id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
maritaca-ai/boolq_pt | 2023-02-09T00:38:29.000Z | [
"region:us"
] | maritaca-ai | BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair... | @inproceedings{clark2019boolq,
title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle = {NAACL},
year = {2019},
} | 1 | 230 | 2023-01-29T21:30:08 | 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... |
jonathanli/hyperpartisan-longformer-split | 2022-12-31T16:08:16.000Z | [
"arxiv:2004.05150",
"region:us"
] | jonathanli | null | null | 0 | 229 | 2022-12-31T15:56:50 | # Hyperpartisan news detection
This dataset has the hyperpartisan new dataset, processed and split exactly as it was for [longformer](https://arxiv.org/abs/2004.05150) experiments.
Code for processing was found at [here](https://github.com/allenai/longformer/blob/master/scripts/hp_preprocess.py).
| 299 | [
[
-0.03277587890625,
-0.060394287109375,
0.04669189453125,
0.0179290771484375,
-0.02069091796875,
0.004177093505859375,
-0.0183563232421875,
-0.01242828369140625,
0.04437255859375,
0.06549072265625,
-0.04193115234375,
-0.0284423828125,
-0.035980224609375,
0.00... |
GATE-engine/cubirds200_bbcrop | 2023-06-04T23:55:39.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 228 | 2023-06-04T23:54:48 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 669852245.5
num_examples: 8204
- name: validation
num_bytes: 141492702.625
num_examples: 1771
- name: test
num_bytes: 146984302.75
num_examples: 1770
downloa... | 543 | [
[
-0.047027587890625,
-0.007450103759765625,
0.00010132789611816406,
0.034515380859375,
-0.0252685546875,
0.0197296142578125,
0.023773193359375,
-0.003448486328125,
0.041748046875,
0.032318115234375,
-0.0517578125,
-0.052581787109375,
-0.042816162109375,
-0.01... |
togethercomputer/llama-instruct | 2023-08-18T05:04:06.000Z | [
"language:en",
"license:llama2",
"arxiv:2304.12244",
"region:us"
] | togethercomputer | null | null | 20 | 228 | 2023-08-03T04:52:19 | ---
license: llama2
language:
- en
---
# llama-instruct
This dataset was used to finetune [Llama-2-7B-32K-Instruct](https://huggingface.co/togethercomputer/Llama-2-7B-32K-Instruct).
We follow the distillation paradigm that is used by [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [Vicuna](https://lmsys.o... | 1,853 | [
[
-0.0260467529296875,
-0.07342529296875,
0.03857421875,
0.03851318359375,
-0.0252227783203125,
0.012298583984375,
-0.015838623046875,
-0.0322265625,
0.0142669677734375,
0.0352783203125,
-0.049713134765625,
-0.0478515625,
-0.043426513671875,
0.0046920776367187... |
greek_legal_code | 2023-06-12T14:25:00.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:el",
"license:cc-by-4.0",
"ar... | null | Greek_Legal_Code contains 47k classified legal resources from Greek Legislation. Its origin is “Permanent Greek Legislation Code - Raptarchis”,
a collection of Greek legislative documents classified into multi-level (from broader to more specialized) categories. | @inproceedings{papaloukas-etal-2021-glc,
title = "Multi-granular Legal Topic Classification on Greek Legislation",
author = "Papaloukas, Christos and Chalkidis, Ilias and Athinaios, Konstantinos and Pantazi, Despina-Athanasia and Koubarakis, Manolis",
booktitle = "Proceedings of the 3rd Natural Legal Langua... | 8 | 227 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- el
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
pretty_name: Greek Legal Code
dat... | 135,347 | [
[
-0.046875,
-0.032501220703125,
0.027069091796875,
0.0027751922607421875,
-0.050323486328125,
-0.0139007568359375,
-0.0071868896484375,
-0.010894775390625,
0.044097900390625,
0.036773681640625,
-0.035919189453125,
-0.065185546875,
-0.04803466796875,
0.0101776... |
so_stacksample | 2022-11-03T16:30:57.000Z | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-... | null | Dataset with the text of 10% of questions and answers from the Stack Overflow programming Q&A website.
This is organized as three tables:
Questions contains the title, body, creation date, closed date (if applicable), score, and owner ID for all non-deleted Stack Overflow questions whose Id is a multiple of 10.
Answe... | null | 3 | 227 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
- open-domain-abstractive-qa
paperswithcode_id: nul... | 8,236 | [
[
-0.05267333984375,
-0.0625,
0.0211029052734375,
0.005733489990234375,
-0.00482940673828125,
0.007335662841796875,
-0.004329681396484375,
-0.022247314453125,
0.042236328125,
0.0430908203125,
-0.06304931640625,
-0.059417724609375,
-0.0196380615234375,
0.005683... |
thaisum | 2022-11-18T21:51:46.000Z | [
"task_categories:summarization",
"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_d... | null | ThaiSum is a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath,
ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs
written by journalists. | @mastersthesis{chumpolsathien_2020,
title={Using Knowledge Distillation from Keyword Extraction to Improve the Informativeness of Neural Cross-lingual Summarization},
author={Chumpolsathien, Nakhun},
year={2020},
school={Beijing Institute of Technology} | 7 | 227 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- th
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcod... | 10,852 | [
[
-0.0264434814453125,
-0.04315185546875,
0.01375579833984375,
0.036407470703125,
-0.05224609375,
-0.0047454833984375,
-0.0232086181640625,
-0.020660400390625,
0.053802490234375,
0.0223236083984375,
-0.006092071533203125,
-0.049041748046875,
-0.050323486328125,
... |
OamPatel/iti_trivia_qa_val | 2023-06-14T18:48:29.000Z | [
"region:us"
] | OamPatel | null | null | 1 | 227 | 2023-06-14T18:48:15 | 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... |
chr_en | 2023-06-01T14:59:50.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_categories:translation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"annotations_creators:found",
"annotations_creators:no-annotation",
"language_creators:found",
... | null | ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English.
ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation.
ChrEn also contains 5k Cherokee monolingual data to enabl... | @inproceedings{zhang2020chren,
title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization},
author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit},
booktitle={EMNLP2020},
year={2020}
} | 3 | 226 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- found
- no-annotation
language_creators:
- found
language:
- chr
- en
license:
- other
multilinguality:
- monolingual
- multilingual
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- fill-mask
- text-generatio... | 6,749 | [
[
-0.0190277099609375,
-0.057891845703125,
0.010528564453125,
0.0013170242309570312,
-0.0308380126953125,
0.0178680419921875,
-0.043731689453125,
-0.030792236328125,
0.030029296875,
0.0445556640625,
-0.03594970703125,
-0.06005859375,
-0.05133056640625,
0.02767... |
HuggingFaceH4/cherry_picked_prompts | 2023-03-08T21:24:46.000Z | [
"license:apache-2.0",
"region:us"
] | HuggingFaceH4 | null | null | 1 | 226 | 2023-03-08T12:49:42 | ---
license: apache-2.0
---
# Dataset Card for Cherry Picked Prompts 🍒
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** Lewis Tunstall
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using ... | 1,585 | [
[
-0.044891357421875,
-0.038177490234375,
0.01445770263671875,
0.019805908203125,
-0.02557373046875,
0.01493072509765625,
-0.01332855224609375,
0.0004520416259765625,
0.03924560546875,
0.051422119140625,
-0.06951904296875,
-0.07421875,
-0.041015625,
0.00319671... |
gutenberg_time | 2022-11-03T16:32:34.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2011.04124",
"region:us"
... | null | A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg. | @misc{kim2020time,
title={What time is it? Temporal Analysis of Novels},
author={Allen Kim and Charuta Pethe and Steven Skiena},
year={2020},
eprint={2011.04124},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 3 | 225 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: gutenberg-time-dataset
pretty_nam... | 5,878 | [
[
0.004497528076171875,
-0.02716064453125,
0.0290985107421875,
0.02001953125,
-0.01666259765625,
-0.0010023117065429688,
0.00946044921875,
-0.0390625,
0.017425537109375,
0.0213165283203125,
-0.06463623046875,
-0.0433349609375,
-0.023773193359375,
0.02185058593... |
kinnews_kirnews | 2023-06-01T14:59:50.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"l... | null | Kinyarwanda and Kirundi news classification datasets | @article{niyongabo2020kinnews,
title={KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi},
author={Niyongabo, Rubungo Andre and Qu, Hong and Kreutzer, Julia and Huang, Li},
journal={arXiv preprint arXiv:2010.12174},
year={2020}
} | 1 | 225 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- rn
- rw
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
paperswithco... | 7,944 | [
[
-0.030487060546875,
-0.0300140380859375,
-0.00203704833984375,
0.0262908935546875,
-0.01849365234375,
0.00534820556640625,
-0.0251617431640625,
-0.036865234375,
0.0400390625,
0.0191650390625,
-0.03466796875,
-0.06378173828125,
-0.056427001953125,
0.015701293... |
qanta | 2023-04-05T13:37:09.000Z | [
"task_categories:question-answering",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"quizbowl",
"arxiv:1904.04792",
"region:us"
] | null | The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. | @article{Rodriguez2019QuizbowlTC,
title={Quizbowl: The Case for Incremental Question Answering},
author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber},
journal={ArXiv},
year={2019},
volume={abs/1904.04792}
} | 3 | 225 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: Quizbowl
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: quizbowl
tags:
- quizbowl
dataset... | 8,802 | [
[
-0.04742431640625,
-0.047698974609375,
0.015380859375,
0.0136566162109375,
-0.00753021240234375,
0.007740020751953125,
-0.0073699951171875,
-0.0239410400390625,
0.0411376953125,
0.0309600830078125,
-0.06573486328125,
-0.06280517578125,
-0.03216552734375,
0.0... |
SophieTr/reddit_clean | 2022-08-13T20:26:31.000Z | [
"region:us"
] | SophieTr | null | null | 3 | 225 | 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... |
conv_ai_3 | 2022-11-03T16:30:50.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"eva... | null | The Conv AI 3 challenge is organized as part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In Information Retrieval (IR) settings such... | @misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
... | 13 | 224 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conversational
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: More... | 7,405 | [
[
-0.0423583984375,
-0.080322265625,
0.0174102783203125,
0.01195526123046875,
-0.0011310577392578125,
0.0064544677734375,
-0.0214691162109375,
-0.0008797645568847656,
0.028350830078125,
0.03875732421875,
-0.0665283203125,
-0.057586669921875,
-0.031402587890625,
... |
thaiqa_squad | 2022-11-03T16:15:52.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-thaiqa",
"language:th",
"license:cc-by-nc-sa-3.0... | null | `thaiqa_squad` is an open-domain, extractive question answering dataset (4,000 questions in `train` and 74 questions in `dev`) in
[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, originally created by [NECTEC](https://www.nectec.or.th/en/) from
Wikipedia articles and adapted to [SQuAD](https://rajpurkar.git... | No clear citation guidelines from source:
https://aiforthai.in.th/corpus.php
SQuAD version:
https://github.com/PyThaiNLP/thaiqa_squad | 5 | 224 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-thaiqa
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null
p... | 8,324 | [
[
-0.044677734375,
-0.050872802734375,
0.0017843246459960938,
0.035003662109375,
-0.02349853515625,
0.00997161865234375,
-0.01100921630859375,
-0.0264434814453125,
0.046051025390625,
0.0102081298828125,
-0.0633544921875,
-0.040557861328125,
-0.0231170654296875,
... |
IIC/qges | 2022-06-16T12:11:00.000Z | [
"region:us"
] | IIC | null | null | 0 | 224 | 2022-06-16T12:10:39 | 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... |
clane9/NSD-Flat | 2023-09-28T01:26:21.000Z | [
"task_categories:image-to-image",
"task_categories:object-detection",
"size_categories:100K<n<1M",
"license:other",
"biology",
"neuroscience",
"fmri",
"region:us"
] | clane9 | null | null | 3 | 224 | 2023-07-20T21:40:43 | ---
license: other
dataset_info:
features:
- name: subject_id
dtype: int64
- name: trial_id
dtype: int64
- name: session_id
dtype: int64
- name: nsd_id
dtype: int64
- name: image
dtype: image
- name: activity
dtype: image
- name: subject
dtype: string
- name: flagged
dt... | 4,727 | [
[
-0.051513671875,
-0.060638427734375,
0.0218963623046875,
0.044586181640625,
-0.012908935546875,
-0.00800323486328125,
-0.01995849609375,
-0.034149169921875,
0.054901123046875,
0.03863525390625,
-0.0660400390625,
-0.05950927734375,
-0.0294647216796875,
0.0020... |
ajgt_twitter_ar | 2023-01-25T14:26:05.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
] | null | Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. | @inproceedings{alomari2017arabic,
title={Arabic tweets sentimental analysis using machine learning},
author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},
booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
pages={602--610... | 2 | 223 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Arabic Jordanian General Tweets
dataset_info:
f... | 4,656 | [
[
-0.034759521484375,
-0.03570556640625,
0.0027103424072265625,
0.03814697265625,
-0.044281005859375,
0.007598876953125,
-0.026947021484375,
-0.0215301513671875,
0.0241241455078125,
0.019622802734375,
-0.055908203125,
-0.090087890625,
-0.06292724609375,
-0.003... |
gsarti/itacola | 2022-07-01T15:38:55.000Z | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:it",
"license:unknown",
"arxiv:2109.12053",... | gsarti | The Italian Corpus of Linguistic Acceptability includes almost 10k sentences taken from
linguistic literature with a binary annotation made by the original authors themselves.
The work is inspired by the English Corpus of Linguistic Acceptability (CoLA) by Warstadt et al.
Part of the dataset has been manually annotat... | @inproceedings{trotta-etal-2021-monolingual,
author = {Trotta, Daniela and Guarasci, Raffaele and Leonardelli, Elisa and Tonelli, Sara},
title = {Monolingual and Cross-Lingual Acceptability Judgments with the Italian {CoLA} corpus},
booktitle = "Findings of the Association for Computational Linguistics: EMN... | 0 | 223 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- it
license:
- unknown
multilinguality:
- monolingual
pretty_name: itacola
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
---
# Dataset C... | 6,046 | [
[
-0.03668212890625,
-0.040557861328125,
0.0226593017578125,
0.03173828125,
-0.00711822509765625,
-0.00879669189453125,
-0.042205810546875,
-0.048065185546875,
0.04412841796875,
0.00969696044921875,
-0.0243988037109375,
-0.0635986328125,
-0.0501708984375,
0.02... |
NeelNanda/counterfact-tracing | 2022-11-05T15:19:43.000Z | [
"arxiv:2211.00593",
"region:us"
] | NeelNanda | null | null | 5 | 223 | 2022-11-05T15:09:51 | ---
dataset_info:
features:
- name: relation
dtype: string
- name: relation_prefix
dtype: string
- name: relation_suffix
dtype: string
- name: prompt
dtype: string
- name: relation_id
dtype: string
- name: target_false_id
dtype: string
- name: target_true_id
dtype: string
-... | 1,934 | [
[
-0.0238189697265625,
-0.0714111328125,
0.054901123046875,
0.0005273818969726562,
-0.0251922607421875,
-0.033477783203125,
0.01026153564453125,
-0.01873779296875,
0.0178985595703125,
0.0301361083984375,
-0.061309814453125,
-0.0238037109375,
-0.017303466796875,
... |
alzoubi36/policy_ie_b | 2023-06-25T07:13:15.000Z | [
"region:us"
] | alzoubi36 | null | null | 0 | 223 | 2023-06-25T07:10:04 | ---
dataset_info:
features:
- name: type-I
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
- name: type-II
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequ... | 692 | [
[
-0.0075836181640625,
-0.02734375,
0.006755828857421875,
0.0142822265625,
0.037689208984375,
0.01265716552734375,
0.0126190185546875,
0.0008559226989746094,
0.0308074951171875,
0.046417236328125,
-0.07354736328125,
-0.05810546875,
-0.021453857421875,
-0.04067... |
pbaoo2705/processed_dataset_v2 | 2023-09-06T05:27:28.000Z | [
"region:us"
] | pbaoo2705 | null | null | 0 | 223 | 2023-09-06T05:27:24 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: pubid
dtype: int32
- name: question
dtype: string
- name: context
dtype: string
- name: long_answer
dtype: string
- name: final_deci... | 737 | [
[
-0.02252197265625,
-0.0250396728515625,
0.0216064453125,
0.0167999267578125,
-0.018402099609375,
-0.00792694091796875,
0.027069091796875,
-0.025604248046875,
0.055419921875,
0.0521240234375,
-0.06494140625,
-0.039764404296875,
-0.048126220703125,
-0.02212524... |
jeopardy | 2023-04-05T10:07:53.000Z | [
"language:en",
"region:us"
] | null | Dataset containing 216,930 Jeopardy questions, answers and other data.
The json file is an unordered list of questions where each question has
'category' : the question category, e.g. "HISTORY"
'value' : integer $ value of the question as string, e.g. "200"
Note: This is "None" for Final Jeopardy! and Tiebreaker quest... | 4 | 222 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: null
pretty_name: jeopardy
dataset_info:
features:
- name: category
dtype: string
- name: air_date
dtype: string
- name: question
dtype: string
- name: value
dtype: int32
- name: answer
dtype: string
- name: round
dtype: string
- name: show_n... | 6,620 | [
[
-0.039306640625,
-0.042388916015625,
0.0096282958984375,
0.0008549690246582031,
-0.0213165283203125,
0.0017805099487304688,
-0.0220947265625,
-0.0165252685546875,
0.05908203125,
0.0428466796875,
-0.0599365234375,
-0.053497314453125,
-0.0452880859375,
0.00584... | |
simple_questions_v2 | 2022-11-18T21:46:14.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] | null | SimpleQuestions is a dataset for simple QA, which consists
of a total of 108,442 questions written in natural language by human
English-speaking annotators each paired with a corresponding fact,
formatted as (subject, relationship, object), that provides the answer
but also a complete explanation. Fast have been extra... | @misc{bordes2015largescale,
title={Large-scale Simple Question Answering with Memory Networks},
author={Antoine Bordes and Nicolas Usunier and Sumit Chopra and Jason Weston},
year={2015},
eprint={1506.02075},
archivePrefix={arXiv},
primaryClass={cs.LG}
} | 1 | 222 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: simplequestions
pretty_name: SimpleQues... | 4,818 | [
[
-0.0408935546875,
-0.060699462890625,
0.01800537109375,
0.003200531005859375,
-0.002651214599609375,
-0.005706787109375,
-0.01654052734375,
-0.0137939453125,
0.041595458984375,
0.04522705078125,
-0.06622314453125,
-0.0604248046875,
-0.0413818359375,
0.016113... |
ChristophSchuhmann/improved_aesthetics_6.5plus | 2022-08-10T11:34:17.000Z | [
"region:us"
] | ChristophSchuhmann | null | null | 37 | 222 | 2022-08-10T11:34:12 | 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... |
bigbio/ebm_pico | 2022-12-22T15:44:33.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | This corpus release contains 4,993 abstracts annotated with (P)articipants,
(I)nterventions, and (O)utcomes. Training labels are sourced from AMT workers and
aggregated to reduce noise. Test labels are collected from medical professionals. | @inproceedings{nye-etal-2018-corpus,
title = "A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature",
author = "Nye, Benjamin and
Li, Junyi Jessy and
Patel, Roma and
Yang, Yinfei and
Marshall, Iain and
... | 0 | 222 | 2022-11-13T22:08:15 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: EBM NLP
homepage: https://github.com/bepnye/EBM-NLP
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for EBM NLP
## Dataset... | 1,455 | [
[
-0.023345947265625,
-0.03863525390625,
0.0111541748046875,
0.0122528076171875,
-0.0214996337890625,
-0.005222320556640625,
-0.017364501953125,
-0.03704833984375,
0.049072265625,
0.016357421875,
-0.03338623046875,
-0.0682373046875,
-0.0574951171875,
0.0377502... |
jondurbin/airoboros-3.0 | 2023-10-12T14:53:54.000Z | [
"license:apache-2.0",
"region:us"
] | jondurbin | null | null | 9 | 222 | 2023-09-29T20:56:48 | ---
license: apache-2.0
---
## Overview
This dataset is a continuation of the airoboros datasets, with two main new contributions:
* MathJSON - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tags, which can be ... | 2,165 | [
[
-0.03619384765625,
-0.05108642578125,
0.029876708984375,
0.002201080322265625,
-0.00795745849609375,
-0.00809478759765625,
-0.021697998046875,
-0.01340484619140625,
0.042022705078125,
0.046722412109375,
-0.062286376953125,
-0.0273590087890625,
-0.033203125,
... |
ubuntu_dialogs_corpus | 2023-04-05T13:42:49.000Z | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1506.08909",
"region:us"
] | null | Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The data... | @article{DBLP:journals/corr/LowePSP15,
author = {Ryan Lowe and
Nissan Pow and
Iulian Serban and
Joelle Pineau},
title = {The Ubuntu Dialogue Corpus: {A} Large Dataset for Research in Unstructured
Multi-Turn Dialogue Systems},
journal = {CoRR},
... | 13 | 221 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: UDC (Ubuntu Dialogue Corpus)
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
paperswithcode_id: ubuntu-dial... | 7,465 | [
[
-0.043060302734375,
-0.04656982421875,
0.0169525146484375,
0.00550079345703125,
-0.014495849609375,
0.00601959228515625,
-0.033599853515625,
-0.021148681640625,
0.03839111328125,
0.04669189453125,
-0.05267333984375,
-0.058013916015625,
-0.0261993408203125,
0... |
ScandEval/suc3-mini | 2023-07-05T09:42:05.000Z | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:sv",
"license:cc-by-4.0",
"region:us"
] | ScandEval | null | null | 0 | 221 | 2022-06-14T18:21:45 | ---
dataset_info:
features:
- name: text
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 344855
num_examples: 1024
- name: test
num_bytes: 681936
num_examples: 2048
- name: val
num_bytes: 81547
num_example... | 644 | [
[
-0.04290771484375,
-0.01253509521484375,
0.025970458984375,
-0.002071380615234375,
-0.0155792236328125,
-0.005245208740234375,
0.02850341796875,
0.0023097991943359375,
0.05816650390625,
0.021697998046875,
-0.07037353515625,
-0.0404052734375,
-0.02532958984375,
... |
tasksource/folio | 2023-05-31T13:40:30.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"language:en",
"license:cc",
"arxiv:2209.00840",
"region:us"
] | tasksource | null | null | 5 | 221 | 2023-02-21T08:15:17 | ---
license: cc
task_categories:
- text-classification
language:
- en
task_ids:
- natural-language-inference
- multi-input-text-classification
---
https://github.com/Yale-LILY/FOLIO
```
@article{han2022folio,
title={FOLIO: Natural Language Reasoning with First-Order Logic},
author = {Han, Simeng and Schoelkopf,... | 869 | [
[
-0.007740020751953125,
-0.038330078125,
0.037322998046875,
0.0164794921875,
-0.0106964111328125,
-0.006374359130859375,
-0.002620697021484375,
-0.038238525390625,
0.01532745361328125,
0.032745361328125,
-0.0382080078125,
-0.049957275390625,
-0.03570556640625,
... |
medalpaca/medical_meadow_wikidoc_patient_information | 2023-04-06T17:08:53.000Z | [
"task_categories:question-answering",
"language:en",
"license:cc",
"region:us"
] | medalpaca | null | null | 6 | 221 | 2023-04-06T17:05:50 | ---
license: cc
task_categories:
- question-answering
language:
- en
---
# Dataset Card for WikiDoc
For the dataset containing rephrased content from the living textbook refer to [this dataset](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc)
## Dataset Description
- **Source:** https://www.wikidoc.o... | 1,396 | [
[
-0.020660400390625,
-0.05218505859375,
0.031402587890625,
-0.004444122314453125,
-0.031280517578125,
-0.00992584228515625,
-0.00005322694778442383,
-0.0196075439453125,
0.0372314453125,
0.0426025390625,
-0.054412841796875,
-0.0499267578125,
-0.0276641845703125,
... |
CollectiveCognition/chats-data-2023-09-27 | 2023-09-28T00:40:51.000Z | [
"license:mit",
"region:us"
] | CollectiveCognition | null | null | 15 | 221 | 2023-09-28T00:39:17 | ---
license: mit
---
# Dataset Card for "Collective Cognition ChatGPT Conversations"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#da... | 6,119 | [
[
-0.027740478515625,
-0.07232666015625,
0.01480865478515625,
0.0293731689453125,
-0.0026035308837890625,
0.009552001953125,
-0.016021728515625,
-0.019561767578125,
0.021453857421875,
0.037139892578125,
-0.051605224609375,
-0.05224609375,
-0.053863525390625,
-... |
hmao/reformatted_multiapi | 2023-10-23T21:44:44.000Z | [
"region:us"
] | hmao | null | null | 0 | 221 | 2023-10-23T21:44:43 | ---
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: 27030
num_examples: 46
download_size... | 528 | [
[
-0.0367431640625,
-0.00742340087890625,
0.010711669921875,
0.0223541259765625,
-0.0099639892578125,
0.0034770965576171875,
0.00974273681640625,
-0.00998687744140625,
0.0704345703125,
0.024322509765625,
-0.07122802734375,
-0.04034423828125,
-0.03326416015625,
... |
ghadeermobasher/BC5CDR-Chemical-Disease | 2022-01-25T10:31:51.000Z | [
"region:us"
] | ghadeermobasher | \ | @article{krallinger2015chemdner,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez, Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan and Ji, Donghong and Lowe, Daniel M and others... | 4 | 220 | 2022-03-02T23:29:22 | annotations_creators:
- expert-generated
language_creators:
- expert-generated
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
paperswithcode_id: bc4chemd
pretty_name: BC4CHEMD
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- structure-prediction
task_ids:
- named-entity-rec... | 4,939 | [
[
-0.0186920166015625,
-0.035064697265625,
0.043609619140625,
0.002197265625,
-0.003032684326171875,
0.007781982421875,
-0.0300750732421875,
-0.039398193359375,
0.0321044921875,
0.037933349609375,
-0.0290679931640625,
-0.08154296875,
-0.04425048828125,
0.04507... |
ChristophSchuhmann/improved_aesthetics_6plus | 2022-08-10T11:30:40.000Z | [
"region:us"
] | ChristophSchuhmann | null | null | 23 | 220 | 2022-08-10T11:29:49 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01494598388671875,
0.057159423828125,
0.02880859375,
-0.0350341796875,
0.046478271484375,
0.052520751953125,
0.005077362060546875,
0.051361083984375,
0.0170135498046875,
-0.05206298828125,
-0.01494598388671875,
-0.06036376953125,
0.03... |
nasa-cisto-data-science-group/modis-lake-powell-toy-dataset | 2023-05-04T01:39:33.000Z | [
"size_categories:n<1K",
"license:apache-2.0",
"region:us"
] | nasa-cisto-data-science-group | null | null | 0 | 220 | 2023-03-09T14:45:40 | ---
license: apache-2.0
size_categories:
- n<1K
---
# MODIS Water Lake Powell Toy Dataset
### Dataset Summary
Tabular dataset comprised of MODIS surface reflectance bands along with calculated indices and a label (water/not-water)
## Dataset Structure
### Data Fields
- `water`: Label, water or not-water (binary)
... | 1,429 | [
[
-0.05364990234375,
-0.0301971435546875,
0.0298309326171875,
0.01788330078125,
-0.038116455078125,
-0.0120849609375,
0.0258026123046875,
-0.01131439208984375,
0.00969696044921875,
0.0298919677734375,
-0.0606689453125,
-0.04949951171875,
-0.024505615234375,
-0... |
FreedomIntelligence/alpaca-gpt4-korean | 2023-08-06T08:10:43.000Z | [
"region:us"
] | FreedomIntelligence | null | null | 1 | 220 | 2023-06-26T08:18:44 | The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT). | 124 | [
[
-0.02838134765625,
-0.02142333984375,
-0.0002911090850830078,
0.01971435546875,
-0.004505157470703125,
0.004131317138671875,
-0.0193939208984375,
-0.030364990234375,
0.0289154052734375,
0.033966064453125,
-0.06427001953125,
-0.032958984375,
-0.01299285888671875,... |
charlie8522/Totto_testing | 2023-09-24T14:42:33.000Z | [
"region:us"
] | charlie8522 | null | null | 0 | 220 | 2023-09-22T07:11:31 | 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... |
nell | 2023-06-01T14:59:50.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"size_categories:10M<n<100M",
"size_categories:1M<n<10M",... | null | This dataset provides version 1115 of the belief
extracted by CMU's Never Ending Language Learner (NELL) and version
1110 of the candidate belief extracted by NELL. See
http://rtw.ml.cmu.edu/rtw/overview. NELL is an open information
extraction system that attempts to read the Clueweb09 of 500 million
web pages (http:/... | @inproceedings{mitchell2015,
added-at = {2015-01-27T15:35:24.000+0100},
author = {Mitchell, T. and Cohen, W. and Hruscha, E. and Talukdar, P. and Betteridge, J. and Carlson, A. and Dalvi, B. and Gardner, M. and Kisiel, B. and Krishnamurthy, J. and Lao, N. and Mazaitis, K. and Mohammad, T. and Nakashole, N. and Plat... | 3 | 219 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
- 10M<n<100M
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
- fact-checking-retriev... | 16,347 | [
[
-0.00830841064453125,
-0.0533447265625,
0.0313720703125,
0.0013036727905273438,
-0.0006089210510253906,
-0.0036945343017578125,
-0.0029277801513671875,
-0.01386260986328125,
0.025054931640625,
0.014495849609375,
-0.039703369140625,
-0.07818603515625,
-0.03817749... |
scikit-learn/iris | 2022-06-20T14:17:01.000Z | [
"license:cc0-1.0",
"region:us"
] | scikit-learn | null | null | 0 | 219 | 2022-06-20T14:10:10 | ---
license: cc0-1.0
---
## Iris Species Dataset
The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.
It includes three iris species with 50 samples each as well as some properties about eac... | 1,659 | [
[
-0.032623291015625,
-0.00441741943359375,
0.0038909912109375,
0.03662109375,
0.003753662109375,
-0.008514404296875,
0.005191802978515625,
-0.05865478515625,
0.035430908203125,
0.026031494140625,
-0.050506591796875,
-0.027557373046875,
-0.022491455078125,
0.0... |
asapp/slue-phase-2 | 2023-08-01T16:05:43.000Z | [
"arxiv:2212.10525",
"region:us"
] | asapp | Spoken Language Understanding Evaluation (SLUE) benchmark Phase 2. | @inproceedings{shon2023slue_phase2,
title={SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks},
author={Shon, Suwon and Arora, Siddhant and Lin, Chyi-Jiunn and Pasad, Ankita and Wu, Felix and Sharma, Roshan and Wu, Wei-Lun and Lee, Hung-Yi and Livescu, Karen and Watanabe, Shinji},
book... | 4 | 219 | 2023-05-31T04:10:08 |
### Dataset description
**(Jul. 11 2023) Detail information will released soon.**
- **Toolkit Repository:** [https://github.com/asappresearch/slue-toolkit/](https://github.com/asappresearch/slue-toolkit/)
- **Paper:** [https://arxiv.org/abs/2212.10525](https://arxiv.org/abs/2212.10525)
### Licensing Information
... | 3,975 | [
[
-0.0430908203125,
-0.05291748046875,
0.0227203369140625,
0.006465911865234375,
-0.01007843017578125,
0.018157958984375,
-0.016082763671875,
-0.04803466796875,
0.033233642578125,
0.02978515625,
-0.061981201171875,
-0.049346923828125,
-0.0179443359375,
-0.0008... |
C-MTEB/CLSClusteringS2S | 2023-07-27T17:29:54.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 219 | 2023-07-27T17:29:48 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 6895612
num_examples: 10
download_size: 4483035
dataset_size: 6895612
---
# Dat... | 485 | [
[
-0.0275421142578125,
0.001995086669921875,
0.01360321044921875,
0.026702880859375,
-0.03173828125,
-0.0015125274658203125,
0.01490020751953125,
-0.0122528076171875,
0.052520751953125,
0.040802001953125,
-0.05413818359375,
-0.040496826171875,
-0.05126953125,
... |
C-MTEB/LCQMC | 2023-07-28T13:51:45.000Z | [
"region:us"
] | C-MTEB | null | null | 2 | 219 | 2023-07-28T13:51:20 | ---
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... | 729 | [
[
-0.041534423828125,
-0.00421905517578125,
0.0283355712890625,
0.0013523101806640625,
-0.017181396484375,
0.0210723876953125,
0.032073974609375,
0.002262115478515625,
0.041534423828125,
0.04925537109375,
-0.06768798828125,
-0.057952880859375,
-0.0246429443359375,... |
wi_locness | 2023-06-01T14:59:47.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"multilinguality:other-language-learner",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"grammatical-error-cor... | null | Write & Improve (Yannakoudakis et al., 2018) is an online web platform that assists non-native
English students with their writing. Specifically, students from around the world submit letters,
stories, articles and essays in response to various prompts, and the W&I system provides instant
feedback. Since W&I went live ... | @inproceedings{bryant-etal-2019-bea,
title = "The {BEA}-2019 Shared Task on Grammatical Error Correction",
author = "Bryant, Christopher and
Felice, Mariano and
Andersen, {\\O}istein E. and
Briscoe, Ted",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP... | 7 | 218 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
- other-language-learner
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: locness-corpus
pretty_nam... | 14,995 | [
[
-0.028656005859375,
-0.059661865234375,
0.02166748046875,
0.003681182861328125,
-0.004901885986328125,
-0.01348114013671875,
-0.00064849853515625,
-0.0526123046875,
0.0489501953125,
0.04193115234375,
-0.029998779296875,
-0.061614990234375,
-0.03875732421875,
... |
flax-sentence-embeddings/stackexchange_math_jsonl | 2022-07-11T13:12:59.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | flax-sentence-embeddings | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | 4 | 218 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
... | 5,614 | [
[
-0.042083740234375,
-0.06878662109375,
0.0386962890625,
0.017059326171875,
-0.01238250732421875,
0.0025577545166015625,
-0.0140228271484375,
-0.011322021484375,
0.041229248046875,
0.038848876953125,
-0.035888671875,
-0.0772705078125,
-0.0421142578125,
0.0145... |
olm/wikipedia | 2022-11-15T18:39:59.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categ... | olm | Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.). | @ONLINE {wikidump,
author = {Wikimedia Foundation},
title = {Wikimedia Downloads},
url = {https://dumps.wikimedia.org}
} | 24 | 218 | 2022-10-04T18:07:56 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
pretty_name: Wikipedia
paperswithcode_id: null
license:
- cc-by-sa-3.0
- gfdl
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
source_datasets:
- original
multilinguality:
- multilingual
si... | 12,808 | [
[
-0.06231689453125,
-0.04852294921875,
0.010772705078125,
0.0105743408203125,
-0.0197296142578125,
-0.0204315185546875,
-0.0304107666015625,
-0.03570556640625,
0.04229736328125,
0.0250396728515625,
-0.0555419921875,
-0.058074951171875,
-0.0350341796875,
0.017... |
C-MTEB/ThuNewsClusteringS2S | 2023-07-27T17:28:46.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 218 | 2023-07-27T17:28:35 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 6649209
num_examples: 10
download_size: 5008942
dataset_size: 6649209
---
# Dat... | 489 | [
[
-0.0194854736328125,
0.0027923583984375,
0.017425537109375,
0.0264129638671875,
-0.0180206298828125,
0.00013768672943115234,
0.013153076171875,
-0.0081787109375,
0.047027587890625,
0.029052734375,
-0.06378173828125,
-0.034637451171875,
-0.040069580078125,
-0... |
result-kand2-sdxl-wuerst-karlo/cfc9bbcd | 2023-10-08T13:50:52.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 218 | 2023-10-08T13:50:51 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 187
num_examples: 10
download_size: 1339
dataset_size: 187
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cfc9bbc... | 455 | [
[
-0.0433349609375,
-0.0180206298828125,
0.0194549560546875,
0.01947021484375,
-0.0141754150390625,
0.0152435302734375,
0.0180206298828125,
-0.00569915771484375,
0.055023193359375,
0.027984619140625,
-0.058135986328125,
-0.052276611328125,
-0.03509521484375,
-... |
lener_br | 2023-09-25T07:35:39.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pt",
"license:unknown",
"legal",
"region:... | null | LeNER-Br is a Portuguese language dataset for named entity recognition
applied to legal documents. LeNER-Br consists entirely of manually annotated
legislation and legal cases texts and contains tags for persons, locations,
time entities, organizations, legislation and legal cases.
To compose the dataset, 66 legal docu... | @inproceedings{luz_etal_propor2018,
author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and
Renato R. R. {de Oliveira} and Matheus Stauffer and
Samuel Couto and Paulo Bermejo},
title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
booktitle = {Internat... | 21 | 217 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: lener-br
pretty_na... | 6,100 | [
[
-0.03497314453125,
-0.043487548828125,
0.01320648193359375,
0.0211944580078125,
-0.0235443115234375,
0.003787994384765625,
-0.0322265625,
-0.0372314453125,
0.040557861328125,
0.034515380859375,
-0.036376953125,
-0.068603515625,
-0.0469970703125,
0.0267639160... |
sharc_modified | 2022-11-03T16:31:23.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|sharc",
"language:en",
"license:unknow... | null | ShARC, a conversational QA task, requires a system to answer user questions based on rules expressed in natural language text. However, it is found that in the ShARC dataset there are multiple spurious patterns that could be exploited by neural models. SharcModified is a new dataset which reduces the patterns identifie... | @inproceedings{verma-etal-2020-neural,
title = "Neural Conversational {QA}: Learning to Reason vs Exploiting Patterns",
author = "Verma, Nikhil and
Sharma, Abhishek and
Madan, Dhiraj and
Contractor, Danish and
Kumar, Harshit and
Joshi, Sachindra",
booktitle = "Proceedings ... | 0 | 217 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|sharc
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_na... | 10,107 | [
[
-0.037811279296875,
-0.07708740234375,
0.0191192626953125,
0.0121307373046875,
-0.027679443359375,
-0.005828857421875,
-0.00848388671875,
-0.0249176025390625,
0.04693603515625,
0.04351806640625,
-0.049957275390625,
-0.04803466796875,
-0.025054931640625,
0.00... |
EMBO/biolang | 2023-01-11T15:31:53.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n>1M",
"language:en",
"license:cc-by-4.0",
"region:us"
] | EMBO | This dataset is based on abstracts from the open access section of EuropePubMed Central to train language models in the domain of biology. | @Unpublished{
huggingface: dataset,
title = {biolang},
authors={Thomas Lemberger, EMBO},
year={2021}
} | 0 | 217 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n>1M
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for BioLang
## Table of Contents
- [Dat... | 7,750 | [
[
-0.037750244140625,
-0.039276123046875,
0.01551055908203125,
0.0167083740234375,
-0.0136260986328125,
0.01503753662109375,
-0.0150909423828125,
-0.00859832763671875,
0.049346923828125,
0.032196044921875,
-0.04742431640625,
-0.061126708984375,
-0.03997802734375,
... |
newspop | 2022-11-03T16:31:06.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"social-media-shares-prediction",
"arxiv:18... | null | This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn.
The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and pale... | @article{Moniz2018MultiSourceSF,
title={Multi-Source Social Feedback of Online News Feeds},
author={N. Moniz and L. Torgo},
journal={ArXiv},
year={2018},
volume={abs/1801.07055}
} | 2 | 216 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: News Popularity in Multipl... | 4,944 | [
[
-0.034942626953125,
-0.057342529296875,
0.0179901123046875,
0.043548583984375,
-0.02435302734375,
0.0036373138427734375,
-0.0246124267578125,
-0.0285186767578125,
0.058868408203125,
0.00689697265625,
-0.048095703125,
-0.08001708984375,
-0.056640625,
-0.00055... |
bond005/sberdevices_golos_10h_crowd | 2022-10-27T04:42:07.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"source_datasets:extended",
"language:... | bond005 | null | null | 1 | 216 | 2022-10-26T11:12:15 | ---
pretty_name: Golos
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ru
license:
- other
multilinguality:
- monolingual
paperswithcode_id: golos
size_categories:
- 10K<n<100k
source_datasets:
- extended
task_categories:
- automatic-speech-recognition
- audio-c... | 6,683 | [
[
-0.0245361328125,
-0.02935791015625,
-0.006069183349609375,
0.02099609375,
-0.024200439453125,
-0.00525665283203125,
-0.031585693359375,
-0.030029296875,
0.034637451171875,
0.0247344970703125,
-0.05426025390625,
-0.0469970703125,
-0.043243408203125,
0.006275... |
DDSC/partial-danish-gigaword-small-test-sample | 2023-01-09T13:11:16.000Z | [
"language:da",
"region:us"
] | DDSC | null | null | 0 | 216 | 2023-01-09T13:07:16 | ---
dataset_info:
features:
- name: text
dtype: string
- name: source
dtype: string
- name: doc_id
dtype: string
- name: LICENSE
dtype: string
- name: uri
dtype: string
- name: date_built
dtype: string
splits:
- name: train
num_bytes: 23816547.04337273
num_examples: 241... | 1,420 | [
[
-0.04833984375,
-0.035003662109375,
0.0033321380615234375,
0.03167724609375,
-0.04364013671875,
0.00415802001953125,
-0.0217132568359375,
-0.01458740234375,
0.044158935546875,
0.03271484375,
-0.055816650390625,
-0.037078857421875,
-0.0191192626953125,
0.0155... |
tum-nlp/IDMGSP | 2023-09-12T11:57:59.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:openrail++",
"scientific paper",
"fake papers",
"science",
"scientific text",
"region:us"
] | tum-nlp | TODO | null | 3 | 216 | 2023-05-27T12:20:55 | ---
viewer: true
task_categories:
- text-classification
language:
- en
tags:
- scientific paper
- fake papers
- science
- scientific text
pretty_name: ' A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era (IDMGSP)'
size_categories:
- 10K<n<100K
dataset_info:
- config_name: classifier_... | 16,024 | [
[
-0.03814697265625,
-0.032135009765625,
0.0234222412109375,
0.01381683349609375,
-0.0172271728515625,
0.004718780517578125,
-0.0012950897216796875,
-0.0325927734375,
0.027008056640625,
0.01262664794921875,
-0.02734375,
-0.05731201171875,
-0.046966552734375,
0... |
yuweiyin/FinBench | 2023-08-02T01:02:19.000Z | [
"task_categories:tabular-classification",
"task_categories:text-classification",
"size_categories:0.3M<n<1M",
"license:cc-by-nc-4.0",
"arxiv:2308.00065",
"region:us"
] | yuweiyin | FinBench Dataset | null | 4 | 216 | 2023-06-18T02:39:45 | ---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- text-classification
size_categories:
- 0.3M<n<1M
---
# Dataset Card for FinBench
## Dataset Description
- **Homepage: https://huggingface.co/datasets/yuweiyin/FinBench**
- **Repository: https://huggingface.co/datasets/yuweiyin/FinBench**
- **Pap... | 9,224 | [
[
-0.0239410400390625,
-0.0382080078125,
0.0046539306640625,
0.005039215087890625,
0.0031261444091796875,
-0.01092529296875,
-0.002063751220703125,
-0.0157012939453125,
0.01947021484375,
0.0380859375,
-0.0306854248046875,
-0.062347412109375,
-0.0278472900390625,
... |
OpenGVLab/InternVid | 2023-07-21T07:32:42.000Z | [
"task_categories:feature-extraction",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2307.06942",
"region:us"
] | OpenGVLab | The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby sh... | @article{wang2023internvid,
title={InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation},
author={Wang, Yi and He, Yinan and Li, Yizhuo and Li, Kunchang and Yu, Jiashuo and Ma, Xin and Chen, Xinyuan and Wang, Yaohui and Luo, Ping and Liu, Ziwei and Wang, Yali and Wang, Limin and Q... | 20 | 216 | 2023-07-14T07:24:39 | ---
license: cc-by-nc-sa-4.0
task_categories:
- feature-extraction
language:
- en
size_categories:
- 10M<n<100M
---
# InternVid
## Dataset Description
- **Homepage:** [InternVid](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid)
- **Repository:** [OpenGVLab](https://github.com/OpenGVLab/InternVideo... | 2,040 | [
[
-0.036346435546875,
-0.052764892578125,
-0.0010213851928710938,
0.01403045654296875,
-0.021575927734375,
-0.0181884765625,
-0.03228759765625,
0.00933074951171875,
-0.01398468017578125,
0.00013267993927001953,
-0.035888671875,
-0.046630859375,
-0.038055419921875,... |
afrikaans_ner_corpus | 2023-01-25T14:20:30.000Z | [
"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:af",
"license:other",
"region:us"
] | null | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | @inproceedings{afrikaans_ner_corpus,
author = { Gerhard van Huyssteen and
Martin Puttkammer and
E.B. Trollip and
J.C. Liversage and
Roald Eiselen},
title = {NCHLT Afrikaans Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Governmen... | 3 | 215 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- af
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Afrikaans Ner Corpus
license... | 5,689 | [
[
-0.0360107421875,
-0.03436279296875,
-0.006946563720703125,
0.031524658203125,
-0.01708984375,
-0.00861358642578125,
-0.02301025390625,
-0.033477783203125,
0.0479736328125,
0.0462646484375,
-0.032257080078125,
-0.056060791015625,
-0.06787109375,
0.0418090820... |
datacommons_factcheck | 2023-06-01T14:59:47.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | null | A dataset of fact checked claims by news media maintained by datacommons.org | @InProceedings{huggingface:dataset,
title = {Data Commons 2019 Fact Checks},
authors={datacommons.org},
year={2019}
} | 3 | 215 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: null
pretty_name: DataCommons F... | 7,577 | [
[
-0.043487548828125,
-0.048431396484375,
0.038055419921875,
0.0072021484375,
-0.0418701171875,
-0.0008707046508789062,
-0.005161285400390625,
-0.0222320556640625,
0.0277252197265625,
0.0391845703125,
-0.044586181640625,
-0.054229736328125,
-0.057586669921875,
... |
doqa | 2023-04-05T10:04:58.000Z | [
"language:en",
"arxiv:2005.01328",
"region:us"
] | null | DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues
(10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also
Community Question Answering sites, as... | @misc{campos2020doqa,
title={DoQA -- Accessing Domain-Specific FAQs via Conversational QA},
author={Jon Ander Campos and Arantxa Otegi and Aitor Soroa and Jan Deriu and Mark Cieliebak and Eneko Agirre},
year={2020},
eprint={2005.01328},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 0 | 215 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: doqa
pretty_name: DoQA
dataset_info:
- config_name: cooking
features:
- name: title
dtype: string
- name: background
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
sequence:
... | 12,661 | [
[
-0.0489501953125,
-0.062744140625,
0.0277862548828125,
0.004974365234375,
0.005035400390625,
-0.002773284912109375,
-0.0011453628540039062,
-0.0033245086669921875,
0.037322998046875,
0.05560302734375,
-0.06207275390625,
-0.03521728515625,
-0.044677734375,
0.... |
poleval2019_mt | 2022-11-18T21:39:08.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:pl",
"language:ru",
"license:unknown",
"region... | null | PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish.Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according topre-established procedures. One of the tasks in PolEval-2019 was Machine Tran... | null | 0 | 215 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
- found
language:
- en
- pl
- ru
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: Poleval2019Mt
data... | 7,237 | [
[
-0.029327392578125,
-0.0389404296875,
0.0157318115234375,
0.0134429931640625,
-0.049468994140625,
0.0118865966796875,
-0.024261474609375,
-0.0248565673828125,
0.0167083740234375,
0.031463623046875,
-0.03955078125,
-0.06878662109375,
-0.04571533203125,
0.0317... |
mxeval/multi-humaneval | 2023-03-20T19:20:48.000Z | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"mxeval",
"code-generation",
"multi-humaneval",
"humaneval",
"arxiv:2210.14868",
"region:us"
] | mxeval | A collection of execution-based multi-lingual benchmark for code generation. | @article{mbxp_athiwaratkun2022,
title = {Multi-lingual Evaluation of Code Generation Models},
author = {Athiwaratkun, Ben and
Gouda, Sanjay Krishna and
Wang, Zijian and
Li, Xiaopeng and
Tian, Yuchen and
Tan, Ming
and Ahmad, Wasi Uddin and
Wang, Shiqi and
Sun, Qing and
Shang, Mingyue and
... | 3 | 215 | 2023-03-14T21:37:18 | ---
dataset_info:
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: prompt
dtype: string
- name: test
dtype: string
- name: entry_point
dtype: string
splits:
- name: multi-humaneval_python
num_bytes: 165716
num_examples: 164
download_size: 67983... | 8,125 | [
[
-0.032989501953125,
-0.046905517578125,
0.01629638671875,
0.023529052734375,
0.007472991943359375,
0.0026721954345703125,
-0.023406982421875,
-0.0167083740234375,
0.00015652179718017578,
0.0278167724609375,
-0.041473388671875,
-0.05511474609375,
-0.0303039550781... |
C-MTEB/CLSClusteringP2P | 2023-07-27T17:29:48.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 215 | 2023-07-27T17:29:10 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 56780231
num_examples: 10
download_size: 37254736
dataset_size: 56780231
---
# ... | 488 | [
[
-0.036285400390625,
-0.00286865234375,
0.010101318359375,
0.04058837890625,
-0.02032470703125,
-0.0064239501953125,
0.01458740234375,
-0.029205322265625,
0.044525146484375,
0.041595458984375,
-0.051300048828125,
-0.037841796875,
-0.05230712890625,
-0.0284423... |
yzhuang/autotree_automl_10000_electricity_sgosdt_l256_dim7_d3_sd0 | 2023-09-07T02:45:46.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 215 | 2023-09-07T02:45:40 | ---
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... | 848 | [
[
-0.02789306640625,
-0.006633758544921875,
0.0210113525390625,
0.01959228515625,
-0.01885986328125,
0.01079559326171875,
0.04364013671875,
0.0053253173828125,
0.048797607421875,
0.03106689453125,
-0.045928955078125,
-0.036895751953125,
-0.039215087890625,
0.0... |
hmao/new_vt_apis | 2023-10-26T00:50:57.000Z | [
"region:us"
] | hmao | null | null | 0 | 215 | 2023-10-13T04:28:16 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: args_dicts
list:
- name: default
dtype: string
- name: description
dtype: string
- name: name
dtype: string
- name: required
dtype: bool
- name: t... | 772 | [
[
-0.048248291015625,
-0.0269622802734375,
0.0161285400390625,
0.004993438720703125,
-0.020233154296875,
0.0101165771484375,
0.031890869140625,
-0.002899169921875,
0.049102783203125,
0.04791259765625,
-0.05804443359375,
-0.0689697265625,
-0.032012939453125,
-0... |
lst20 | 2023-01-25T14:34:28.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:th",
"license:other",
"wo... | null | LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002... | @article{boonkwan2020annotation,
title={The Annotation Guideline of LST20 Corpus},
author={Boonkwan, Prachya and Luantangsrisuk, Vorapon and Phaholphinyo, Sitthaa and Kriengket, Kanyanat and Leenoi, Dhanon and Phrombut, Charun and Boriboon, Monthika and Kosawat, Krit and Supnithi, Thepchai},
journal={arXiv prepri... | 2 | 214 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
pretty_name: LST20
tags:
- word-s... | 9,481 | [
[
-0.036041259765625,
-0.05035400390625,
0.01788330078125,
0.0259246826171875,
-0.032379150390625,
-0.002910614013671875,
-0.026092529296875,
-0.032958984375,
0.0306854248046875,
0.03839111328125,
-0.0316162109375,
-0.056365966796875,
-0.04144287109375,
0.0280... |
sepidmnorozy/Korean_sentiment | 2022-08-16T09:25:48.000Z | [
"region:us"
] | sepidmnorozy | null | null | 1 | 214 | 2022-08-16T09:25:01 | 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... |
philschmid/emotion | 2023-01-20T14:56:20.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"emotion-classific... | philschmid | Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. | @inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empi... | 1 | 214 | 2023-01-20T14:56:20 | ---
pretty_name: Emotion
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_... | 8,802 | [
[
-0.038909912109375,
-0.050567626953125,
0.01465606689453125,
0.023101806640625,
-0.0267791748046875,
-0.0034313201904296875,
-0.02978515625,
-0.0364990234375,
0.05133056640625,
0.014556884765625,
-0.05706787109375,
-0.07659912109375,
-0.0513916015625,
0.0159... |
axiong/pmc_oa | 2023-08-22T17:42:06.000Z | [
"region:us"
] | axiong | Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity.
To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset,... | @article{lin2023pmc,
title={PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents},
author={Lin, Weixiong and Zhao, Ziheng and Zhang, Xiaoman and Wu, Chaoyi and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
journal={arXiv preprint arXiv:2303.07240},
year={2023}
} | 15 | 214 | 2023-04-02T02:30:31 | # PMC-OA Dataset
**News: We have released the PMC-OA dataset. You can choose the subset specifically.**
**P.S.** There's something wrong with the huggingface dataset viewer when the dataset scale gets large.
So we sample a subset of it to visualize it directly on web. Click [PMC-OA-Demo](https://huggingface.co/datase... | 1,782 | [
[
-0.05426025390625,
-0.033203125,
0.00986480712890625,
0.03399658203125,
-0.0390625,
-0.007198333740234375,
0.01409149169921875,
-0.0187530517578125,
0.0211181640625,
0.0472412109375,
-0.06573486328125,
-0.0472412109375,
-0.026519775390625,
0.0069847106933593... |
IlyaGusev/ru_turbo_saiga | 2023-09-04T13:26:47.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:ru",
"license:cc-by-4.0",
"chat",
"region:us"
] | IlyaGusev | null | null | 11 | 214 | 2023-04-08T20:53:59 | ---
dataset_info:
features:
- name: messages
sequence:
- name: role
dtype: string
- name: content
dtype: string
- name: seed
dtype: string
- name: source
dtype: string
- name: model_name
dtype: string
splits:
- name: train
num_bytes: 87316730
num_examples: 37731... | 1,723 | [
[
-0.0183868408203125,
-0.0411376953125,
0.0235748291015625,
0.016204833984375,
-0.0318603515625,
-0.012664794921875,
-0.0234222412109375,
-0.007007598876953125,
0.01971435546875,
0.0123138427734375,
-0.052032470703125,
-0.056365966796875,
-0.040802001953125,
... |
yuvalkirstain/pickapic_v1_no_images | 2023-04-16T14:53:35.000Z | [
"region:us"
] | yuvalkirstain | null | null | 0 | 214 | 2023-04-16T14:52:20 | ---
dataset_info:
features:
- name: are_different
dtype: bool
- name: best_image_uid
dtype: string
- name: caption
dtype: string
- name: created_at
dtype: timestamp[ns]
- name: has_label
dtype: bool
- name: image_0_uid
dtype: string
- name: image_0_url
dtype: string
- name:... | 1,283 | [
[
-0.049896240234375,
-0.0223846435546875,
0.01221466064453125,
0.0236968994140625,
-0.045684814453125,
-0.02056884765625,
0.044891357421875,
-0.0191192626953125,
0.075439453125,
0.03778076171875,
-0.0654296875,
-0.052734375,
-0.05010986328125,
-0.018615722656... |
C-MTEB/IFlyTek-classification | 2023-07-28T13:30:24.000Z | [
"region:us"
] | C-MTEB | null | null | 1 | 214 | 2023-07-28T13:30:02 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
... | 3,278 | [
[
-0.03887939453125,
0.0208740234375,
0.0081787109375,
0.0105438232421875,
-0.00823211669921875,
0.0030803680419921875,
0.0106658935546875,
-0.037322998046875,
0.038909912109375,
0.011688232421875,
-0.05792236328125,
-0.059539794921875,
-0.046234130859375,
-0.... |
banghua/hh_reward_model_labeled | 2023-08-06T02:03:27.000Z | [
"region:us"
] | banghua | null | null | 0 | 214 | 2023-08-04T21:23:15 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 225756769
num_examples: 124503
download_size: 136142109
dataset_size: 225756769
---
# Dataset Car... | 484 | [
[
-0.02001953125,
-0.018951416015625,
0.0107269287109375,
0.0077056884765625,
-0.0038623809814453125,
-0.005695343017578125,
0.02459716796875,
-0.0131072998046875,
0.05389404296875,
0.03851318359375,
-0.049163818359375,
-0.0550537109375,
-0.04742431640625,
-0.... |
distil-whisper/meanwhile | 2023-10-17T17:17:28.000Z | [
"arxiv:2212.04356",
"region:us"
] | distil-whisper | null | null | 0 | 214 | 2023-09-19T15:45:32 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype: audio
- name: begin
dtype: string
- name: end
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 58250833.0
num_examples: 64
... | 641 | [
[
-0.01416778564453125,
-0.0350341796875,
0.02459716796875,
0.0023670196533203125,
-0.0170135498046875,
0.00909423828125,
-0.00695037841796875,
-0.006072998046875,
0.05145263671875,
0.0255889892578125,
-0.05267333984375,
0.004669189453125,
-0.00940704345703125,
... |
nlu_evaluation_data | 2023-01-25T14:41:34.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"li... | null | Raw part of NLU Evaluation Data. It contains 25 715 non-empty examples (original dataset has 25716 examples) from 68 unique intents belonging to 18 scenarios. | @InProceedings{XLiu.etal:IWSDS2019,
author = {Xingkun Liu, Arash Eshghi, Pawel Swietojanski and Verena Rieser},
title = {Benchmarking Natural Language Understanding Services for building Conversational Agents},
booktitle = {Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technolo... | 7 | 213 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
pretty_nam... | 11,633 | [
[
-0.04351806640625,
-0.0474853515625,
0.0184173583984375,
0.005817413330078125,
0.0008168220520019531,
-0.0049591064453125,
-0.0234527587890625,
-0.030548095703125,
0.04168701171875,
0.043426513671875,
-0.059600830078125,
-0.06109619140625,
-0.0276031494140625,
... |
EMBO/BLURB | 2022-12-09T07:57:37.000Z | [
"task_categories:question-answering",
"task_categories:token-classification",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:closed-domain-qa",
"task_ids:named-entity-recognition",
"task_ids:parsing",
"task_ids:semantic-similarity-scoring",
"task_ids:text-sco... | EMBO | null | null | 3 | 213 | 2022-03-14T10:29:16 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- token-classification
- sentence-similarity
- text-classification
task_ids... | 30,312 | [
[
-0.0256195068359375,
-0.048370361328125,
0.0233154296875,
0.0196685791015625,
-0.0180511474609375,
0.0107421875,
-0.020599365234375,
-0.0565185546875,
0.0355224609375,
0.0220794677734375,
-0.0302581787109375,
-0.06304931640625,
-0.045623779296875,
0.02851867... |
tonytan48/Re-DocRED | 2022-11-25T02:48:32.000Z | [
"license:mit",
"arxiv:2205.12696",
"region:us"
] | tonytan48 | null | null | 0 | 213 | 2022-11-25T02:42:48 | ---
license: mit
---
# Re-DocRED Dataset
This repository contains the dataset of our EMNLP 2022 research paper [Revisiting DocRED – Addressing the False Negative Problem
in Relation Extraction](https://arxiv.org/pdf/2205.12696.pdf).
DocRED is a widely used benchmark for document-level relation extraction. However, th... | 1,553 | [
[
-0.022430419921875,
-0.044036865234375,
0.03338623046875,
-0.0045013427734375,
-0.003467559814453125,
-0.0303192138671875,
-0.005596160888671875,
-0.026611328125,
0.0255889892578125,
0.05316162109375,
-0.035858154296875,
-0.0455322265625,
-0.034881591796875,
... |
ChilleD/MultiArith | 2023-05-02T01:44:21.000Z | [
"region:us"
] | ChilleD | null | null | 2 | 213 | 2023-05-01T13:19:47 | 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... |
LDJnr/Verified-Camel | 2023-09-26T02:33:30.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"Physics",
"Biology",
"Math",
"Chemistry",
"Culture",
"Logic",
"region:us"
] | LDJnr | null | null | 10 | 213 | 2023-09-26T02:20:36 | ---
license: apache-2.0
task_categories:
- conversational
- question-answering
- text-generation
language:
- en
tags:
- Physics
- Biology
- Math
- Chemistry
- Culture
- Logic
pretty_name: Verified-Camel
size_categories:
- n<1K
---
## This is the Official Verified Camel dataset. Just over 100 verified examples, and man... | 1,935 | [
[
-0.0179443359375,
-0.04718017578125,
0.003177642822265625,
0.0174713134765625,
-0.0113677978515625,
-0.005092620849609375,
0.0110015869140625,
-0.039703369140625,
0.001338958740234375,
0.04254150390625,
-0.05364990234375,
-0.031494140625,
-0.026641845703125,
... |
atmallen/qm_bob_1.0e_eval | 2023-10-31T19:45:06.000Z | [
"region:us"
] | atmallen | null | null | 0 | 213 | 2023-10-27T05:42:42 | ---
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: summand1
dtype: int64
- name: summand2
dtype: int64
- name: character
dtype: string
- na... | 1,127 | [
[
-0.03912353515625,
-0.034149169921875,
0.00902557373046875,
0.0244293212890625,
-0.0251007080078125,
0.022308349609375,
0.0288848876953125,
0.01043701171875,
0.05877685546875,
0.046234130859375,
-0.055816650390625,
-0.060821533203125,
-0.0287933349609375,
-0... |
ccdv/patent-classification | 2022-10-22T09:25:36.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"size_categories:10K<n<100K",
"language:en",
"long context",
"region:us"
] | ccdv | Patent Classification Dataset: a classification of Patents (9 classes).
It contains 9 unbalanced classes, 35k Patents and summaries divided into 3 splits: train (25k), val (5k) and test (5k).
Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li an... | null | 5 | 212 | 2022-03-02T23:29:22 | ---
language: en
task_categories:
- text-classification
tags:
- long context
task_ids:
- multi-class-classification
- topic-classification
size_categories: 10K<n<100K
---
**Patent Classification: a classification of Patents and abstracts (9 classes).**
This dataset is intended for long context classification (non ab... | 1,335 | [
[
-0.0091400146484375,
-0.0253143310546875,
0.016082763671875,
0.030426025390625,
-0.0226593017578125,
0.0034160614013671875,
-0.00995635986328125,
-0.026763916015625,
0.014495849609375,
0.031524658203125,
-0.004547119140625,
-0.050811767578125,
-0.048797607421875... |
dali-does/clevr-math | 2022-10-31T11:28:31.000Z | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:clevr",
"language:en",
"license:cc-by-4.0",
"reasoning",
"neuro-symbolic",
"multimod... | dali-does | CLEVR-Math is a dataset for compositional language, visual and mathematical reasoning. CLEVR-Math poses questions about mathematical operations on visual scenes using subtraction and addition, such as "Remove all large red cylinders. How many objects are left?". There are also adversarial (e.g. "Remove all blue cubes. ... | @misc{https://doi.org/10.48550/arxiv.2208.05358,
doi = {10.48550/ARXIV.2208.05358},
url = {https://arxiv.org/abs/2208.05358},
author = {Lindström, Adam Dahlgren and Abraham, Savitha Sam},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS... | 4 | 212 | 2022-08-06T12:09:39 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: CLEVR-Math - Compositional language, visual, and mathematical reasoning
size_categories:
#- 100K<n<1M
source_datasets: [clevr]
tags:
- reasoning
- neuro-sy... | 4,708 | [
[
-0.046661376953125,
-0.03961181640625,
-0.00168609619140625,
0.021209716796875,
-0.015716552734375,
-0.0136260986328125,
-0.03814697265625,
-0.0191192626953125,
0.0067138671875,
0.0182952880859375,
-0.052642822265625,
-0.055267333984375,
-0.033477783203125,
... |
Amani27/massive_translation_dataset | 2023-07-25T14:54:44.000Z | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"language:de",
"language:es",
"language:hi",
"language:fr",
"language:it",
"language:ar",
"language:nl",
"language:ja",
"language:pt",
"license:cc-by-4.0",
"region:us"
] | Amani27 | null | null | 3 | 212 | 2023-07-20T16:09:42 | ---
configs:
- config_name: default
data_files:
- split: train
path: "train.csv"
- split: validation
path: "validation.csv"
- split: test
path: "test.csv"
license: cc-by-4.0
task_categories:
- translation
language:
- en
- de
- es
- hi
- fr
- it
- ar
- nl
- ja
- pt
size_categories:
- 10K<n<100K
... | 740 | [
[
-0.0041656494140625,
-0.04071044921875,
0.009033203125,
0.04656982421875,
-0.018585205078125,
0.032562255859375,
-0.0308685302734375,
-0.021942138671875,
0.019561767578125,
0.03985595703125,
-0.042572021484375,
-0.06494140625,
-0.0723876953125,
0.03894042968... |
hmao/vt_multiapi_v0 | 2023-10-19T16:52:49.000Z | [
"region:us"
] | hmao | null | null | 0 | 212 | 2023-10-14T04:51:56 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: fncall
sequence: string
- name: generated_question
dtype: string
splits:
- name: train
num_bytes: 25028
num_examples: 70
download_size: 12622
dataset_size: 25028
---
# ... | 486 | [
[
-0.04547119140625,
-0.007205963134765625,
0.01947021484375,
0.015289306640625,
-0.02362060546875,
0.0027484893798828125,
0.037384033203125,
-0.004863739013671875,
0.0643310546875,
0.03167724609375,
-0.0579833984375,
-0.04559326171875,
-0.031280517578125,
-0.... |
derek-thomas/dataset-creator-reddit-amitheasshole | 2023-11-03T01:00:10.000Z | [
"region:us"
] | derek-thomas | null | null | 0 | 212 | 2023-10-27T16:21:23 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: content
dtype: string
- name: poster
dtype: string
- name: date_utc
dtype: timestamp[ns]
- name: flair
dtype: 'null'
- name: title
dtype: string
- name: permalink
... | 2,699 | [
[
-0.0477294921875,
-0.042236328125,
0.0215301513671875,
0.03521728515625,
-0.038177490234375,
-0.0229339599609375,
-0.01690673828125,
-0.0257568359375,
0.05218505859375,
0.043975830078125,
-0.057647705078125,
-0.0511474609375,
-0.0426025390625,
0.035369873046... |
hlgd | 2023-01-25T14:32:19.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"headline-grouping",
"region:us"
] | null | HLGD is a binary classification dataset consisting of 20,056 labeled news headlines pairs indicating
whether the two headlines describe the same underlying world event or not. | @inproceedings{Laban2021NewsHG,
title={News Headline Grouping as a Challenging NLU Task},
author={Philippe Laban and Lucas Bandarkar},
booktitle={NAACL 2021},
publisher = {Association for Computational Linguistics},
year={2021}
} | 2 | 210 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Headline Grouping (HLGD)
tags:
- headline-grouping... | 9,978 | [
[
-0.039215087890625,
-0.06292724609375,
0.0156402587890625,
0.04052734375,
-0.009368896484375,
0.0117034912109375,
-0.01044464111328125,
-0.035308837890625,
0.023162841796875,
0.0172576904296875,
-0.05316162109375,
-0.06573486328125,
-0.04095458984375,
0.0050... |
IlyaGusev/headline_cause | 2023-02-12T00:02:58.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ru",
"language:en",
"license:cc0-1.0",
"causal-reasoni... | IlyaGusev | null | @misc{gusev2021headlinecause,
title={HeadlineCause: A Dataset of News Headlines for Detecting Casualties},
author={Ilya Gusev and Alexey Tikhonov},
year={2021},
eprint={2108.12626},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 2 | 210 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ru
- en
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: HeadlineCause
tags:
- causal-rea... | 6,273 | [
[
-0.015899658203125,
-0.04559326171875,
0.036163330078125,
0.033294677734375,
-0.0225677490234375,
-0.011566162109375,
-0.0184478759765625,
-0.03302001953125,
0.034881591796875,
0.0252532958984375,
-0.04669189453125,
-0.07354736328125,
-0.05029296875,
0.01773... |
maritaca-ai/imdb_pt | 2023-04-01T16:15:34.000Z | [
"region:us"
] | maritaca-ai | Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.\ | @InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for... | 2 | 210 | 2023-01-26T14:20:51 | 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... |
vlsp-2023-vllm/ai2_arc_vi | 2023-10-08T09:54:04.000Z | [
"region:us"
] | vlsp-2023-vllm | null | null | 0 | 210 | 2023-09-29T18:17:01 | ---
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: id
dtype: string
- name: question
dtype: string
- name: choices
struct:
- name: label
... | 2,638 | [
[
-0.030242919921875,
-0.058868408203125,
0.03631591796875,
0.0217437744140625,
0.00524139404296875,
-0.01020050048828125,
0.00374603271484375,
-0.0189666748046875,
0.00852203369140625,
0.03814697265625,
-0.05389404296875,
-0.0247955322265625,
-0.03997802734375,
... |
sem_eval_2014_task_1 | 2023-01-25T14:43:53.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extend... | null | The SemEval-2014 Task 1 focuses on Evaluation of Compositional Distributional Semantic Models
on Full Sentences through Semantic Relatedness and Entailment. The task was designed to
predict the degree of relatedness between two sentences and to detect the entailment
relation holding between them. | @inproceedings{inproceedings,
author = {Marelli, Marco and Bentivogli, Luisa and Baroni, Marco and Bernardi, Raffaella and Menini, Stefano and Zamparelli, Roberto},
year = {2014},
month = {08},
pages = {},
title = {SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through... | 1 | 209 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-ImageFlickr and SemEval-2012 STS MSR-Video Descriptions
task_categories:
- text-classification
task_ids:
- text-... | 3,564 | [
[
-0.038116455078125,
-0.030548095703125,
0.0181884765625,
0.0166473388671875,
-0.02410888671875,
-0.0046539306640625,
-0.012420654296875,
-0.0192108154296875,
0.038421630859375,
0.059112548828125,
-0.06500244140625,
-0.07135009765625,
-0.052734375,
0.00595855... |
alistvt/coqa-stories | 2022-01-20T22:17:46.000Z | [
"region:us"
] | alistvt | null | null | 1 | 209 | 2022-03-02T23:29:22 | This is a dataset containing just stories of the CoQA dataset with their respective ids. This can be used in the pretraining phase for the MLM tasks. | 149 | [
[
-0.0211944580078125,
-0.032958984375,
-0.0033130645751953125,
0.018218994140625,
-0.01494598388671875,
0.0121917724609375,
0.0305938720703125,
0.004543304443359375,
0.03656005859375,
0.0640869140625,
-0.0997314453125,
-0.04876708984375,
0.0003941059112548828,
... |
Harsit/xnli2.0_train_swahili | 2022-10-15T09:22:30.000Z | [
"region:us"
] | Harsit | null | null | 0 | 209 | 2022-10-15T09:21:59 | 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... |
Francesco/abdomen-mri | 2023-03-30T09:41:54.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
] | Francesco | null | null | 0 | 209 | 2023-03-30T09:41:19 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
lengt... | 3,324 | [
[
-0.049346923828125,
-0.01861572265625,
0.01323699951171875,
-0.00872802734375,
-0.041168212890625,
-0.022857666015625,
-0.0020465850830078125,
-0.029144287109375,
0.0236968994140625,
0.0269012451171875,
-0.0400390625,
-0.07183837890625,
-0.03814697265625,
0.... |
distil-whisper/tedlium-long-form | 2023-05-22T14:04:04.000Z | [
"region:us"
] | distil-whisper | null | null | 0 | 209 | 2023-05-22T13:19:52 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
- name: speaker_id
dtype: string
splits:
- name: validation
num_bytes: 180166870.0
num_examples: 8
- name: test
num_bytes: 285107770.0
num_examples: 11
download_size: 284926490
dataset_size: 4... | 2,356 | [
[
-0.0299072265625,
-0.0478515625,
0.0216064453125,
0.01038360595703125,
-0.0199432373046875,
-0.0005321502685546875,
-0.038482666015625,
0.00585174560546875,
0.013092041015625,
0.02923583984375,
-0.048858642578125,
-0.06072998046875,
-0.0167083740234375,
0.00... |
rguo123/trump_tweets | 2023-08-07T14:11:46.000Z | [
"region:us"
] | rguo123 | null | null | 0 | 209 | 2023-07-10T19:55:56 | 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... |
result-kand2-sdxl-wuerst-karlo/25b005b7 | 2023-10-08T22:30:24.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 209 | 2023-10-08T22:30:23 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1383
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "25b005b... | 455 | [
[
-0.053619384765625,
-0.0032672882080078125,
0.00830078125,
0.03668212890625,
-0.0149383544921875,
-0.001224517822265625,
0.02850341796875,
-0.0222625732421875,
0.045196533203125,
0.038177490234375,
-0.061126708984375,
-0.050445556640625,
-0.033599853515625,
... |
albertvillanova/pmc_open_access | 2023-01-16T13:43:54.000Z | [
"region:us"
] | albertvillanova | The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under
license terms that allow reuse.
Not all articles in PMC are available for text mining and other reuse, many have copyright protection, however articles
in the PMC Open Access Subset are made availabl... | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | 0 | 208 | 2022-03-02T23:29:22 | # Dataset Card for pmc_open_access
## Dataset Description
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dar... | 527 | [
[
-0.038116455078125,
-0.023406982421875,
0.0005059242248535156,
0.028656005859375,
-0.0307769775390625,
-0.0030059814453125,
0.0018215179443359375,
0.0008416175842285156,
0.01230621337890625,
0.0255126953125,
-0.060577392578125,
-0.066650390625,
-0.02827453613281... |
cassandra-themis/QR-AN | 2022-10-24T20:31:22.000Z | [
"task_categories:summarization",
"task_categories:text-classification",
"task_categories:text-generation",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"size_categories:10K<n<100K",
"language:fr",
"conditional-text-generation",
"region:us"
] | cassandra-themis | QR-AN Dataset: a classification dataset on french Parliament debates
This is a dataset for theme/topic classification, made of questions and answers from https://www2.assemblee-nationale.fr/recherche/resultats_questions.
It contains 188 unbalanced classes, 80k questions-answers divided into 3 splits: train (60k), va... | null | 2 | 208 | 2022-03-02T23:29:22 | ---
language:
- fr
size_categories: 10K<n<100K
task_categories:
- summarization
- text-classification
- text-generation
task_ids:
- multi-class-classification
- topic-classification
tags:
- conditional-text-generation
---
**QR-AN Dataset: a classification and generation dataset of french Parliament questions-answers.*... | 1,499 | [
[
-0.04193115234375,
-0.0147857666015625,
0.01174163818359375,
0.01264190673828125,
-0.0199127197265625,
0.0013151168823242188,
-0.002109527587890625,
0.0264129638671875,
0.01084136962890625,
0.04547119140625,
-0.036102294921875,
-0.032745361328125,
-0.03179931640... |
dynabench/qa | 2022-07-02T20:17:58.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2002.0... | dynabench | Dynabench.QA is a Reading Comprehension dataset collected using a human-and-model-in-the-loop. | null | 0 | 208 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
---
# Dataset Card for Dynabench.QA
## Ta... | 11,596 | [
[
-0.03839111328125,
-0.06268310546875,
0.035980224609375,
-0.00746917724609375,
-0.00403594970703125,
0.0244598388671875,
0.0213165283203125,
-0.029541015625,
0.0156707763671875,
0.0322265625,
-0.05645751953125,
-0.048431396484375,
-0.030914306640625,
0.04010... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.