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 |
|---|---|---|---|---|---|---|---|---|---|
FreedomIntelligence/alpaca-gpt4-korean | 2023-08-06T08:10:43.000Z | [
"region:us"
] | FreedomIntelligence | null | null | null | 1 | 653 | The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT). |
masakhaner | 2023-06-01T14:59:56.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:am",
"language:ha",
"language:ig",
"lang... | null | MasakhaNER is the first large publicly available high-quality dataset for named entity recognition (NER) in ten 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 wit... | @article{Adelani2021MasakhaNERNE,
title={MasakhaNER: Named Entity Recognition for African Languages},
author={D. Adelani and Jade Abbott and Graham Neubig and Daniel D'Souza and Julia Kreutzer and Constantine Lignos
and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen May... | null | 4 | 651 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- am
- ha
- ig
- lg
- luo
- pcm
- rw
- sw
- wo
- yo
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-r... |
regisss/librispeech_asr_for_optimum_habana_ci | 2023-09-10T19:40:47.000Z | [
"license:cc-by-4.0",
"region:us"
] | regisss | LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.87 | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--... | null | 0 | 645 | ---
license: cc-by-4.0
---
This dataset contains the splits `clean.train.100` and `clean.dev` of the [LibriSpeech dataset](https://huggingface.co/datasets/librispeech_asr).
It is only meant to be used in Optimum Habana's CI to avoid downloading other splits.
|
result-kand2-sdxl-wuerst-karlo/36e1d427 | 2023-09-19T14:17:01.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 645 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 232
num_examples: 10
download_size: 1385
dataset_size: 232
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "36e1d42... |
transformersbook/codeparrot-train | 2022-02-05T16:23:03.000Z | [
"region:us"
] | transformersbook | null | null | null | 3 | 644 | # CodeParrot Dataset
This is the train split of the CodeParrot dataset. It contains Python files used to train the code generation model in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can ... |
KATANABRAVE/stories | 2023-08-25T06:37:13.000Z | [
"license:llama2",
"region:us"
] | KATANABRAVE | null | null | null | 0 | 643 | ---
license: llama2
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: title
dtype: string
- name: article
dtype: string
- name: text
dtype: string
- name: input_ids
sequence: i... |
cuad | 2022-11-18T19:50:02.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:210... | null | Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions. | @article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
} | null | 28 | 638 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: cuad
pretty_name: CUA... |
argilla/agnews_weak_labeling | 2023-07-13T11:46:28.000Z | [
"language:en",
"region:us"
] | argilla | null | null | null | 0 | 638 | ---
language: en
dataset_info:
features:
- name: text
dtype: string
- name: inputs
struct:
- name: text
dtype: string
- name: prediction
dtype: 'null'
- name: prediction_agent
dtype: 'null'
- name: annotation
dtype: string
- name: annotation_agent
dtype: 'null'
- name: ... |
conceptofmind/flan2021_submix_original | 2023-05-09T23:31:13.000Z | [
"region:us"
] | conceptofmind | null | null | null | 33 | 638 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
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dtype: string
splits:
- name: train
num_bytes: 8988026240
num_examples: 5362361
download_size: 5486... |
polinaeterna/amazon_us_reviews | 2023-06-09T17:56:17.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"ta... | polinaeterna | Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website... | \ | null | 0 | 636 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
- text-classification
task_ids:
- text-scoring
- language-modeling
-... |
cedr | 2023-01-25T14:27:50.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ru",
"license:apache-2... | null | This new dataset is designed to solve emotion recognition task for text data in Russian. The Corpus for Emotions Detecting in
Russian-language text sentences of different social sources (CEDR) contains 9410 sentences in Russian labeled for 5 emotion
categories. The data collected from different sources: posts of the Li... | @article{sboev2021data,
title={Data-Driven Model for Emotion Detection in Russian Texts},
author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman},
journal={Procedia Computer Science},
volume={190},
pages={637--642},
year={2021},
publisher={Elsevier}
} | null | 4 | 635 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ru
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- multi-label-classification
pretty_name: The Corpus... |
seungheondoh/LP-MusicCaps-MTT | 2023-08-04T10:39:28.000Z | [
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"art",
"music",
"text-to-music",
"music-to-text",
"arxiv:2307.16372",
"region:us"
] | seungheondoh | null | null | null | 1 | 635 | ---
license: mit
language:
- en
tags:
- art
- music
- text-to-music
- music-to-text
pretty_name: LP-MusicCaps-MTT
size_categories:
- 10K<n<100K
---
======================================
**!important**: Be careful when using `caption_attribute_prediction` (We don't recommend to use)!
================================... |
Siddharth63/biological_dataset | 2023-09-11T14:01:11.000Z | [
"license:other",
"region:us"
] | Siddharth63 | null | null | null | 0 | 634 | ---
license: other
dataset_info:
features:
- name: index
dtype: string
- name: text
dtype: string
- name: doi
dtype: string
splits:
- name: train
num_bytes: 30985524742.471012
num_examples: 22538431
- name: validation
num_bytes: 3442837304.52899
num_examples: 2504271
download... |
TREC-AToMiC/AToMiC-Images-v0.2 | 2023-02-14T21:29:39.000Z | [
"size_categories:100M<n<1B",
"license:cc-by-sa-4.0",
"arxiv:2103.01913",
"region:us"
] | TREC-AToMiC | null | null | null | 1 | 633 | ---
dataset_info:
features:
- name: image_url
dtype: string
- name: image_id
dtype: string
- name: language
sequence: string
- name: caption_reference_description
sequence: string
- name: caption_alt_text_description
sequence: string
- name: caption_attribution_description
sequence... |
JonasGeiping/the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020 | 2023-06-13T16:25:54.000Z | [
"arxiv:2212.14034",
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] | JonasGeiping | null | null | null | 0 | 631 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 43860000000
num_examples: 85000000
download_size: 24001057282
dataset_size: 43860000000
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
mu... |
castorini/mr-tydi | 2022-10-12T20:25:19.000Z | [
"task_categories:text-retrieval",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:fi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"license:apache-2.0",
"region:us"
] | castorini | null | null | null | 9 | 630 | ---
language:
- ar
- bn
- en
- fi
- id
- fi
- ja
- ko
- ru
- sw
- te
- th
multilinguality:
- multilingual
task_categories:
- text-retrieval
license: apache-2.0
---
# Dataset Summary
Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse l... |
conceptofmind/niv2_submix_original | 2023-04-29T00:58:20.000Z | [
"region:us"
] | conceptofmind | null | null | null | 18 | 630 | ---
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: 13104211362
num_examples: 10066896
download_size: 76... |
yzhuang/autotree_automl_10000_electricity_sgosdt_l256_dim7_d3_sd0 | 2023-09-07T02:45:46.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 630 | ---
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... |
hyperpartisan_news_detection | 2023-06-13T07:46:19.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"bias-classification",
"regio... | null | Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.
Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.
There are 2 parts:
- byarticle: Labeled t... | @inproceedings{kiesel-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
author = "Kiesel, Johannes and
Mestre, Maria and
Shukla, Rishabh and
Vincent, Emmanuel and
Adineh, Payam and
Corney, David and
Stein, Benno and
Potthast, Mar... | null | 8 | 629 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: HyperpartisanNewsDetection
tags:
- bias-class... |
lamini/lamini_docs_evaluation | 2023-07-24T03:08:13.000Z | [
"region:us"
] | lamini | null | null | null | 0 | 629 | ---
dataset_info:
features:
- name: predicted_answer
dtype: string
- name: target_answer
dtype: string
splits:
- name: train
num_bytes: 744520
num_examples: 139
download_size: 86086
dataset_size: 744520
---
# Dataset Card for "lamini_docs_evaluation"
[More Information needed](https://gith... |
HuggingFaceM4/SEED | 2023-08-23T13:32:09.000Z | [
"region:us"
] | HuggingFaceM4 | null | null | null | 2 | 626 | ---
configs:
- config_name: Instance_Attributes
data_files:
- split: test
path: Instance_Attributes/test-*
- config_name: Instance_Identity
data_files:
- split: test
path: Instance_Identity/test-*
- config_name: Instance_Interaction
data_files:
- split: test
path: Instance_Interaction/test-*
- c... |
pg19 | 2023-07-28T09:21:25.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1911.05507",
"regio... | null | This repository contains the PG-19 language modeling benchmark.
It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919.
It also contains metadata of book titles and publication dates.
PG-19 is over double the size of the Billion Word benchmark and contains docume... | @article{raecompressive2019,
author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and
Hillier, Chloe and Lillicrap, Timothy P},
title = {Compressive Transformers for Long-Range Sequence Modelling},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/1911.05507},
year = {2019},
... | null | 23 | 625 | ---
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:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: pg-19
pretty_name: PG-19
da... |
phiyodr/coco2017 | 2023-06-26T11:40:47.000Z | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"size_categories:100K<n<1M",
"language:en",
"coco",
"image-captioning",
"region:us"
] | phiyodr | null | null | null | 0 | 619 | ---
language:
- en
pretty_name: COCO2017
size_categories:
- 100K<n<1M
task_categories:
- image-to-text
task_ids:
- image-captioning
tags:
- coco
- image-captioning
dataset_info:
features:
- name: license
dtype: int64
- name: file_name
dtype: string
- name: coco_url
dtype: string
- name: height
... |
nahyeon00/mixsnips_clean | 2023-07-19T08:38:38.000Z | [
"region:us"
] | nahyeon00 | null | null | null | 0 | 619 | ---
dataset_info:
features:
- name: token
sequence: string
- name: tag
sequence: string
- name: intent
sequence: string
splits:
- name: train
num_bytes: 16319528
num_examples: 39776
- name: validation
num_bytes: 915087
num_examples: 2198
- name: test
num_bytes: 902367
... |
BeIR/dbpedia-entity | 2022-10-23T06:03:56.000Z | [
"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 | 617 | ---
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:
... |
facat/sci-llm-new-512 | 2023-09-15T06:31:11.000Z | [
"region:us"
] | facat | null | null | null | 0 | 617 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: train_attack
path: data/train_attack-*
- split: train_old
path: data/train_old-*
- split: train_new
path: data/train_new-*
- split: test
path: data/test-*
- split: test2
path: data/test2-*
da... |
result-kand2-sdxl-wuerst-karlo/a77d2949 | 2023-09-20T09:17:02.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 616 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1322
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a77d294... |
spacemanidol/dset-corpus | 2023-09-27T19:17:42.000Z | [
"region:us"
] | spacemanidol | null | null | 0 | 616 | Entry not found | |
huggan/pokemon | 2022-04-01T11:50:45.000Z | [
"region:us"
] | huggan | null | null | null | 13 | 615 | Source: https://www.kaggle.com/datasets/djilax/pkmn-image-dataset |
WizardLM/WizardLM_evol_instruct_V2_196k | 2023-08-24T03:55:18.000Z | [
"arxiv:2308.09583",
"arxiv:2304.12244",
"arxiv:2306.08568",
"region:us"
] | WizardLM | null | null | null | 141 | 615 |
## News
- 🔥 🔥 🔥 [08/11/2023] We release **WizardMath** Models.
- 🔥 Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**.
- 🔥 Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchm... |
cyrilzhang/TinyStories2-ascii-val-1k | 2023-09-27T12:44:27.000Z | [
"region:us"
] | cyrilzhang | null | null | null | 0 | 613 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: validation
num_bytes: 793968
num_examples: 1000
download_size: 410730
dataset_size: 793968
---
# Dataset Card for "TinyStories2-a... |
nightingal3/fig-qa | 2023-06-10T18:13:33.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
... | nightingal3 | null | null | null | 2 | 609 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Fig-QA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
---
# Dataset Card f... |
mrqa | 2022-11-18T21:30:01.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|drop",
"source_datasets:extended|hotpot_qa",
"source_datasets:extended|natural_questions",
... | null | The MRQA 2019 Shared Task focuses on generalization in question answering.
An effective question answering system should do more than merely
interpolate from the training set to answer test examples drawn
from the same distribution: it should also be able to extrapolate
to out-of-distribution examples — a significantly... | @inproceedings{fisch2019mrqa,
title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension},
author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen},
booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNL... | null | 8 | 607 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|drop
- extended|hotpot_qa
- extended|natural_questions
- extended|race
- extended|search_qa
- extended|squad
- extended|trivia_qa
task_ca... |
augtoma/medqa_usmle | 2023-08-11T20:50:07.000Z | [
"region:us"
] | augtoma | null | null | null | 0 | 607 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: str... |
brwac | 2022-11-03T16:16:00.000Z | [
"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:pt",
... | null | The BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework,
which was made public for research purposes. The current corpus version, released in January 2017, is composed by
3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource... | @inproceedings{wagner2018brwac,
title={The brwac corpus: A new open resource for brazilian portuguese},
author={Wagner Filho, Jorge A and Wilkens, Rodrigo and Idiart, Marco and Villavicencio, Aline},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},... | null | 7 | 604 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: brwac
p... |
emo | 2023-04-05T10:05:14.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. | @inproceedings{chatterjee-etal-2019-semeval,
title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text},
author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal},
booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
year={20... | null | 3 | 603 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: emocontext
pretty_name:... |
lewtun/github-issues | 2021-10-04T15:49:55.000Z | [
"arxiv:2005.00614",
"region:us"
] | lewtun | null | null | null | 4 | 602 | # Dataset Card for GitHub Issues
## Dataset Description
- **Point of Contact:** [Lewis Tunstall](lewis@huggingface.co)
### Dataset Summary
GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets [repository](https://github.com/huggingface/datasets). It is intended fo... |
squad_kor_v2 | 2023-02-07T14:40:49.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|squad_kor_v1",
"source_datasets:original",
"language:ko",
"license:cc-by-nd-4.0",
... | null | KorQuAD 2.0 is a Korean question and answering dataset consisting of a total of 100,000+ pairs. There are three major differences from KorQuAD 1.0, which is the standard Korean Q & A data. The first is that a given document is a whole Wikipedia page, not just one or two paragraphs. Second, because the document also con... | @article{NODE09353166,
author={Youngmin Kim,Seungyoung Lim;Hyunjeong Lee;Soyoon Park;Myungji Kim},
title={{KorQuAD 2.0: Korean QA Dataset for Web Document Machine Comprehension}},
booltitle={{Journal of KIISE 제47권 제6호}},
journal={{Journal of KIISE}},
volume={{47}},
issue={{6}},
publisher={Th... | null | 2 | 601 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ko
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad_kor_v1
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name:... |
mteb/emotion | 2022-09-27T19:14:18.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 5 | 601 | ---
language:
- en
---
** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)** |
flaviagiammarino/vqa-rad | 2023-06-03T18:38:48.000Z | [
"task_categories:visual-question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"medical",
"region:us"
] | flaviagiammarino | null | null | null | 5 | 600 | ---
license: cc0-1.0
task_categories:
- visual-question-answering
language:
- en
paperswithcode_id: vqa-rad
tags:
- medical
pretty_name: VQA-RAD
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: answer
dtype: string
splits:
- na... |
SetFit/CR | 2022-06-21T09:04:33.000Z | [
"region:us"
] | SetFit | null | null | null | 0 | 598 | # Customer Reviews
This dataset is a port of the official [`CR` dataset](https://github.com/hiyouga/Dual-Contrastive-Learning/tree/main/data) from [this paper](https://www.cs.uic.edu/~liub/FBS/opinion-mining-final-WSDM.pdf).
There is no validation split. |
result-kand2-sdxl-wuerst-karlo/df2d5286 | 2023-09-20T21:15:39.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 598 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 215
num_examples: 10
download_size: 1374
dataset_size: 215
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "df2d528... |
result-kand2-sdxl-wuerst-karlo/9cc99eaf | 2023-09-20T21:15:42.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 597 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 215
num_examples: 10
download_size: 1374
dataset_size: 215
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "9cc99ea... |
LeoCordoba/CC-NEWS-ES | 2023-02-23T21:53:55.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"size_categories:1M<n<1... | LeoCordoba | null | null | 6 | 596 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- es
license:
- mit
multilinguality:
- monolingual
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
- 1M<n<10M
source_datasets:
- cc-news
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-gen... | |
Multimodal-Fatima/FGVC_Aircraft_train | 2023-05-04T05:30:31.000Z | [
"region:us"
] | Multimodal-Fatima | null | null | null | 0 | 596 | ---
dataset_info:
features:
- name: image
dtype: image
- name: family
dtype:
class_label:
names:
'0': A300
'1': A310
'2': A320
'3': A330
'4': A340
'5': A380
'6': ATR-42
'7': ATR-72
'8': An-12
... |
nampdn-ai/tiny-codes | 2023-09-30T04:14:36.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"arxiv:2306.11644",
"arxiv:2305.07759",
"doi:10.57967/hf/0937",
"region:us"
] | nampdn-ai | null | null | null | 125 | 596 | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Codes
size_categories:
- 1M<n<10M
---
# Reasoning with Language and Code
This synthetic dataset is a collection of **1.6 millions short and clear code snippets** that can help LLM models learn how to reason with both natural and progr... |
mteb/stackexchange-clustering | 2022-09-27T19:11:56.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 0 | 595 | ---
language:
- en
--- |
McGill-NLP/TopiOCQA | 2023-09-29T19:37:48.000Z | [
"task_categories:text-retrieval",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:en",
"license:cc-by-nc-sa-4.0",
"conversational-question-answeri... | McGill-NLP | TopiOCQA is an information-seeking conversational dataset with challenging topic switching phenomena. | null | null | 4 | 594 | ---
annotations_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- text-retrieval
- text-generation
task_ids:
- language-modeling
- open-domain-qa
pretty_name: Open-domain Conversational Question Answering with Topic Switchi... |
result-kand2-sdxl-wuerst-karlo/6845e847 | 2023-09-20T22:33:18.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 594 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 211
num_examples: 10
download_size: 1393
dataset_size: 211
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6845e84... |
Multimodal-Fatima/SNLI-VE_train | 2023-02-07T23:21:35.000Z | [
"region:us"
] | Multimodal-Fatima | null | null | null | 1 | 593 | ---
dataset_info:
features:
- name: image
dtype: image
- name: filename
dtype: string
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- na... |
eduagarcia/cnj_benchmarks | 2023-07-17T03:23:48.000Z | [
"region:us"
] | eduagarcia | null | null | null | 0 | 593 | Entry not found |
orgcatorg/multilingual | 2023-10-03T13:32:28.000Z | [
"region:us"
] | orgcatorg | null | null | null | 0 | 593 | ---
dataset_info:
- config_name: eng_Latn-lao_Laoo
features:
- name: translation
struct:
- name: eng_Latn
dtype: string
- name: lao_Laoo
dtype: string
splits:
- name: train
num_bytes: 42871606
num_examples: 140265
download_size: 23468883
dataset_size: 42871606
- config_name: ... |
JayalekshmiGopakumar/DocLayexp1 | 2023-08-30T13:25:15.000Z | [
"region:us"
] | JayalekshmiGopakumar | null | null | null | 0 | 591 | ---
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: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': financi... |
Riksarkivet/test_images_demo | 2023-08-31T13:58:13.000Z | [
"task_categories:image-to-text",
"language:sv",
"HTR",
"region:us"
] | Riksarkivet | Demo dataset for the htr demo. | @InProceedings{huggingface:dataset,
title = {Small htr examples images},
author={Gabriel Borg},
year={2023}
} | null | 1 | 590 | ---
language:
- sv
tags:
- HTR
task_categories:
- image-to-text
---
# Information
This is a demo dataset contains images from the Swedish National Archives, Riksarkivet.
To find the images at Riksarkivet:
30002030_00003.jpg = https://sok.riksarkivet.se/bildvisning/30002030_00003
| Image_name | Description |
|---|... |
mteb/summeval | 2022-09-27T19:14:10.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 1 | 589 | ---
language:
- en
---
# SummEval
The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total).
Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total).
Summaries were evaluated across 4 dimensions: co... |
eraser_multi_rc | 2023-04-05T10:05:21.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | Eraser Multi RC is a dataset for queries over multi-line passages, along with
answers and a rationalte. Each example in this dataset has the following 5 parts
1. A Mutli-line Passage
2. A Query about the passage
3. An Answer to the query
4. A Classification as to whether the answer is right or wrong
5. An Explanation j... | @unpublished{eraser2019,
title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
}
@inproceedings{MultiRC2018,
author = {Daniel Khashabi and Snigdha Chaturve... | null | 3 | 588 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
pretty_name: Eraser MultiRC (Multi-Sentence Reading Comprehension... |
snips_built_in_intents | 2023-01-25T14:44:32.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"arxiv:1805.10190",
"region... | null | Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at
https://github.com/sonos/nlu-benchmark 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. The
related paper mentioned on the github page is https://arxiv... | @article{DBLP:journals/corr/abs-1805-10190,
author = {Alice Coucke and
Alaa Saade and
Adrien Ball and
Th{\'{e}}odore Bluche and
Alexandre Caulier and
David Leroy and
Cl{\'{e}}ment Doumouro and
Thibault Gisselbr... | null | 4 | 587 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: snips
pretty_name: SNIPS Nat... |
lmsys/lmsys-chat-1m | 2023-10-04T17:40:32.000Z | [
"task_categories:conversational",
"size_categories:1M<n<10M",
"arxiv:2309.11998",
"region:us"
] | lmsys | null | null | null | 220 | 587 | ---
size_categories:
- 1M<n<10M
task_categories:
- conversational
extra_gated_prompt: You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement).
extra_gated_fields:
Name: text
Email: text
Affiliation: text
Country: tex... |
silicone | 2023-06-01T14:59:53.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:expert-generated... | null | The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection
of resources for training, evaluating, and analyzing natural language understanding systems
specifically designed for spoken language. All datasets are in the English language and cover a
variety of domains including... | @inproceedings{chapuis-etal-2020-hierarchical,
title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog",
author = "Chapuis, Emile and
Colombo, Pierre and
Manica, Matteo and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
booktitle = "Findings of the Association for Co... | null | 7 | 585 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- dialo... |
result-kand2-sdxl-wuerst-karlo/ce65a06b | 2023-09-21T02:55:46.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 585 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 187
num_examples: 10
download_size: 1357
dataset_size: 187
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ce65a06... |
mteb/arxiv-clustering-s2s | 2022-09-27T19:12:49.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 0 | 581 | ---
language:
- en
--- |
HumanCompatibleAI/ppo-CartPole-v1 | 2023-07-18T14:43:49.000Z | [
"region:us"
] | HumanCompatibleAI | null | null | null | 0 | 578 | ---
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: 2103613
num_examples: 100
download_size: 126383... |
hippocrates/qa_train | 2023-10-03T03:42:29.000Z | [
"region:us"
] | hippocrates | null | null | null | 0 | 576 | ---
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... |
stas/oscar-en-10k | 2022-10-19T21:40:14.000Z | [
"language:en",
"license:apache-2.0",
"region:us"
] | stas | This is a small subset representing 10K records from the original OSCAR dataset, "unshuffled_deduplicated_en" subset - created for testing. The records were extracted after having been shuffled.
The full 1TB+ dataset is at https://huggingface.co/datasets/oscar. | @inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Co... | null | 2 | 575 | ---
language:
- en
license: apache-2.0
---
# OSCAR EN 10K for testing
This is a small subset representing the 10K records from the original OSCAR dataset, "unshuffled_deduplicated_en" subset - created for testing. The records were extracted after having been shuffled.
The full 1TB+ dataset is at https://huggingfac... |
HuggingFaceM4/FairFace | 2022-12-09T00:14:46.000Z | [
"license:cc-by-4.0",
"region:us"
] | HuggingFaceM4 | FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino.
Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups. | @inproceedings{karkkainenfairface,
title={FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation},
author={Karkkainen, Kimmo and Joo, Jungseock},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
year={2021},
... | null | 5 | 575 | ---
license: cc-by-4.0
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Data... |
C-MTEB/DuRetrieval | 2023-07-28T09:48:49.000Z | [
"region:us"
] | C-MTEB | null | null | null | 0 | 574 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 91213303
num_examples: 100001
- name: queries
... |
shmuhammad/AfriSenti-twitter-sentiment | 2023-09-03T09:59:15.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categor... | shmuhammad | AfriSenti is the largest sentiment analysis benchmark dataset for under-represented African languages---covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and yo... | @inproceedings{muhammad-etal-2023-semeval,
title="{S}em{E}val-2023 Task 12: Sentiment Analysis for African Languages ({A}fri{S}enti-{S}em{E}val)",
author="Muhammad, Shamsuddeen Hassan and
Yimam, Seid and
Abdulmumin, Idris and
Ahmad, Ibrahim Sa'id and
Ousidhoum, Nedjma, and
Ayele, Abinew, and
... | null | 3 | 572 | ---
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
- sentiment-scoring
- semantic-similarity-classification
- semantic-similarity-scoring
tags:
- sentiment analysis, Twitter, tweets
- sentiment
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
l... |
alespalla/chatbot_instruction_prompts | 2023-03-21T13:36:36.000Z | [
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | alespalla | null | null | null | 23 | 572 | ---
license: apache-2.0
dataset_info:
features:
- name: response
dtype: string
- name: prompt
dtype: string
splits:
- name: test
num_bytes: 24612503
num_examples: 64511
- name: train
num_bytes: 98485829
num_examples: 258042
download_size: 78591384
dataset_size: 123098332
task_cat... |
alzoubi36/policy_detection | 2023-06-24T06:26:17.000Z | [
"region:us"
] | alzoubi36 | null | null | null | 0 | 570 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 8258295
num_examples: 773
- name: validation
num_bytes: 1340647
num_examples: 137
- name: test
num_bytes: 3702713
num_examples: 391
download_size: 6887636
... |
alexandrainst/audio_test_dataset | 2023-05-01T14:28:58.000Z | [
"size_categories:n<1K",
"language:da",
"license:cc0-1.0",
"region:us"
] | alexandrainst | null | null | null | 0 | 569 | ---
dataset_info:
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- nam... |
BeIR/fever-qrels | 2022-10-23T06:08:11.000Z | [
"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 | 0 | 568 | ---
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:
... |
pinecone/core-2020-05-10-deduplication | 2022-10-28T03:01:02.000Z | [
"task_categories:other",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:unknown",
"language:en",
"lic... | pinecone | null | null | null | 1 | 566 | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- unknown
task_categories:
- other
task_ids:
- natural-language-inference
- semantic-similarity-scoring
- text-scoring
pretty_name: CORE Deduplicati... |
C-MTEB/DuRetrieval-qrels | 2023-07-28T09:48:53.000Z | [
"region:us"
] | C-MTEB | null | null | null | 0 | 566 | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 787120
num_examples: 9839
download_size: 420443
dataset_size: 78... |
md_gender_bias | 2023-06-01T14:59:54.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
... | null | Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of th... | @inproceedings{md_gender_bias,
author = {Emily Dinan and
Angela Fan and
Ledell Wu and
Jason Weston and
Douwe Kiela and
Adina Williams},
editor = {Bonnie Webber and
Trevor Cohn and
Yulan He and
... | null | 13 | 565 | ---
annotations_creators:
- crowdsourced
- found
- machine-generated
language_creators:
- crowdsourced
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
- n<1K
source_datasets:
- extended|other-convai2
- extended|other-light
- extended|o... |
plaguss/snli-small | 2023-09-10T14:53:06.000Z | [
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | plaguss | null | null | null | 0 | 563 | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for snli-small
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly ... |
embedding-data/sentence-compression | 2022-08-02T03:02:47.000Z | [
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-classification",
"language:en",
"license:mit",
"region:us"
] | embedding-data | null | null | null | 10 | 562 | ---
license: mit
language:
- en
paperswithcode_id: embedding-data/sentence-compression
pretty_name: sentence-compression
task_categories:
- sentence-similarity
- paraphrase-mining
task_ids:
- semantic-similarity-classification
---
# Dataset Card for "sentence-compression"
## Table of Contents
- [Dataset Description]... |
jigsaw_unintended_bias | 2023-01-25T14:33:20.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"toxicity-prediction",
"region:us"
] | null | A collection of comments from the defunct Civil Comments platform that have been annotated for their toxicity. | null | null | 2 | 561 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
pretty_name: Jigsaw Unintended Bias in Toxicity Classificati... |
ehartford/wizard_vicuna_70k_unfiltered | 2023-05-16T00:43:23.000Z | [
"license:apache-2.0",
"region:us"
] | ehartford | null | null | null | 97 | 561 | ---
license: apache-2.0
---
This dataset is the wizard_vicuna dataset junelee/wizard_vicuna_70k, removing conversations with alignment.
34598 conversations remain.
inspired by https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered
All credit to anon8231489123 I basically took his scripts and appli... |
BeIR/nq | 2022-10-23T06:02:24.000Z | [
"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 | 560 | ---
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:
... |
Yijia-Xiao/pii-wikidoc | 2023-09-12T22:24:36.000Z | [
"region:us"
] | Yijia-Xiao | null | null | null | 1 | 560 | ---
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
- name: cleaned_output
dtype: string
splits:
- name: train
num_bytes: 19486545
num_examples: 10000
download_size: 10662804
dataset_size: 19486545
configs:
- co... |
IlyaGusev/gazeta | 2023-02-12T00:01:45.000Z | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ru",
"license:unknown",
"arx... | IlyaGusev | null | @InProceedings{10.1007/978-3-030-59082-6_9,
author="Gusev, Ilya",
editor="Filchenkov, Andrey and Kauttonen, Janne and Pivovarova, Lidia",
title="Dataset for Automatic Summarization of Russian News",
booktitle="Artificial Intelligence and Natural Language",
year="2020",
publisher="Springer Intern... | null | 13 | 559 | ---
annotations_creators:
- expert-generated
- found
language_creators:
- expert-generated
- found
task_categories:
- summarization
language:
- ru
size_categories:
- 10K<n<100K
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
paperswithcode_id: gazeta
---
# Dataset Card for Gazeta
## Tabl... |
result-kand2-sdxl-wuerst-karlo/04554133 | 2023-09-21T19:46:51.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 558 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 167
num_examples: 10
download_size: 1328
dataset_size: 167
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "0455413... |
embedding-data/QQP_triplets | 2022-08-02T03:14:14.000Z | [
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-classification",
"language:en",
"license:mit",
"region:us"
] | embedding-data | null | null | null | 3 | 554 | ---
license: mit
language:
- en
paperswithcode_id: embedding-data/QQP_triplets
pretty_name: QQP_triplets
task_categories:
- sentence-similarity
- paraphrase-mining
task_ids:
- semantic-similarity-classification
---
# Dataset Card for "QQP_triplets"
## Table of Contents
- [Dataset Description](#dataset-description)
... |
cardiffnlp/tweet_topic_multi | 2022-11-27T11:26:34.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:1k<10K",
"language:en",
"license:other",
"arxiv:2209.09824",
"region:us"
] | cardiffnlp | [TweetTopic](https://arxiv.org/abs/2209.09824) | @inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Co... | null | 8 | 554 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1k<10K
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: TweetTopicSingle
---
# Dataset Card for "cardiffnlp/tweet_topic_multi"
## Dataset Description
- **Paper:** [https://arxiv.org/abs/2209.... |
llm-book/aio-retriever | 2023-07-04T04:56:01.000Z | [
"size_categories:10K<n<100K",
"language:ja",
"region:us"
] | llm-book | null | null | null | 0 | 554 | ---
language:
- ja
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: qid
dtype: string
- name: competition
dtype: string
- name: timestamp
dtype: string
- name: section
dtype: string
- name: number
dtype: string
- name: original_question
dtype: string
- name: original... |
cyrilzhang/wiki-bpe-32k | 2023-09-22T16:02:48.000Z | [
"region:us"
] | cyrilzhang | null | null | null | 0 | 554 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 21123228700
num_examples: 5152007
- name: test
num_bytes: 212326700
num... |
TheFusion21/PokemonCards | 2022-11-21T18:28:25.000Z | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"reg... | TheFusion21 | null | null | null | 6 | 553 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: Pokemoncards
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- text-to-image
- image-to-text
task_ids:
- image-captioning
---
#... |
gamy0315/mixatis_clean | 2023-07-19T07:41:46.000Z | [
"region:us"
] | gamy0315 | null | null | null | 0 | 551 | ---
dataset_info:
features:
- name: token
sequence: string
- name: tag
sequence: string
- name: intent
sequence: string
splits:
- name: train
num_bytes: 6266669
num_examples: 13162
- name: validation
num_bytes: 334004
num_examples: 759
- name: test
num_bytes: 341726
n... |
sberquad | 2023-08-29T12:35:15.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ru",
"license:unknown",
"arxiv:1912... | null | Sber Question Answering Dataset (SberQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Russian original a... | @article{Efimov_2020,
title={SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis},
ISBN={9783030582197},
ISSN={1611-3349},
url={http://dx.doi.org/10.1007/978-3-030-58219-7_1},
DOI={10.1007/978-3-030-58219-7_1},
journal={Experimental IR Meets Multilinguality, Multimodality, and I... | null | 10 | 550 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
- crowdsourced
language:
- ru
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: sberquad
pretty_name: SberQuAD
... |
SetFit/SentEval-CR | 2022-06-21T09:14:00.000Z | [
"region:us"
] | SetFit | null | null | null | 2 | 550 | # SentEval Customer Reviews
This dataset is a port of the official [SentEval `CR` dataset](https://nlp.stanford.edu/~sidaw/home/projects:nbsvm) from [this paper](https://dl.acm.org/doi/10.1145/1014052.1014073). The test split was created from the by randomly sampling 20% of the original data and the train split is the... |
israfelsr/mm_tiny_imagenet | 2022-12-16T11:19:54.000Z | [
"region:us"
] | israfelsr | null | null | null | 1 | 550 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': n01443537
'1': n01629819
'2': n01641577
'3': n01644900
'4': n01698640
'5': n01742172
'6': n01768244
'7': n01770... |
mc_taco | 2023-01-25T14:40:09.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
... | null | MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer
pairs that require temporal commonsense comprehension. A system receives a sentence
providing context information, a question designed to require temporal commonsense
knowledge, and multiple candidate answers. More than one candidate ans... | @inproceedings{ZKNR19,
author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding },
booktitle = {EMNLP},
year = {2019},
} | null | 0 | 547 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mc-tac... |
climate_fever | 2023-03-16T14:57:07.000Z | [
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_ids:text-scoring",
"task_ids:fact-checking",
"task_ids:fact-checking-retrieval",
"task_ids:semantic-similarity-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"annotations_creato... | null | A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim ... | @misc{diggelmann2020climatefever,
title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims},
author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold},
year={2020},
eprint={2012.00614},
archivePrefix={arXiv},
... | null | 8 | 545 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|wikipedia
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- text-scoring
- fact-che... |
result-kand2-sdxl-wuerst-karlo/103deca7 | 2023-09-22T05:57:32.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 545 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 210
num_examples: 10
download_size: 1367
dataset_size: 210
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "103deca... |
taishi-i/awesome-japanese-nlp-classification-dataset | 2023-09-09T11:09:04.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"language:ja",
"license:other",
"code",
"region:us"
] | taishi-i | This dataset determines whether a GitHub repository description relates to Japanese natural language processing (NLP). The labels are categorized as "Relevant (1)" and "Not Relevant (0)". | null | null | 1 | 544 | ---
license: other
task_categories:
- text-classification
language:
- en
- ja
tags:
- code
size_categories:
- 1K<n<10K
---
# Dataset overview
This dataset identifies whether a GitHub repository description pertains to Japanese natural language processing (NLP).
The labels are categorized as **"Relevant (1)" and "Not... |
nielsr/docvqa_1200_examples_donut | 2022-08-05T16:39:23.000Z | [
"region:us"
] | nielsr | null | null | null | 1 | 542 | Entry not found |
result-kand2-sdxl-wuerst-karlo/54ae8a8b | 2023-09-22T08:45:06.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 542 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 200
num_examples: 10
download_size: 1374
dataset_size: 200
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "54ae8a8... |
carblacac/twitter-sentiment-analysis | 2022-10-25T05:42:06.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"region:us"
] | carblacac | The Twitter Sentiment Analysis Dataset contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment.
The dataset is based on data from the following two sources:
University of Michigan Sentiment Analysis competition on Kaggle
Twitter Sentiment Corpus by Niek Sanders... | @InProceedings{thinknook:dataset,
title = {Twitter Sentiment Analysis Training Corpus (Dataset)},
author={Ibrahim Naji},
year={2012}
} | null | 8 | 541 | ---
pretty_name: "TSATC: Twitter Sentiment Analysis Training Corpus"
annotations_creators:
- expert-generated
language_creators:
- other
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- feeling... |
ecthr_cases | 2022-11-18T19:59:57.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-... | null | The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases. | @InProceedings{chalkidis-et-al-2021-ecthr,
title = "Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases",
author = "Chalkidis, Ilias and Fergadiotis, Manos and Tsarapatsanis, Dimitrios and Aletras, Nikolaos and Androutsopoulos, Ion and Malakasiotis, ... | null | 8 | 540 | ---
annotations_creators:
- expert-generated
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: ecthr
pretty... |
emrgnt-cmplxty/sciphi-textbooks-are-all-you-need | 2023-09-30T21:57:36.000Z | [
"license:llama2",
"region:us"
] | emrgnt-cmplxty | null | null | null | 82 | 540 | ---
dataset_info:
features:
- name: formatted_prompt
dtype: string
- name: completion
dtype: string
- name: first_task
dtype: string
- name: second_task
dtype: string
- name: last_task
dtype: string
- name: notes
dtype: string
- name: title
dtype: string
- name: model
d... |
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