id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
lcw99/wikipedia-korean-20221001 | 2022-10-10T03:55:17.000Z | [
"language:ko",
"region:us"
] | lcw99 | null | null | null | 3 | 1,417 | ---
language:
- ko
--- |
osunlp/ConflictQA | 2023-06-15T18:45:52.000Z | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"arxiv:2305.13300",
"region:us"
] | osunlp | data for ConflictQA. | @article{xie2023adaptive,
title={Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Conflicts},
author={Xie, Jian and Zhang, Kai and Chen, Jiangjie and Lou, Renze and Su, Yu},
journal={arXiv preprint arXiv:2305.13300},
year={2023}
} | null | 4 | 1,417 | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
pretty_name: conflictQA
size_categories:
- 10K<n<100K
---
# Dataset Card for ConflcitQA
## Dataset Description
- **Repository:** https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict
- **Paper:** https://arxiv.org/abs/2305.13300
- **Point o... |
lucadiliello/naturalquestionsshortqa | 2023-06-06T08:35:50.000Z | [
"region:us"
] | lucadiliello | null | null | null | 1 | 1,413 | ---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: key
dtype: string
- name: labels
list:
- name: end
sequence: int64
- name: start
sequence: int64
splits:
- name: train
num_bytes: ... |
keremberke/csgo-object-detection | 2023-01-27T13:39:19.000Z | [
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"region:us"
] | keremberke | null | @misc{ wlots_dataset,
title = { wlots Dataset },
type = { Open Source Dataset },
author = { asd },
howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
url = { https://universe.roboflow.com/asd-culfr/wlots },
journal = { Roboflow Universe },
publisher = { Roboflow },
... | null | 4 | 1,411 | ---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="keremberke/csgo-object-detection" src="https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['ct', 'cthead', 't', 't... |
sahil2801/CodeAlpaca-20k | 2023-10-03T11:46:04.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"code",
"region:us"
] | sahil2801 | null | null | null | 108 | 1,393 | ---
license: cc-by-4.0
task_categories:
- text-generation
tags:
- code
pretty_name: CodeAlpaca 20K
size_categories:
- 10K<n<100K
language:
- en
--- |
banghua/hh_reward_model_labeled | 2023-08-06T02:03:27.000Z | [
"region:us"
] | banghua | null | null | null | 0 | 1,392 | ---
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... |
ola13/small-the_pile | 2022-11-24T11:40:52.000Z | [
"region:us"
] | ola13 | null | null | null | 3 | 1,387 | ---
dataset_info:
features:
- name: text
dtype: string
- name: meta
struct:
- name: perplexity_score
dtype: float64
- name: pile_set_name
dtype: string
splits:
- name: train
num_bytes: 606056668
num_examples: 100000
download_size: 328667964
dataset_size: 606056668
---
#... |
iamtarun/python_code_instructions_18k_alpaca | 2023-07-27T15:51:36.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"code",
"region:us"
] | iamtarun | null | null | null | 12 | 1,377 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 25180782
num_examples: 18612
download_size: 11357076
dataset_size: 25180782
configs:
- config_nam... |
guardian_authorship | 2023-04-05T10:06:55.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region... | null | A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013.
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).
3- The same-... | @article{article,
author = {Stamatatos, Efstathios},
year = {2013},
month = {01},
pages = {421-439},
title = {On the robustness of authorship attribution based on character n-gram features},
volume = {21},
journal = {Journal of Law and Policy}
}
@inproceedings{stamatatos2017authorship,
... | null | 2 | 1,372 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
pretty_name: GuardianAuthorship
datas... |
cs_restaurants | 2022-11-18T19:49:56.000Z | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:machine-gene... | null | This is a dataset for NLG in task-oriented spoken dialogue systems with Czech as the target language. It originated as
a translation of the English San Francisco Restaurants dataset by Wen et al. (2015). | @article{DBLP:journals/corr/abs-1910-05298,
author = {Ondrej Dusek and
Filip Jurcicek},
title = {Neural Generation for Czech: Data and Baselines},
journal = {CoRR},
volume = {abs/1910.05298},
year = {2019},
url = {http://arxiv.org/abs/1910.05298},
archivePrefix = {arX... | null | 1 | 1,369 | ---
annotations_creators:
- found
language_creators:
- expert-generated
- machine-generated
language:
- cs
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-san-francisco-restaurants
task_categories:
- text2text-generation
- text-generation
- fill-mask
tas... |
ccdv/arxiv-summarization | 2022-12-08T06:58:05.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"conditional-text-generation",
"region:us"
] | ccdv | Arxiv dataset for summarization.
From paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" by A. Cohan et al.
See: https://aclanthology.org/N18-2097.pdf
See: https://github.com/armancohan/long-summarization | @inproceedings{cohan-etal-2018-discourse,
title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
author = "Cohan, Arman and
Dernoncourt, Franck and
Kim, Doo Soon and
Bui, Trung and
Kim, Seokhwan and
Chang, Walter and
Goharian, N... | null | 31 | 1,359 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
train-eval-index:
- config: document
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
article... |
masakhane/masakhaner2 | 2023-09-11T18:00:07.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:bm",
"language:bbj",
"language:ee",
"langu... | masakhane | MasakhaNER 2.0 is the largest publicly available high-quality dataset for named entity recognition (NER) in 20 African languages.
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with... | @article{Adelani2022MasakhaNER2A,
title={MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition},
author={David Ifeoluwa Adelani and Graham Neubig and Sebastian Ruder and Shruti Rijhwani and Michael Beukman and Chester Palen-Michel and Constantine Lignos and Jesujoba Oluwadara Alabi and Shams... | null | 8 | 1,351 | ---
annotations_creators:
- expert-generated
language:
- bm
- bbj
- ee
- fon
- ha
- ig
- rw
- lg
- luo
- mos
- ny
- pcm
- sn
- sw
- tn
- tw
- wo
- xh
- yo
- zu
language_creators:
- expert-generated
license:
- afl-3.0
multilinguality:
- multilingual
pretty_name: masakhaner2.0
size_categories:
- 1K<n<10K
source_datasets:... |
Dahoas/cot_gsm8k | 2023-05-31T13:01:00.000Z | [
"region:us"
] | Dahoas | null | null | null | 4 | 1,351 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 7710945
num_examples: 7217
- name: val
num_bytes: 267770
num_examples: 256
- name: test
... |
conll2012_ontonotesv5 | 2023-01-25T15:03:49.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"task_ids:coreference-resolution",
"task_ids:parsing",
"task_ids:lemmatization",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:found",
"multil... | null | OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
multilingual corpus manually annotated with syntactic, semantic and discourse information.
This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task.
It includes v4 train/dev and v9 test d... | @inproceedings{pradhan-etal-2013-towards,
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
author = {Pradhan, Sameer and
Moschitti, Alessandro and
Xue, Nianwen and
Ng, Hwee Tou and
Bj{\"o}rkelund, Anders and
Uryupina, Olga and
Zhang, Yuchen and
Z... | null | 23 | 1,343 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ar
- en
- zh
license:
- cc-by-nc-nd-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
- coreferenc... |
TIGER-Lab/MathInstruct | 2023-10-07T01:40:07.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"arxiv:2309.05653",
"region:us"
] | TIGER-Lab | null | null | null | 87 | 1,340 | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: MathInstruct
size_categories:
- 100K<n<1M
---
# 🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
MathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathIns... |
bigcode/guanaco-commits | 2023-06-28T08:54:47.000Z | [
"region:us"
] | bigcode | null | null | null | 3 | 1,339 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 17347601.0
num_examples: 12958
- name: test
num_bytes: 827046.0
num_examples: 629
download_size: 10948498
dataset_size: 18174647.0
---
# Dataset Card for "gu... |
pie/brat | 2023-09-20T16:04:35.000Z | [
"region:us"
] | pie | null | null | null | 0 | 1,328 | Entry not found |
lhoestq/test | 2022-07-01T15:26:34.000Z | [
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | lhoestq | This is a test dataset. | \ | null | 0 | 1,324 | ---
type: test
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- other-test
task_ids:
- other-test
paperswithcode_id: null
pretty_name: Test Dataset
---
This is a test d... |
HuggingFaceH4/test-dataset-all-splits | 2023-04-25T22:09:49.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | null | 0 | 1,320 | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
- name: messages
list:
- name: cont... |
tner/bc5cdr | 2022-07-18T00:43:04.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
] | tner | [Bio Creative 5 CDR NER dataset](https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true) | @article{wei2016assessing,
title={Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task},
author={Wei, Chih-Hsuan and Peng, Yifan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Li, Jiao and Wiegers, Thomas C and L... | null | 1 | 1,319 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: BioCreative V CDR
---
# Dataset Card for "tner/bc5cdr"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi41... |
Cohere/wikipedia-22-12-en-embeddings | 2023-03-22T16:51:57.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:en",
"license:apache-2.0",
"region:us"
] | Cohere | null | null | null | 34 | 1,315 | ---
annotations_creators:
- expert-generated
language:
- en
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Wikipedia (en) embedded with cohere.ai `multilingual-22-12` encoder
We encoded... |
ccdv/mediasum | 2022-10-25T10:56:04.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"conditional-text-generation",
"region:us"
] | ccdv | MediaSum dataset for summarization.
From paper: "MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization" by C. Zhu et al." | @article{zhu2021mediasum,
title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
journal={arXiv preprint arXiv:2103.06410},
year={2021}
} | null | 5 | 1,309 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# MediaSum dataset for summarization
Summarization dataset copied from [MediaSum: A Large-scale Media Interview Dataset for Dialog... |
bigbio/med_qa | 2023-09-26T13:00:32.000Z | [
"multilinguality:multilingual",
"language:en",
"language:zh",
"license:unknown",
"region:us"
] | bigbio | In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,
collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and
traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages... | @article{jin2021disease,
title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={Applied Sciences},
volume={11},
number={14},
... | null | 20 | 1,308 | ---
language:
- en
- zh
bigbio_language:
- English
- Chinese (Simplified)
- Chinese (Traditional, Taiwan)
license: unknown
multilinguality: multilingual
bigbio_license_shortname: UNKNOWN
pretty_name: MedQA
homepage: https://github.com/jind11/MedQA
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- QUESTION_ANSWER... |
PolyAI/banking77 | 2022-10-25T10:12:22.000Z | [
"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... | PolyAI | BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
It comprises 13,083 customer service queries labeled with 77 intents.
It focuses on fine-grained single-domain intent detection. | @inproceedings{Casanueva2020,
author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic},
title = {Efficient Intent Detection with Dual Sentence Encoders},
year = {2020},
month = {mar},
note = {Data available at https://gi... | null | 17 | 1,305 | ---
annotations_creators:
- expert-generated
extended:
- original
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-clas... |
ChilleD/SVAMP | 2023-04-24T07:55:08.000Z | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:mit",
"region:us"
] | ChilleD | null | null | null | 1 | 1,297 | ---
license: mit
task_categories:
- text-generation
language:
- en
size_categories:
- n<1K
--- |
alzoubi36/piextract | 2023-06-25T07:11:15.000Z | [
"region:us"
] | alzoubi36 | null | null | null | 0 | 1,297 | ---
dataset_info:
features:
- name: COLLECT
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
- name: NOT_COLLECT
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
... |
stingning/ultrachat | 2023-07-04T10:19:58.000Z | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | stingning | null | null | null | 121 | 1,291 | ---
license: cc-by-nc-4.0
task_categories:
- conversational
- text-generation
language:
- en
size_categories:
- 1M<n<10M
pretty_name: UltraChat
---
# Dataset Card for Dataset Name
## Dataset Description
An open-source, large-scale, and multi-round dialogue data powered by Turbo APIs. In consideration of factors such... |
lm1b | 2023-06-27T15:36:19.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"language:en",
"arxiv:1312.3005",
"region:us"
] | null | A benchmark corpus to be used for measuring progress in statistical language modeling. This has almost one billion words in the training data. | @article{DBLP:journals/corr/ChelbaMSGBK13,
author = {Ciprian Chelba and
Tomas Mikolov and
Mike Schuster and
Qi Ge and
Thorsten Brants and
Phillipp Koehn},
title = {One Billion Word Benchmark for Measuring Progress in Statistical Langu... | null | 8 | 1,286 | ---
pretty_name: One Billion Word Language Model Benchmark
paperswithcode_id: billion-word-benchmark
dataset_info:
features:
- name: text
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 4238206516
num_examples: 30301028
- name: test
num_bytes: 42942045
num_examples... |
proteinea/secondary_structure_prediction | 2023-03-02T22:42:31.000Z | [
"doi:10.57967/hf/1104",
"region:us"
] | proteinea | null | null | null | 1 | 1,281 | Entry not found |
visual_genome | 2023-06-29T15:23:59.000Z | [
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:visual-question-answering",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:... | null | Visual Genome enable to model objects and relationships between objects.
They collect dense annotations of objects, attributes, and relationships within each image.
Specifically, the dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between obj... | @article{Krishna2016VisualGC,
title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations},
author={Ranjay Krishna and Yuke Zhu and Oliver Groth and Justin Johnson and Kenji Hata and Joshua Kravitz and Stephanie Chen and Yannis Kalantidis and Li-Jia Li and David A. Shamma and Mic... | null | 29 | 1,278 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-to-text
- object-detection
- visual-question-answering
task_ids:
- image-captioning
paperswithcode_id: visual-... |
big_patent | 2023-06-01T14:59:54.000Z | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"patent-summariz... | null | BIGPATENT, consisting of 1.3 million records of U.S. patent documents
along with human written abstractive summaries.
Each US patent application is filed under a Cooperative Patent Classification
(CPC) code. There are nine such classification categories:
A (Human Necessities), B (Performing Operations; Transporting),
C... | @misc{sharma2019bigpatent,
title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},
author={Eva Sharma and Chen Li and Lu Wang},
year={2019},
eprint={1906.03741},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 24 | 1,276 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: bigpatent
pretty_name: Big Patent
tags... |
bigbio/meqsum | 2022-12-22T15:45:35.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | Dataset for medical question summarization introduced in the ACL 2019 paper "On the Summarization of Consumer Health
Questions". Question understanding is one of the main challenges in question answering. In real world applications,
users often submit natural language questions that are longer than needed and include p... | @inproceedings{ben-abacha-demner-fushman-2019-summarization,
title = "On the Summarization of Consumer Health Questions",
author = "Ben Abacha, Asma and
Demner-Fushman, Dina",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
y... | null | 0 | 1,269 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: MeQSum
homepage: https://github.com/abachaa/MeQSum
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- SUMMARIZATION
---
# Dataset Card for MeQSum
## Dataset Description... |
tyqiangz/multilingual-sentiments | 2023-05-23T15:01:51.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"language:de",
"language:en",
"language:es",
"language:fr",
"langua... | tyqiangz | null | null | null | 18 | 1,255 | ---
language:
- de
- en
- es
- fr
- ja
- zh
- id
- ar
- hi
- it
- ms
- pt
license: apache-2.0
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
- 1M<n<10M
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
---
# Multilingual Sentiments Dataset
A c... |
Joanne/Unified_Benchmark_for_Metaphor_Identification | 2023-03-13T17:32:19.000Z | [
"region:us"
] | Joanne | [Unified Benchmark for Metaphor Identification] | null | null | 0 | 1,254 | Entry not found |
mstz/acute_inflammation | 2023-04-15T11:37:39.000Z | [
"task_categories:tabular-classification",
"size_categories:100<n<1K",
"language:en",
"acute_inflammation",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] | mstz | null | @misc{misc_acute_inflammations_184,
author = {Czerniak,Jacek},
title = {{Acute Inflammations}},
year = {2009},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5V59S}}
} | null | 0 | 1,246 | ---
language:
- en
tags:
- acute_inflammation
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Acute Inflammation
size_categories:
- 100<n<1K
task_categories:
- tabular-classification
configs:
- inflammation
- nephritis
- bladder
---
# Acute Inflammation
The [Acute Inflamm... |
lamini/taylor_swift | 2023-07-24T03:47:45.000Z | [
"region:us"
] | lamini | null | null | null | 1 | 1,223 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 850749.3
num_examples: 783
- name: test
num_by... |
heegyu/hh-rlhf-vicuna-format | 2023-09-06T03:07:11.000Z | [
"region:us"
] | heegyu | null | null | null | 1 | 1,223 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: chosen
struct:
- name: from
dtype: string
- name: value
dtype: string
- name: rejected
struct:
- name: from
dtype: s... |
allenai/mslr2022 | 2022-11-18T21:16:10.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"lang... | allenai | The Multidocument Summarization for Literature Review (MSLR) Shared Task aims to study how medical
evidence from different clinical studies are summarized in literature reviews. Reviews provide the
highest quality of evidence for clinical care, but are expensive to produce manually.
(Semi-)automation via NLP may facili... | @inproceedings{DeYoung2021MS2MS,
title = {MSˆ2: Multi-Document Summarization of Medical Studies},
author = {Jay DeYoung and Iz Beltagy and Madeleine van Zuylen and Bailey Kuehl and Lucy Lu Wang},
booktitle = {EMNLP},
year = {2021}
}
@article{Wallace2020GeneratingN,
title ... | null | 5 | 1,217 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
paperswithcode_id:... |
adv_glue | 2023-06-01T14:57:45.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:sentiment-classification",
"annotations_creators:other",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|glue",
"language:en",
"license:cc... | null | Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark
that focuses on the adversarial robustness evaluation of language models. It covers five
natural language understanding tasks from the famous GLUE tasks and is an adversarial
version of GLUE benchmark. | @article{Wang2021AdversarialGA,
title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},
author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},
journal={ArXiv},
year={2021},
volume={abs/2111.028... | null | 4 | 1,214 | ---
annotations_creators:
- other
language_creators:
- machine-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- natural-language-inference
- sentiment-classification
pretty_name: Ad... |
Babelscape/wikineural | 2022-11-13T07:52:46.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"... | Babelscape | null | null | null | 14 | 1,207 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: wik... |
zxvix/pubmed_subset_new | 2023-08-23T09:04:37.000Z | [
"region:us"
] | zxvix | null | null | null | 0 | 1,202 | ---
dataset_info:
features:
- name: MedlineCitation
struct:
- name: PMID
dtype: int32
- name: DateCompleted
struct:
- name: Year
dtype: int32
- name: Month
dtype: int32
- name: Day
dtype: int32
- name: NumberOfReferences
dtype: int32
- ... |
mteb/banking77 | 2022-09-27T19:15:02.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 0 | 1,200 | ---
language:
- en
--- |
JeanKaddour/minipile | 2023-06-20T10:08:26.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
... | JeanKaddour | null | null | null | 34 | 1,199 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5906108510
num_examples: 1000000
- name: validation
num_bytes: 2779386
num_examples: 500
- name: test
num_bytes: 58558191
num_examples: 10000
download_size: 3177432813
dataset_size: 596744... |
CShorten/ML-ArXiv-Papers | 2022-06-27T12:15:11.000Z | [
"license:afl-3.0",
"region:us"
] | CShorten | null | null | null | 13 | 1,195 | ---
license: afl-3.0
---
This dataset contains the subset of ArXiv papers with the "cs.LG" tag to indicate the paper is about Machine Learning.
The core dataset is filtered from the full ArXiv dataset hosted on Kaggle: https://www.kaggle.com/datasets/Cornell-University/arxiv. The original dataset contains roughly 2 mi... |
gbharti/finance-alpaca | 2023-09-26T04:13:35.000Z | [
"language:en",
"region:us"
] | gbharti | null | null | null | 44 | 1,194 | ---
language:
- en
---
This dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5
Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https... |
spider | 2022-11-03T16:31:49.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"text-to-sql",
... | null | Spider is a large-scale complex and cross-domain semantic parsing and text-toSQL dataset annotated by 11 college students | @article{yu2018spider,
title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and oth... | null | 55 | 1,190 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: spider-1
pretty_name: ... |
squad_kor_v1 | 2023-06-15T15:25:29.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"license:cc-by-nd-4.0",
"arxiv:1909.07005",
"region:us"
] | null | KorQuAD 1.0 is a large-scale Korean dataset for machine reading comprehension task consisting of human generated questions for Wikipedia articles. We benchmark the data collecting process of SQuADv1.0 and crowdsourced 70,000+ question-answer pairs. 1,637 articles and 70,079 pairs of question answers were collected. 1,4... | @article{lim2019korquad1,
title={Korquad1. 0: Korean qa dataset for machine reading comprehension},
author={Lim, Seungyoung and Kim, Myungji and Lee, Jooyoul},
journal={arXiv preprint arXiv:1909.07005},
year={2019}
} | null | 9 | 1,190 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ko
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: korquad
pretty_name: The Korean Question ... |
dalle-mini/YFCC100M_OpenAI_subset | 2021-08-26T17:56:01.000Z | [
"arxiv:1503.01817",
"region:us"
] | dalle-mini | The YFCC100M is one of the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr, all of which were shared under one of the various Creative Commons licenses.
This version is a subset defined in openai/CLIP. | @article{thomee2016yfcc100m,
author = "Bart Thomee and David A. Shamma and Gerald Friedland and Benjamin Elizalde and Karl Ni and Douglas Poland and Damian Borth and Li-Jia Li",
title = "{YFCC100M}: The New Data in Multimedia Research",
journal = "Communications of the {ACM}",
volume = "59",
number = "2",
pages = "64--... | null | 7 | 1,188 | # YFCC100M subset from OpenAI
Subset of [YFCC100M](https://arxiv.org/abs/1503.01817) used by OpenAI for [CLIP](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md), filtered to contain only the images that we could retrieve.
| Split | train | validation |
| --- | --- | --- |
| Number of samples | 14,808,859 | 1... |
nlpai-lab/kullm-v2 | 2023-06-01T05:45:04.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:ko",
"license:apache-2.0",
"region:us"
] | nlpai-lab | null | null | null | 37 | 1,184 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- ko
pretty_name: kullm
size_categories:
- 10K<n<100K
---
# Dataset Card for "KULLM-v2"
## Dataset Summary
Korean translation of GPT4ALL, Dolly, and Vicuna data.
repository: [nlpai-lab/KULLM](https://github.com/nlpai-lab/KULLM)
huggingface: [nlp... |
eduagarcia/portuguese_benchmark | 2023-07-09T06:31:26.000Z | [
"region:us"
] | eduagarcia | null | null | null | 2 | 1,182 | Entry not found |
Tevatron/wikipedia-nq | 2021-11-22T05:32:24.000Z | [
"region:us"
] | Tevatron | null | @inproceedings{karpukhin-etal-2020-dense,
title = "Dense Passage Retrieval for Open-Domain Question Answering",
author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov,
Sergey and Chen, Danqi and Yih, Wen-tau",
booktitle = "Proceedings of the 2020 Conf... | null | 2 | 1,175 | Entry not found |
drop | 2023-04-05T10:05:02.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:extractive-qa",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"langua... | null | DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counti... | @inproceedings{Dua2019DROP,
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
booktitle={Proc. of NAACL},
year={2019}
} | null | 9 | 1,174 | ---
pretty_name: DROP
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text2text-generation
task_ids:
- extractive-qa
- abstractiv... |
tau/sled | 2022-10-25T07:33:44.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:multiple-choice-qa",
"task_ids:natural-language-inference",
"language:en",
"license:mit",
"multi-hop-question-answering",
"query-based-summarization",
"long-texts",
"arxiv:2208.007... | tau | Efficient Long-Text Understanding with Short-Text Models.
Our SLiding-Encoder and Decoder uses any pretrained encoder-decoder model, to independtly encode overlapping chunks of
the inputs, and perform fusion-in-decoder to achieve linear-memory requirment for long-range natural language understanding. | @inproceedings{Ivgi2022EfficientLU,
title={Efficient Long-Text Understanding with Short-Text Models},
author={Maor Ivgi and Uri Shaham and Jonathan Berant},
year={2022}
}
Note that each SLED dataset has its own citation. Please see the source to
get the correct citation for each contained dataset (and also cite t... | null | 3 | 1,172 | ---
language:
- en
license:
- mit
task_categories:
- question-answering
- summarization
- text-generation
task_ids:
- multiple-choice-qa
- natural-language-inference
configs:
- gov_report
- summ_screen_fd
- qmsum
- qasper
- narrative_qa
- quality
- contract_nli
- squad
- squad_shuffled_distractors
- squad_ordered_distr... |
Nexusflow/NexusRaven_API_evaluation | 2023-09-29T05:19:42.000Z | [
"arxiv:2306.05301",
"arxiv:2307.16789",
"region:us"
] | Nexusflow | null | null | null | 3 | 1,170 | ---
dataset_info:
- config_name: outputs_in_toolllm_format
features:
- name: response
list:
- name: function_call
dtype: string
- name: query
dtype: string
- name: task_id
dtype: int64
- name: timestamp
dtype: float64
splits:
- name: train
num_bytes: 303376
nu... |
huggingface/semantic-segmentation-test-sample | 2022-04-11T09:15:24.000Z | [
"region:us"
] | huggingface | null | null | null | 0 | 1,154 | This dataset contains 10 examples of the [segments/sidewalk-semantic](https://huggingface.co/datasets/segments/sidewalk-semantic) dataset (i.e. 10 images with corresponding ground-truth segmentation maps). |
BeIR/fever | 2022-10-23T06:04:31.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 2 | 1,150 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
... |
mteb/sickr-sts | 2022-09-27T19:13:22.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 1 | 1,145 | ---
language:
- en
--- |
nsmc | 2023-01-25T14:41:49.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ko",
"license:cc-by-2.0",
"region:us"
] | null | This is a movie review dataset in the Korean language. Reviews were scraped from Naver movies. The dataset construction is based on the method noted in Large movie review dataset from Maas et al., 2011. | @InProceedings{Park:2016,
title = "Naver Sentiment Movie Corpus",
author = "Lucy Park",
year = "2016",
howpublished = {\\url{https://github.com/e9t/nsmc}}
} | null | 3 | 1,139 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ko
license:
- cc-by-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: nsmc
pretty_name: Naver Sentiment... |
bigbio/biored | 2023-01-12T05:54:49.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"arxiv:2204.04263",
"region:us"
] | bigbio | Relation Extraction corpus with multiple entity types (e.g., gene/protein,
disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical),
on a set of 600 PubMed articles | @article{DBLP:journals/corr/abs-2204-04263,
author = {Ling Luo and
Po{-}Ting Lai and
Chih{-}Hsuan Wei and
Cecilia N. Arighi and
Zhiyong Lu},
title = {BioRED: {A} Comprehensive Biomedical Relation Extraction Dataset},
journal = {CoRR},
volume ... | null | 0 | 1,139 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: BioRED
homepage: https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
---
# Datas... |
dlb/plue | 2022-10-29T12:19:26.000Z | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:found",
"language_creators:machine-generated",
"multiling... | dlb | PLUE: Portuguese Language Understanding Evaluationis a Portuguese translation of
the GLUE benchmark and Scitail using OPUS-MT model and Google Cloud Translation. | @misc{Gomes2020,
author = {GOMES, J. R. S.},
title = {Portuguese Language Understanding Evaluation},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/jubs12/PLUE}},
commit = {CURRENT_COMMIT}
}
@inproceedings{wang2019glue,
title={{GLUE}: A Mult... | null | 6 | 1,138 | ---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- pt
license:
- lgpl-3.0
multilinguality:
- monolingual
- translation
size_categories:
- 10K<n<100K
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-infer... |
pharaouk/dharma-1 | 2023-09-14T23:50:58.000Z | [
"region:us"
] | pharaouk | null | null | null | 19 | 1,135 | ---
configs:
- config_name: default
data_files:
- split: 'dharma_1_full'
path: dharma_1_full*
- split: 'dharma_1_mini'
path: dharma_1_mini*
- split: 'dharma_1_micro'
path: dharma_1_micro*
- split: 'dharma_1_unshuffled'
path: dharma_eval_unshuffled*
---
# "Dharma-1"
A new carefully curated benc... |
HumanCompatibleAI/ppo-seals-CartPole-v0 | 2023-05-29T09:52:49.000Z | [
"region:us"
] | HumanCompatibleAI | null | null | null | 0 | 1,134 | ---
dataset_info:
features:
- name: obs
sequence:
sequence: float32
- name: acts
sequence: int64
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float64
splits:
- name: train
num_bytes: 516313
num_examples: 24
download_size: 297546
... |
Ryan-sjtu/celebahq-caption | 2023-05-26T15:54:04.000Z | [
"license:mit",
"region:us"
] | Ryan-sjtu | null | null | null | 0 | 1,129 | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2756863400.0
num_examples: 30000
download_size: 2762815442
dataset_size: 2756863400.0
---
|
laion/laion400m | 2023-04-04T06:35:23.000Z | [
"license:cc-by-4.0",
"region:us"
] | laion | null | null | null | 17 | 1,127 | ---
license: cc-by-4.0
---
# LAION-400m_new
This datasets has two improvements compared to original LAION_400m dataset:
1. It uses a multilingual text filter to filter out malicious content
2. The better open_clip VitH model was used to detect potential harmful content in the images
All in all, we filtered out arou... |
fujiki/japanese_hh-rlhf-49k | 2023-05-28T06:08:04.000Z | [
"language:ja",
"license:mit",
"region:us"
] | fujiki | null | null | null | 1 | 1,126 | ---
license: mit
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: index
dtype: string
splits:
- name: train
num_bytes: 34168978
num_examples: 49332
download_size: 18427777
dataset_size: 34168978
language:... |
deepset/germanquad | 2023-04-06T13:58:35.000Z | [
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"task_ids:open-domain-qa",
"multilinguality:monolingual",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"arxiv:2104.12741",
"region:us"
] | deepset | In order to raise the bar for non-English QA, we are releasing a high-quality, human-labeled German QA dataset consisting of 13 722 questions, incl. a three-way annotated test set.
The creation of GermanQuAD is inspired by insights from existing datasets as well as our labeling experience from several industry projects... | @misc{möller2021germanquad,
title={GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval},
author={Timo Möller and Julian Risch and Malte Pietsch},
year={2021},
eprint={2104.12741},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 21 | 1,123 | ---
thumbnail: >-
https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
language:
- de
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- question-answering
- text-retrieval
task_ids:
- extractive-qa
- closed-domain-qa
- open-domain-qa
tr... |
mstz/arcene | 2023-04-17T08:46:30.000Z | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"arcene",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] | mstz | null | @misc{misc_arcene_167,
author = {Guyon,Isabelle, Gunn,Steve, Ben-Hur,Asa & Dror,Gideon},
title = {{Arcene}},
year = {2008},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C58P55}}
} | null | 0 | 1,123 | ---
language:
- en
tags:
- arcene
- tabular_classification
- binary_classification
- UCI
pretty_name: Arcene
size_categories:
- n<1K
task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
- tabular-classification
configs:
- arcene
---
# Arcene
The [Arcene datas... |
nampdn-ai/tiny-textbooks | 2023-10-04T03:56:50.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2309.05463",
"arxiv:2306.01116",
"arxiv:2304.08442",
"arxiv:2305.07759",
"doi:10.57967/hf/1126",
"region:us"
] | nampdn-ai | null | null | null | 45 | 1,117 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Textbooks
size_categories:
- 100K<n<1M
license: cc-by-nc-sa-4.0
---
# Textbook-like Dataset: A High-Quality Resource for Small Language Models
The idea is simply inspired by the [Textbooks Are All You Need II: phi-1.5 technical report](https://ar... |
bigbio/ddi_corpus | 2022-12-22T15:44:31.000Z | [
"multilinguality:monolingual",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | bigbio | The DDI corpus has been manually annotated with drugs and pharmacokinetics and pharmacodynamics interactions. It contains 1025 documents from two different sources: DrugBank database and MedLine. | @article{HERREROZAZO2013914,
title = {
The DDI corpus: An annotated corpus with pharmacological substances and
drug-drug interactions
},
author = {
María Herrero-Zazo and Isabel Segura-Bedmar and Paloma Martínez and Thierry
Declerck
},
year = 2013,
journal = {Journa... | null | 1 | 1,113 |
---
language:
- en
bigbio_language:
- English
license: cc-by-nc-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_NC_4p0
pretty_name: DDI Corpus
homepage: https://github.com/isegura/DDICorpus
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
---
... |
argilla/news-summary | 2023-03-16T09:36:12.000Z | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | argilla | null | null | null | 28 | 1,111 | ---
language:
- en
license:
- cc-by-nc-4.0
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
dataset_info:
features:
- name: text
dtype: string
- name: prediction
list:
- name: score
dtype: float64
- name: text
... |
alzoubi36/opp_115 | 2023-06-24T07:08:08.000Z | [
"region:us"
] | alzoubi36 | null | null | null | 0 | 1,110 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
sequence: int64
splits:
- name: train
num_bytes: 1047118
num_examples: 2185
- name: validation
num_bytes: 270827
num_examples: 550
- name: test
num_bytes: 316635
num_examples: 697
download_size: 811600
... |
knowrohit07/know_sql | 2023-09-20T20:13:06.000Z | [
"license:openrail",
"region:us"
] | knowrohit07 | null | null | null | 78 | 1,108 | ---
license: openrail
---
please use the val ign file for training, its much cleaner. thanks :) |
ncbi_disease | 2023-01-25T14:41:18.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed
abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural
language processing community. Each PubMed abstract was manually annotated by two anno... | @article{dougan2014ncbi,
title={NCBI disease corpus: a resource for disease name recognition and concept normalization},
author={Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
journal={Journal of biomedical informatics},
volume={47},
pages={1--10},
year... | null | 18 | 1,107 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: ncbi-disease-1
prett... |
unicamp-dl/mmarco | 2022-11-30T17:31:26.000Z | [
"arxiv:2108.13897",
"arxiv:2105.06813",
"region:us"
] | unicamp-dl | mMARCO translated datasets | @misc{bonifacio2021mmarco,
title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Vitor Jeronymo and Hugo Queiroz Abonizio and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
eprint={2108.13897},
archivePr... | null | 35 | 1,104 | # Dataset Summary
**mMARCO** is a multilingual version of the [MS MARCO passage ranking dataset](https://microsoft.github.io/msmarco/).
For more information, checkout our papers:
* [**mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897)
* [**A cost-benefit ana... |
augtoma/usmle_step_1 | 2023-08-11T21:25:08.000Z | [
"region:us"
] | augtoma | null | null | null | 0 | 1,102 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
... |
cats_vs_dogs | 2023-01-25T14:27:39.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | null | @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
booktitle = {Proceedings of 14th A... | null | 13 | 1,098 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: cats-vs-dogs
prett... |
Graphcore/gqa | 2022-10-25T08:59:27.000Z | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | Graphcore | GQA is a new dataset for real-world visual reasoning and compositional question answering,
seeking to address key shortcomings of previous visual question answering (VQA) datasets. | @inproceedings{hudson2019gqa,
title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
author={Hudson, Drew A and Manning, Christopher D},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={6700--6709},
year={2019}
} | null | 0 | 1,095 | ---
language:
- en
license:
- cc-by-4.0
---
|
cyrilzhang/TinyStories2-ascii-bpe-32k | 2023-09-08T06:00:38.000Z | [
"region:us"
] | cyrilzhang | null | null | null | 0 | 1,095 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 2116666000
num_examples: 516260
- name: validation
num_bytes: 2... |
dongyoung4091/hh-generated_flan_t5_large_flan_t5_zeroshot | 2023-09-08T11:53:45.000Z | [
"region:us"
] | dongyoung4091 | null | null | null | 0 | 1,087 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: zeroshot_helpfulness
dtype: float64
- name: zeroshot_specificity
dtype: float64
- name: zeroshot_intent
dtype: int64
- name: zeroshot_factuality
dtype: int64
- name: zeroshot_easy-to-u... |
cfilt/iitb-english-hindi | 2022-04-26T13:50:22.000Z | [
"region:us"
] | cfilt | null | null | null | 11 | 1,082 | <p align="center"><img src="https://huggingface.co/datasets/cfilt/HiNER-collapsed/raw/main/cfilt-dark-vec.png" alt="Computation for Indian Language Technology Logo" width="150" height="150"/></p>
# IITB-English-Hindi Parallel Corpus
[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-ligh... |
crows_pairs | 2023-07-06T09:23:23.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"bias-evaluation",
"region:us"
] | null | CrowS-Pairs, a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models (MLMs). | @inproceedings{nangia2020crows,
title = "{CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models}",
author = "Nangia, Nikita and
Vania, Clara and
Bhalerao, Rasika and
Bowman, Samuel R.",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods... | null | 3 | 1,078 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: crows-pairs
pretty_name: CrowS-Pairs... |
lucasmccabe-lmi/CodeAlpaca-20k | 2023-05-19T00:10:02.000Z | [
"region:us"
] | lucasmccabe-lmi | null | null | null | 4 | 1,072 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 6576710.0
num_examples: 20022
download_size: 3450938
dataset_size: 6576710.0
---
# Dataset Card for "CodeAlpaca-20k"
We provide a m... |
conceptofmind/cot_submix_original | 2023-04-28T22:57:04.000Z | [
"region:us"
] | conceptofmind | null | null | null | 52 | 1,071 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 209004809
num_examples: 183848
download_size: 100293... |
Critiquers/gsm8k_pairwise | 2023-08-23T19:29:20.000Z | [
"region:us"
] | Critiquers | null | null | null | 1 | 1,063 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: selected
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 411013
num_examples: 512
download_size: 234406
dataset_size: 411013
---
# Dataset Card for "gsm8k_pairwise"
[More Information neede... |
NeelNanda/counterfact-tracing | 2022-11-05T15:19:43.000Z | [
"arxiv:2211.00593",
"region:us"
] | NeelNanda | null | null | null | 5 | 1,062 | ---
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
-... |
speechcolab/gigaspeech | 2023-09-25T17:54:37.000Z | [
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"arxiv:2106.06909",
"region:us"
] | speechcolab | GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks,... | @article{DBLP:journals/corr/abs-2106-06909,
author = {Guoguo Chen and
Shuzhou Chai and
Guanbo Wang and
Jiayu Du and
Wei{-}Qiang Zhang and
Chao Weng and
Dan Su and
Daniel Povey and
Jan Trmal and
... | null | 28 | 1,058 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Gigaspeech
size_categories: []
source_datasets: []
task_categories:
- automatic-speech-recognition
extra_gated_prompt: |-
SpeechColab does not own the copyright of the audio files. For... |
dongyoung4091/shp-generated_flan_t5_large_flan_t5_zeroshot | 2023-09-09T02:42:23.000Z | [
"region:us"
] | dongyoung4091 | null | null | null | 0 | 1,058 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: zeroshot_helpfulness
dtype: float64
- name: zeroshot_specificity
dtype: float64
- name: zeroshot_intent
dtype: float64
- name: zeroshot_factuality
dtype: float64
- name: zeroshot_easy-... |
assin2 | 2023-01-25T14:26:53.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
... | null | The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1.
The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese,
annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5... | @inproceedings{real2020assin,
title={The assin 2 shared task: a quick overview},
author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={406--412},
year={2020},
organization={Springer}
} | null | 9 | 1,057 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- natural-language-inference
- semantic-similarity-scoring
pape... |
hippocrates/re_train | 2023-10-09T16:55:29.000Z | [
"region:us"
] | hippocrates | null | null | null | 0 | 1,056 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype... |
llm-book/wrime-sentiment | 2023-10-06T00:56:38.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:ja",
"region:us"
] | llm-book | null | null | null | 1 | 1,053 | ---
task_categories:
- text-classification
language:
- ja
size_categories:
- 10K<n<100K
---
# Dataset Card for llm-book/wrime-sentiment
日本語の感情分析データセット WRIME を、ポジティブ/ネガティブの二値分類のタスクに加工したデータセットです。
GitHub リポジトリ [ids-cv/wrime](https://github.com/ids-cv/wrime) で公開されているデータセットを利用しています。
`Avg. Readers_Sentiment` の値が0より大きいものをポジティ... |
allenai/lila | 2023-03-15T18:36:28.000Z | [
"license:cc-by-4.0",
"region:us"
] | allenai | Līla is a comprehensive benchmark for mathematical reasoning with over 140K natural language questions annotated with Python programs and natural language instructions. The data set comes with multiple splits: Līla-IID (train, dev, test), Līla-OOD (train, dev, test), and Līla-Robust. | @INPROCEEDINGS{Mishra2022Lila,
author = {
Swaroop Mishra
and Matthew Finlayson
and Pan Lu
and Leonard Tang
and Sean Welleck
and Chitta Baral
and Tanmay Rajpurohit
and Oyvind Tafjord
and Ashish Sabharwal
and Peter Clark
and Ashwin Kalyan},
tit... | null | 15 | 1,050 | ---
license: cc-by-4.0
---
## Dataset Description
- **Repository:** [allenai/lila](https://github.com/allenai/lila)
- **Paper:** [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392.pdf)
- **Point of Contact:** [Matthew Finlayson](https://mattf1n.github.io/), [Sean Welle... |
code_x_glue_cc_defect_detection | 2022-11-18T19:31:11.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"license:c-uda",
"region:us"
] | null | Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
The dataset we use comes from the... | @inproceedings{zhou2019devign,
title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
booktitle={Advances in Neural Information Processing Systems},
pages={101... | null | 5 | 1,046 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: CodeXGlueCcDefectDetection
da... |
argilla/databricks-dolly-15k-curated-multilingual | 2023-06-14T07:47:54.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:es",
"language:de",
"language:fr",
"license:cc-by-sa-3.0",
"machine-translated",
"instruction-following",
"region:us"
] | argilla | null | null | null | 33 | 1,046 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: category
dtype: string
- name: instruction_original_en
dtype: string
- name: context_original_en
dtype: string
- name: response_original_en
dtype... |
Yijia-Xiao/pii-wikidoc_patient_information | 2023-09-12T22:24:25.000Z | [
"region:us"
] | Yijia-Xiao | null | null | null | 2 | 1,046 | ---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
- name: cleaned_output
dtype: string
splits:
- name: train
num_bytes: 6017940
num_examples: 5942
download_size: 2993583
dataset_size: 6017940
configs:
- config... |
climatebert/climate_sentiment | 2023-04-18T14:37:00.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | climatebert | null | null | null | 1 | 1,037 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: ClimateSentiment
dataset_info:
... |
cbt | 2023-06-01T14:59:53.000Z | [
"task_categories:other",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"licen... | null | The Children’s Book Test (CBT) is designed to measure directly
how well language models can exploit wider linguistic context.
The CBT is built from books that are freely available. | @misc{hill2016goldilocks,
title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations},
author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston},
year={2016},
eprint={1511.02301},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 9 | 1,033 | ---
pretty_name: Children’s Book Test (CBT)
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- gfdl
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- n<1K
source_datasets:
- original
task_categories:
- other
- question-answering
task_ids:
- multiple-choice-qa
pape... |
dbrd | 2023-01-25T14:29:14.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"si... | null | The Dutch Book Review Dataset (DBRD) contains over 110k book reviews of which 22k have associated binary sentiment polarity labels. It is intended as a benchmark for sentiment classification in Dutch and created due to a lack of annotated datasets in Dutch that are suitable for this task. | @article{DBLP:journals/corr/abs-1910-00896,
author = {Benjamin van der Burgh and
Suzan Verberne},
title = {The merits of Universal Language Model Fine-tuning for Small Datasets
- a case with Dutch book reviews},
journal = {CoRR},
volume = {abs/1910.00896},
year =... | null | 3 | 1,032 | ---
annotations_creators:
- found
language_creators:
- found
language:
- nl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- language-modeling
- masked-language-modeling
- s... |
zeroshot/twitter-financial-news-sentiment | 2022-12-12T14:32:59.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"twitter",
"finance",
"markets",
"stoc... | zeroshot | null | null | null | 28 | 1,017 | ---
annotations_creators:
- other
language:
- en
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: twitter financial news
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- twitter
- finance
- markets
- stocks
- wallstreet
- quant
- hedgefunds
- markets
task_categories... |
assin | 2023-01-25T14:26:50.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
... | null | The ASSIN (Avaliação de Similaridade Semântica e INferência textual) corpus is a corpus annotated with pairs of sentences written in
Portuguese that is suitable for the exploration of textual entailment and paraphrasing classifiers. The corpus contains pairs of sentences
extracted from news articles written in Europea... | @inproceedings{fonseca2016assin,
title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
pages={13--15},
yea... | null | 8 | 1,015 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- natural-language-inference
- semantic-similarity-scoring
pa... |
mteb/amazon_reviews_multi | 2022-09-27T19:10:01.000Z | [
"language:de",
"language:en",
"language:es",
"language:fr",
"language:ja",
"language:zh",
"region:us"
] | mteb | We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an a... | @inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
} | null | 3 | 1,011 | ---
language:
- de
- en
- es
- fr
- ja
- zh
--- |
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