/hellaswag | 2022-06-06T10:33:23.000Z | [
"region:us"
] | AlekseyKorshuk | null | null | 2 | 301 | 2022-06-06T10:33:09 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01497650146484375,
0.057098388671875,
0.028839111328125,
-0.0350341796875,
0.046478271484375,
0.052520751953125,
0.005046844482421875,
0.051361083984375,
0.016998291015625,
-0.05206298828125,
-0.01497650146484375,
-0.06036376953125,
0... |
ds4sd/DocLayNet | 2023-01-25T17:01:19.000Z | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:10K<n<100K",
"license:other",
"layout-segmentation",
"COCO",
"document-understanding",
"PDF",
"region:us"
] | ds4sd | DocLayNet is a human-annotated document layout segmentation dataset from a broad variety of document sources. | @article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},
doi = {10.1145/3534678.353904},
url = {https://arxiv.org/abs/2206.01062},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022}
} | 24 | 301 | 2023-01-17T07:51:59 | ---
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet
size_categories:
- 10K<n<100K
tags:
- layout-segmentation
- COCO
- document-understanding
- PDF
task_categories:
- object-detection
- image-segmentation
task_ids:
- instance-segmentation
---
# Dataset Card for DocLayNet
## Table of Content... | 5,569 | [
[
-0.038360595703125,
-0.023834228515625,
0.031402587890625,
0.00969696044921875,
-0.01361846923828125,
-0.0045318603515625,
-0.0022907257080078125,
-0.0247344970703125,
0.0255126953125,
0.038116455078125,
-0.0306854248046875,
-0.07061767578125,
-0.037811279296875... |
vietgpt/openbookqa_en | 2023-06-03T22:16:08.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"SFT",
"region:us"
] | vietgpt | null | null | 0 | 301 | 2023-06-03T22:08:45 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question_stem
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 895386
num_examples: 4957
... | 1,998 | [
[
-0.015625,
-0.05523681640625,
0.01922607421875,
0.00867462158203125,
0.0012874603271484375,
-0.02630615234375,
-0.00508880615234375,
0.005008697509765625,
-0.01207733154296875,
0.02581787109375,
-0.050689697265625,
-0.038299560546875,
-0.019561767578125,
0.0... |
spacemanidol/dset-corpus | 2023-09-27T19:17:42.000Z | [
"region:us"
] | spacemanidol | null | 0 | 301 | 2023-09-21T18:52:00 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.014984130859375,
0.05718994140625,
0.0288543701171875,
-0.0350341796875,
0.046478271484375,
0.052520751953125,
0.005062103271484375,
0.051361083984375,
0.016998291015625,
-0.0521240234375,
-0.01496124267578125,
-0.0604248046875,
0.037... | |
laion/laion1B-nolang-aesthetic | 2022-05-22T13:40:12.000Z | [
"region:us"
] | laion | null | null | 0 | 300 | 2022-05-22T12:34:57 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
shmuhammad/AfriSenti-twitter-sentiment | 2023-09-03T09:59:15.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categor... | shmuhammad | AfriSenti is the largest sentiment analysis benchmark dataset for under-represented African languages---covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and yo... | @inproceedings{muhammad-etal-2023-semeval,
title="{S}em{E}val-2023 Task 12: Sentiment Analysis for African Languages ({A}fri{S}enti-{S}em{E}val)",
author="Muhammad, Shamsuddeen Hassan and
Yimam, Seid and
Abdulmumin, Idris and
Ahmad, Ibrahim Sa'id and
Ousidhoum, Nedjma, and
Ayele, Abinew, and
... | 3 | 300 | 2023-02-16T21:02:20 | ---
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
- sentiment-scoring
- semantic-similarity-classification
- semantic-similarity-scoring
tags:
- sentiment analysis, Twitter, tweets
- sentiment
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
l... | 9,115 | [
[
-0.0531005859375,
-0.03131103515625,
-0.011749267578125,
0.042327880859375,
-0.0196685791015625,
-0.006351470947265625,
-0.0258941650390625,
-0.03302001953125,
0.056060791015625,
0.01551055908203125,
-0.04339599609375,
-0.05908203125,
-0.056610107421875,
0.0... |
masakhane/afriqa | 2023-07-07T16:57:28.000Z | [
"task_categories:question-answering",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"language:bem",
"language:fon",
"language:ha",
"language:ig",
"language:kin",
"language:sw",
"language:wo",
"language:yo",
"language:zu",
"language:tw",
"license:cc-by-sa-4.0",
"cross-ling... | masakhane | AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages
AfriQA is the first cross-lingual question answering (QA) dataset with a focus on African languages.
The dataset includes over 12,000 XOR QA examples across 10 African languages, making it an invaluable resource for developing more equitabl... | \ | 5 | 300 | 2023-04-23T20:05:43 | ---
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- bem
- fon
- ha
- ig
- kin
- sw
- wo
- yo
- zu
- tw
pretty_name: AfriQA
size_categories:
- 10K<n<100K
multilinguality:
- multilingual
tags:
- cross-lingual
- question-answering
- qa
---
# Dataset Card for AfriQA
## Table of Contents
- [Table o... | 6,574 | [
[
-0.050689697265625,
-0.0452880859375,
0.00830841064453125,
0.01523590087890625,
-0.006374359130859375,
-0.0006761550903320312,
-0.01043701171875,
-0.01812744140625,
0.03875732421875,
0.033477783203125,
-0.049041748046875,
-0.037750244140625,
-0.042633056640625,
... |
juletxara/pawsx_mt | 2023-07-21T10:18:49.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:exper... | juletxara | PAWS-X, a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages.
This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine
translated training pairs in six typologically distinct languages: French, Spanish, German,
Chinese, Japanese, and Korean. Engl... | @InProceedings{pawsx2019emnlp,
title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
booktitle = {Proc. of EMNLP},
year = {2019}
} | 0 | 299 | 2023-05-23T10:39:03 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- expert-generated
- machine-generated
language:
- en
license:
- other
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-paws
task_categories:
- text-classification
task_ids:
- semantic-simi... | 31,253 | [
[
-0.02410888671875,
-0.0297088623046875,
0.025665283203125,
0.034881591796875,
-0.0292816162109375,
0.013092041015625,
-0.017181396484375,
-0.03436279296875,
0.053558349609375,
0.0443115234375,
-0.0364990234375,
-0.058624267578125,
-0.036834716796875,
0.02803... |
jxie/guacamol | 2023-08-03T23:49:15.000Z | [
"region:us"
] | jxie | null | null | 0 | 299 | 2023-08-03T23:49:05 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 65660530
num_examples: 1273104
- name: validation
num_bytes: 4097829
num_examples: 79568
- name: test
num_bytes: 12306244
num_examples: 238706
download_size: 45009159
dataset_size: 8206460... | 488 | [
[
-0.0308685302734375,
-0.041107177734375,
0.0206298828125,
0.02587890625,
-0.01397705078125,
0.0017251968383789062,
0.01319122314453125,
-0.0247802734375,
0.06719970703125,
0.0216064453125,
-0.048492431640625,
-0.060638427734375,
-0.05670166015625,
-0.0184783... |
siyue/squall | 2023-09-08T06:08:06.000Z | [
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | siyue | To explore the utility of fine-grained, lexical-level supervision, authors introduce SQUALL, a dataset that enriches 11,276 WikiTableQuestions \
English-language questions with manually created SQL equivalents plus \
alignments between SQL and question fragments. | @inproceedings{Shi:Zhao:Boyd-Graber:Daume-III:Lee-2020,
Title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries},
Author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee},
Booktitle = {Findings of EMNLP},
Year = {2020},
} | 0 | 299 | 2023-09-02T06:59:17 | ---
license: mit
task_categories:
- table-question-answering
language:
- en
pretty_name: SQUALL
size_categories:
- 10K<n<100K
---
## SQUALL Dataset
To explore the utility of fine-grained, lexical-level supervision, authors introduce SQUALL, a dataset that enriches 11,276 WikiTableQuestions English-language questions ... | 3,398 | [
[
-0.0237579345703125,
-0.042022705078125,
0.0184783935546875,
0.0152130126953125,
-0.0016069412231445312,
-0.020721435546875,
-0.0017347335815429688,
-0.0160064697265625,
0.0391845703125,
0.013031005859375,
-0.02960205078125,
-0.06732177734375,
-0.023834228515625... |
maxolotl/must-c-en-es-wait3-02 | 2023-10-22T07:48:24.000Z | [
"region:us"
] | maxolotl | null | null | 0 | 298 | 2023-10-22T07:48:05 | ---
dataset_info:
features:
- name: current_source
dtype: string
- name: current_target
dtype: string
- name: target_token
dtype: string
splits:
- name: train
num_bytes: 995120593
num_examples: 5240243
- name: test
num_bytes: 9960448
num_examples: 57187
- name: validation
... | 597 | [
[
-0.045745849609375,
-0.007747650146484375,
0.0330810546875,
0.05291748046875,
-0.007568359375,
-0.007843017578125,
0.0222015380859375,
-0.034423828125,
0.055145263671875,
0.04205322265625,
-0.07891845703125,
-0.040802001953125,
-0.04400634765625,
0.013450622... |
xed_en_fi | 2023-06-01T14:59:50.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_catego... | null | A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BER... | @inproceedings{ohman2020xed,
title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},
author={{\"O}hman, Emily and P{\"a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},
booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
year={2020}
} | 6 | 297 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
- fi
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- extended|other-OpenSubtitles2016
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-c... | 6,476 | [
[
-0.0418701171875,
-0.02337646484375,
0.007568359375,
0.016632080078125,
-0.031402587890625,
0.0028705596923828125,
-0.0292510986328125,
-0.029205322265625,
0.045074462890625,
0.0198974609375,
-0.068359375,
-0.078857421875,
-0.041015625,
0.0222930908203125,
... |
mteb/reddit-clustering-p2p | 2022-09-27T19:13:59.000Z | [
"language:en",
"region:us"
] | mteb | null | null | 0 | 297 | 2022-05-11T08:52:19 | ---
language:
- en
---
10 sets with the following stats:
1. 91 labels & 15592 samples
2. 64 labels & 79172 samples
3. 38 labels & 1942 samples
4. 11 labels & 13224 samples
5. 64 labels & 92303 samples
6. 87 labels & 28607 samples
7. 10 labels & 69146 samples
8. 48 labels & 67469 samples
9. 64 labels & 29683 samples
1... | 428 | [
[
-0.047454833984375,
-0.01357269287109375,
0.026153564453125,
0.040191650390625,
-0.0034809112548828125,
-0.0010824203491210938,
0.0062713623046875,
-0.00981903076171875,
0.0361328125,
0.0396728515625,
-0.033660888671875,
-0.052490234375,
-0.0310821533203125,
... |
nlp-guild/intent-recognition-biomedical | 2022-09-22T16:13:44.000Z | [
"license:mit",
"region:us"
] | nlp-guild | null | null | 0 | 297 | 2022-09-22T16:10:30 | ---
license: mit
---
[source](https://github.com/wangle1218/KBQA-for-Diagnosis/tree/main/nlu/bert_intent_recognition/data) | 123 | [
[
0.00183868408203125,
-0.0440673828125,
0.052276611328125,
0.016876220703125,
-0.00934600830078125,
-0.032012939453125,
0.0014190673828125,
-0.036895751953125,
0.023223876953125,
0.01556396484375,
-0.055145263671875,
-0.040313720703125,
-0.0103302001953125,
-... |
GabeHD/pokemon-type-captions | 2022-10-23T04:40:59.000Z | [
"region:us"
] | GabeHD | null | null | 3 | 297 | 2022-10-18T08:38:18 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 19372532.0
num_examples: 898
download_size: 0
dataset_size: 19372532.0
---
# Dataset Card for Pokémon type captions
Contains official artwork and type-specific caption for Po... | 832 | [
[
-0.0224761962890625,
-0.007171630859375,
0.004505157470703125,
0.0259552001953125,
-0.0377197265625,
0.014862060546875,
0.00984954833984375,
-0.02288818359375,
0.056549072265625,
0.036529541015625,
-0.0465087890625,
-0.022369384765625,
-0.02667236328125,
0.0... |
banghua/tldr_reward_model_labeled | 2023-09-21T19:08:04.000Z | [
"region:us"
] | banghua | null | null | 0 | 297 | 2023-08-06T17:18:36 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 300444471.0
num_examples: 176163
download_size: 177215543
dataset_size: 300444471.0
configs:
- config_name: default
data_files:
- ... | 539 | [
[
-0.01074981689453125,
-0.01335906982421875,
0.005748748779296875,
0.004261016845703125,
-0.006866455078125,
0.00909423828125,
0.020355224609375,
-0.01261138916015625,
0.037017822265625,
0.032318115234375,
-0.047637939453125,
-0.05670166015625,
-0.05364990234375,... |
roszcz/giant-midi-masked-v3 | 2023-10-03T18:34:23.000Z | [
"region:us"
] | roszcz | null | null | 0 | 297 | 2023-10-03T16:25:29 | ---
dataset_info:
features:
- name: pitch
sequence: int8
length: 90
- name: start
sequence: float64
length: 90
- name: dstart
sequence: float64
length: 90
- name: end
sequence: float64
length: 90
- name: duration
sequence: float64
length: 90
- name: velocity
seq... | 1,062 | [
[
-0.05120849609375,
-0.01222991943359375,
0.0273895263671875,
0.0217742919921875,
-0.0198822021484375,
0.005809783935546875,
0.0225067138671875,
-0.0266265869140625,
0.07073974609375,
0.054229736328125,
-0.060546875,
-0.05279541015625,
-0.041961669921875,
-0.... |
alexandrainst/nst-da | 2023-10-05T14:27:00.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"size_categories:100K<n<1M",
"language:da",
"license:cc0-1.0",
"region:us"
] | alexandrainst | null | null | 2 | 297 | 2023-10-05T11:27:17 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: age
dtype: i... | 4,217 | [
[
-0.05340576171875,
-0.03631591796875,
0.008087158203125,
0.0235137939453125,
-0.02947998046875,
-0.01715087890625,
-0.02691650390625,
-0.0213623046875,
0.038818359375,
0.039520263671875,
-0.043212890625,
-0.052215576171875,
-0.038818359375,
0.016006469726562... |
TheFusion21/PokemonCards | 2022-11-21T18:28:25.000Z | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"reg... | TheFusion21 | null | null | 6 | 296 | 2022-11-20T14:14:51 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: Pokemoncards
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- text-to-image
- image-to-text
task_ids:
- image-captioning
---
#... | 2,765 | [
[
-0.0182037353515625,
-0.0303802490234375,
0.00870513916015625,
0.01274871826171875,
-0.029510498046875,
0.00882720947265625,
0.003498077392578125,
-0.0300445556640625,
0.0626220703125,
0.049652099609375,
-0.04180908203125,
-0.04595947265625,
-0.0440673828125,
... |
microsoft/LCC_python | 2023-06-21T03:13:06.000Z | [
"region:us"
] | microsoft | null | null | 1 | 296 | 2023-06-21T03:12:37 | ---
dataset_info:
features:
- name: gt
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 1761900743
num_examples: 100000
- name: validation
num_bytes: 146577328
num_examples: 10000
- name: test
num_bytes: 149430294
num_examples: 10000
download_s... | 530 | [
[
-0.03448486328125,
-0.01345062255859375,
0.0110931396484375,
0.0140838623046875,
-0.0032863616943359375,
-0.002315521240234375,
0.004390716552734375,
-0.0034942626953125,
0.03729248046875,
0.028411865234375,
-0.05950927734375,
-0.05596923828125,
-0.0255584716796... |
heegyu/hh-rlhf-vicuna-format | 2023-09-06T03:07:11.000Z | [
"region:us"
] | heegyu | null | null | 1 | 296 | 2023-08-28T08:37:18 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: chosen
struct:
- name: from
dtype: string
- name: value
dtype: string
- name: rejected
struct:
- name: from
dtype: s... | 2,046 | [
[
-0.0127105712890625,
-0.04656982421875,
0.037078857421875,
0.0148162841796875,
-0.058258056640625,
-0.0155029296875,
0.0128631591796875,
-0.0294952392578125,
0.060272216796875,
0.051116943359375,
-0.038360595703125,
-0.069580078125,
-0.017730712890625,
0.018... |
Geonmo/midjourney-prompts-only | 2023-10-25T09:12:51.000Z | [
"region:us"
] | Geonmo | null | null | 0 | 296 | 2023-10-25T09:10:06 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 495800699
num_examples: 3492544
download_size: 334934157
dataset_size: 495800699
---
# Dataset Card for "midjourney-prompts-only"
[More Information needed](https://github.com/huggingface/datasets/blob/main/C... | 374 | [
[
-0.03472900390625,
-0.022369384765625,
0.043975830078125,
0.042083740234375,
-0.0213470458984375,
-0.01184844970703125,
0.0110626220703125,
0.013946533203125,
0.061248779296875,
0.032379150390625,
-0.1065673828125,
-0.044830322265625,
-0.0305633544921875,
-0... |
wdc/products-2017 | 2022-10-23T05:50:24.000Z | [
"task_categories:text-classification",
"annotations_creators:weak supervision",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | wdc | Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from d... | @inproceedings{primpeli2019wdc,
title={The WDC training dataset and gold standard for large-scale product matching},
author={Primpeli, Anna and Peeters, Ralph and Bizer, Christian},
booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
pages={381--386},
year={2019}
} | 2 | 295 | 2022-05-16T13:23:21 | ---
annotations_creators:
- weak supervision
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- unknown
multilinguality:
- monolingual
pretty_name: products-2017
size_categories:
- 1K<n<10K
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
- data-integration
task_ids:
- ... | 6,208 | [
[
-0.046722412109375,
-0.04791259765625,
0.01108551025390625,
0.014617919921875,
-0.0115966796875,
-0.0041656494140625,
-0.0185089111328125,
-0.04498291015625,
0.0135650634765625,
0.01556396484375,
-0.05621337890625,
-0.069091796875,
-0.0238037109375,
0.004795... |
JanosAudran/financial-reports-sec | 2023-01-06T17:44:08.000Z | [
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:masked-language-modeling",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_cat... | JanosAudran | The dataset contains the annual report of US public firms filing with the SEC EDGAR system.
Each annual report (10K filing) is broken into 20 sections. Each section is split into individual sentences.
Sentiment labels are provided on a per filing basis from the market reaction around the filing data.
Additional metadat... | null | 41 | 294 | 2023-01-02T15:21:14 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: US public firm Annual Reports (10-K)
size_categories:
- 10M<n<100M
source_datasets:
- extended|other
tags:
- "'finance"
- financial
- 10... | 25,651 | [
[
-0.0221099853515625,
-0.0293426513671875,
0.012359619140625,
0.03564453125,
-0.016204833984375,
0.00212860107421875,
-0.0099945068359375,
-0.02044677734375,
0.0513916015625,
0.02728271484375,
-0.041900634765625,
-0.06597900390625,
-0.0372314453125,
0.0156402... |
discofuse | 2023-04-05T10:04:50.000Z | [
"task_categories:text2text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"sentence-fusion",
"arxiv:1902.10526",
"region:us"
] | null | DISCOFUSE is a large scale dataset for discourse-based sentence fusion. | @InProceedings{GevaEtAl2019,
title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion},
author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan},
booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Lingu... | 3 | 293 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: DiscoFuse
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: discofuse
tags:
- senten... | 9,269 | [
[
-0.0469970703125,
-0.054595947265625,
0.0032291412353515625,
0.0229644775390625,
-0.007572174072265625,
0.0015926361083984375,
-0.0214996337890625,
-0.0287628173828125,
0.03521728515625,
0.04931640625,
-0.061248779296875,
-0.0582275390625,
-0.03582763671875,
... |
tomasg25/scientific_lay_summarisation | 2022-10-26T11:11:33.000Z | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"abstractive-summarization",
"scientific-papers",
"la... | tomasg25 | This repository contains the PLOS and eLife datasets, introduced in the EMNLP 2022 paper "[Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
](https://arxiv.org/abs/2210.09932)".
Each dataset contains full biomedical research articles paired with expert-written lay summaries (i.e., non-... | @misc{Goldsack_2022,
doi = {10.48550/ARXIV.2210.09932},
url = {https://arxiv.org/abs/2210.09932},
author = {Goldsack, Tomas and Zhang, Zhihao and Lin, Chenghua and Scarton, Carolina},
title = {Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature},
publisher = {arXiv},
year = {20... | 12 | 293 | 2022-10-19T14:46:52 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: ScientificLaySummarisation
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
tags:
- abstractive-summarization
- scientific-papers
- lay-summarization
- PLOS
- eL... | 5,937 | [
[
-0.0261688232421875,
-0.0295867919921875,
0.016387939453125,
0.0199737548828125,
-0.0244598388671875,
-0.018402099609375,
-0.01183319091796875,
-0.0259857177734375,
0.05035400390625,
0.038238525390625,
-0.046875,
-0.0531005859375,
-0.034820556640625,
0.04449... |
qwedsacf/grade-school-math-instructions | 2023-02-11T01:59:26.000Z | [
"region:us"
] | qwedsacf | null | null | 27 | 293 | 2023-02-11T01:32:53 | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
splits:
- name: train
num_bytes: 4804916
num_examples: 8792
download_size: 2554896
dataset_size: 4804916
---
# Dataset Card for grade-school-math-instructions
Op... | 852 | [
[
-0.0062255859375,
-0.04925537109375,
0.0304718017578125,
0.016265869140625,
-0.011444091796875,
-0.0295257568359375,
-0.01800537109375,
0.01287841796875,
0.0063323974609375,
0.014984130859375,
-0.055145263671875,
-0.052886962890625,
-0.0301055908203125,
-0.0... |
orgcatorg/multilingual | 2023-10-18T00:11:33.000Z | [
"region:us"
] | orgcatorg | null | null | 0 | 293 | 2023-09-19T18:55:56 | ---
dataset_info:
- config_name: eng_Latn-lao_Laoo
features:
- name: translation
struct:
- name: eng_Latn
dtype: string
- name: lao_Laoo
dtype: string
splits:
- name: train
num_bytes: 42871606
num_examples: 140265
download_size: 23468883
dataset_size: 42871606
- config_name: ... | 1,753 | [
[
-0.0439453125,
-0.015472412109375,
0.0031948089599609375,
0.031768798828125,
-0.006389617919921875,
0.01151275634765625,
-0.0115509033203125,
-0.028045654296875,
0.064453125,
0.028350830078125,
-0.050750732421875,
-0.0535888671875,
-0.048309326171875,
-0.006... |
glaiveai/glaive-code-assistant | 2023-09-27T22:51:02.000Z | [
"size_categories:100K<n<1M",
"license:apache-2.0",
"region:us"
] | glaiveai | null | null | 35 | 293 | 2023-09-21T18:56:47 | ---
license: apache-2.0
size_categories:
- 100K<n<1M
---
# Glaive-code-assistant
Glaive-code-assistant is a dataset of ~140k code problems and solutions generated using Glaive’s synthetic data generation platform.
The data is intended to be used to make models act as code assistants, and so the data is structured in... | 566 | [
[
-0.00949859619140625,
-0.05682373046875,
0.025146484375,
0.0179901123046875,
-0.001979827880859375,
0.0102996826171875,
0.034423828125,
-0.0263214111328125,
0.0252685546875,
0.042694091796875,
-0.052581787109375,
-0.044891357421875,
-0.01424407958984375,
-0.... |
Exr0n/wiki-entity-similarity | 2022-08-19T18:51:04.000Z | [
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:mit",
"named entities",
"similarity",
"paraphrasing",
"synonyms",
"wikipedia",
"arxiv:2004.04906",
"arxiv:2202.13581",
... | Exr0n | null | null | 6 | 292 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: 'Wiki Entity Similarity
'
size_categories:
- 10M<n<100M
source_datasets:
- original
tags:
- named entities
- similarity
- paraphrasing
- synonyms
- wikipedia
task_categories: []
task... | 2,677 | [
[
-0.051422119140625,
-0.034393310546875,
0.0149688720703125,
-0.0163116455078125,
-0.022552490234375,
-0.0191192626953125,
-0.0157012939453125,
-0.031524658203125,
0.0372314453125,
0.0184173583984375,
-0.031494140625,
-0.0555419921875,
-0.0379638671875,
0.041... |
diwank/hinglish-dump | 2022-03-05T14:28:55.000Z | [
"license:mit",
"region:us"
] | diwank | Raw merged dump of Hinglish (hi-EN) datasets. | null | 1 | 292 | 2022-03-02T23:29:22 | ---
license: mit
---
# Hinglish Dump
Raw merged dump of Hinglish (hi-EN) datasets.
## Subsets and features
Subsets:
- crowd_transliteration
- hindi_romanized_dump
- hindi_xlit
- hinge
- hinglish_norm
- news2018
```
_FEATURE_NAMES = [
"target_hinglish",
"source_hindi",
"parall... | 399 | [
[
-0.035369873046875,
-0.0301513671875,
-0.016845703125,
0.03851318359375,
-0.01445770263671875,
0.01018524169921875,
-0.029144287109375,
-0.0036983489990234375,
0.045562744140625,
0.061614990234375,
-0.033447265625,
-0.0274200439453125,
-0.043792724609375,
0.... |
laion/laion2B-en | 2023-08-13T10:21:14.000Z | [
"license:cc-by-4.0",
"region:us"
] | laion | null | null | 143 | 292 | 2022-03-08T22:49:04 | ---
license: cc-by-4.0
---
HEIGHT and WIDTH are swapped | 56 | [
[
-0.0240325927734375,
-0.013336181640625,
0.059173583984375,
0.0019016265869140625,
-0.032745361328125,
-0.0147552490234375,
0.017486572265625,
-0.0570068359375,
0.069580078125,
0.045684814453125,
-0.0264892578125,
0.019012451171875,
-0.06341552734375,
-0.019... |
Jackmin108/c4-en-validation | 2023-08-18T22:00:10.000Z | [
"region:us"
] | Jackmin108 | null | null | 0 | 292 | 2023-08-18T21:59:09 | 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... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.