Datasets:
nkasmanoff
/
huggingface-datasets

Dataset card Data Studio Files Community
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5.93k
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LTress/lrl_transfer_hubert
false
[]
LibriAdapt (For more information refer to the original paper at https://doi.org/10.1109%2Ficassp40776.2020.9053074)
0
0
Cheatham/unlabelled-EU-JAV-Italian
false
[]
null
0
0
wrice/sv_corpora_parliament_processed
false
[]
null
0
0
ErenHali/disaster_edited
false
[ "license:afl-3.0" ]
null
0
0
pinecone/yt-transcriptions
false
[]
null
19
1
Yah216/APCD_only_meter_data
false
[]
null
0
0
merionum/ru_paraphraser
false
[ "task_categories:text-classification", "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:sentence-similarity", "task_ids:semantic-similarity-scoring", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:ru", "license:mit" ]
null
46
3
mteb/toxic_conversations_50k
false
[ "language:en" ]
null
217
0
yanekyuk/wikikey
false
[ "license:mit" ]
null
2
0
mteb/tweet_sentiment_extraction
false
[ "language:en" ]
null
2,011
3
coreybrady/blackwoods
false
[ "license:cc-by-nc-4.0" ]
null
0
0
coreybrady/processedblackwoods
false
[]
null
0
0
Aniemore/REPV
false
[ "task_categories:audio-classification", "task_ids:audio-emotion-recognition", "annotations_creators:crowdsourced", "language_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ru", "license:mit" ]
null
22
2
Aniemore/REPV-S
false
[ "task_categories:audio-classification", "task_ids:audio-emotion-recognition", "annotations_creators:crowdsourced", "language_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ru", "license:mit" ]
null
0
2
coreybrady/blackwoods-sentences
false
[]
null
0
0
Evelyn18/becasv2
false
[]
automatic translation of the Stanford Question Answering Dataset (SQuAD) v2 into Spanish
0
0
patrickvonplaten/diffusion_images
false
[]
null
0
0
deepakvk/conversational_dialogues_001_holdout
false
[]
null
0
0
ChristophSchuhmann/wikiart_with_BLIP_captions
false
[ "license:cc-by-nc-4.0" ]
null
0
1
mbazaNLP/kinyarwanda-tts-dataset
false
[ "language_creators:Digital Umuganda", "size_categories:3K<n<4K", "size_categories:~6hours", "language:rw", "license:cc-by-4.0" ]
null
4
0
sileod/movie_recommendation
false
[ "task_categories:multiple-choice", "task_categories:question-answering", "task_ids:multiple-choice-qa", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:apache-2.0", "movie-recommendation", "collaborative-filtering", "movielens", "film", "doi:10.57967/hf/0257" ]
Movie recommendation task based on the Movielens dataset
7
6
elham/vv
false
[]
null
0
0
sileod/discourse_marker_qa
false
[ "task_categories:question-answering", "task_categories:multiple-choice", "task_ids:open-domain-qa", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:apache-2.0" ]
Discourse marker/connective prediction as multiple choice questions based on the Discovery dataset
0
3
deepakvk/conversational_dialogues_001_iteration
false
[]
null
0
0
blinoff/medical_institutions_reviews
false
[ "task_categories:text-classification", "task_ids:sentiment-classification", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:ru" ]
null
0
0
wrice/sv_corpora_parliament_processed_punctuation
false
[]
null
0
0
nickmuchi/fin-class-label
false
[]
null
0
0
wrice/wikipedia-en-punctuated
false
[]
null
0
0
Yah216/APCD-Poem_Rawiy_detection
false
[ "task_categories:text-classification", "language:ar" ]
null
0
0
Yah216/Poem_APCD_text_only
false
[]
null
0
0
cat-state/clip-embeddings
false
[]
null
0
0
Abdelrahman-Rezk/Arabic_Poem_Comprehensive_Dataset_APCD
false
[]
null
0
0
jet-universe/top_landscape
false
[]
null
0
0
jet-universe/quark_gluon
false
[]
null
0
0
gary109/sv_corpora_parliament_processed
false
[]
null
0
0
Lais/Sentiment-Analysis-on-Movie-Reviews
false
[]
null
0
0
gary109/onset-singing_corpora_parliament_processed
false
[]
null
0
0
diegorysr/sentiment-banking
false
[]
null
0
0
jonas/undp_jobs_raw
false
[ "license:wtfpl" ]
null
0
0
diegorysr/sentiment-banking-pln
false
[]
null
0
0
diversifix/inclusive_words
false
[ "language:de", "license:other" ]
null
0
1
daniel-dona/dani-voice
false
[ "license:cc0-1.0" ]
null
0
0
Rexhaif/xsum_reduced
false
[]
null
0
0
abdoutony207/en_ar_dataset
false
[]
null
0
0
vcv/sentiment-banking
false
[]
null
2
0
meghazisofiane/mt-ar-en-1
false
[]
null
0
0
Rexhaif/xnli_ru
false
[]
null
0
0
ashesicsis1/asr-sample
false
[]
Sample automatic speech recognition dataset from Librispeech (clean) by Dennis Owusu.
0
0
stfuowned/rick2
false
[]
null
0
0
silver/lccc
false
[ "task_categories:conversational", "task_ids:dialogue-generation", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:zh", "license:mit", "dialogue-response-retrieval", "arxiv:2008.03946" ]
LCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large corpus of Chinese conversations. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered.
0
7
silver/mmchat
false
[ "task_categories:conversational", "task_ids:dialogue-generation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:zh", "license:other", "arxiv:2108.07154", "arxiv:2008.03946" ]
MMChat is a large-scale dialogue dataset that contains image-grounded dialogues in Chinese. Each dialogue in MMChat is associated with one or more images (maximum 9 images per dialogue). We design various strategies to ensure the quality of the dialogues in MMChat.
2
7
avacaondata/fullnovalid
false
[]
null
0
0
silver/personal_dialog
false
[ "task_categories:conversational", "task_ids:dialogue-generation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:zh", "license:other", "arxiv:1901.09672" ]
The PersonalDialog dataset is a large-scale multi-turn Chinese dialogue dataset containing various traits from a large number of speakers. We are releasing about 5M sessions of carefully filtered dialogues. Each utterance in PersonalDialog is associated with a speaker marked with traits like Gender, Location, Interest Tags.
66
7
w4ngatang/squality
false
[]
null
1
0
GEM/squality
false
[ "task_categories:summarization", "annotations_creators:crowd-sourced", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2205.11465", "arxiv:2112.07637", "arxiv:2104.05938" ]
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
5
0
danielhou13/cogs402dataset
false
[]
null
0
0
cat-state/mscoco-1st-caption
false
[ "license:cc-by-4.0" ]
null
0
0
Rexhaif/cedr-full
false
[]
null
0
0
meetyildiz/toqad-aug
false
[]
Turkish Question Answering Dataset - Base
0
0
aaraki/github-issues8
false
[]
null
0
0
eabayed/EmiratiDialictShowsAudioTranscription
false
[ "license:afl-3.0" ]
null
0
1
Wangchunshu/VLUE
false
[ "license:afl-3.0" ]
null
0
1
juletxara/xquad_xtreme
false
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:extended|squad", "language:en", "language:es", "language:de", "language:el", "language:hi", "language:th", "language:ru", "language:tr", "language:ar", "language:vi", "language:zh", "language:ro", "license:cc-by-sa-4.0", "arxiv:1910.11856" ]
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages. We also include "translate-train", "translate-dev", and "translate-test" splits for each non-English language from XTREME (Hu et al., 2020). These can be used to run XQuAD in the "translate-train" or "translate-test" settings.
0
3
Lehrig/Monkey-Species-Collection
false
[]
This dataset is intended as a test case for fine-grain classification tasks (10 different kinds of monkey species). The dataset consists of almost 1400 JPEG images grouped into two splits - training and validation. Each split contains 10 categories labeled as n0~n9, each corresponding a species from [Wikipedia's monkey cladogram](https://en.wikipedia.org/wiki/Monkey). Images were downloaded with help of the [googliser](https://github.com/teracow/googliser) open source code. | Label | Latin Name | Common Name | Train Images | Validation Images | | ----- | --------------------- | ------------------------- | ------------ | ----------------- | | n0 | alouatta_palliata | mantled_howler | 131 | 26 | | n1 | erythrocebus_patas | patas_monkey | 139 | 28 | | n2 | cacajao_calvus | bald_uakari | 137 | 27 | | n3 | macaca_fuscata | japanese_macaque | 152 | 30 | | n4 | cebuella_pygmea | pygmy_marmoset | 131 | 26 | | n5 | cebus_capucinus | white_headed_capuchin | 141 | 28 | | n6 | mico_argentatus | silvery_marmoset | 132 | 26 | | n7 | saimiri_sciureus | common_squirrel_monkey | 142 | 28 | | n8 | aotus_nigriceps | black_headed_night_monkey | 133 | 27 | | n9 | trachypithecus_johnii | nilgiri_langur | 132 | 26 | This collection includes the following GTZAN variants: * original (images are 400x300 px or larger; ~550 MB) * downsized (images are downsized to 224x224 px; ~40 MB)
6
1
momilla/flattened_contracts
false
[]
null
1
0
anton-l/earnings21
false
[ "license:cc-by-sa-4.0" ]
null
0
0
DFKI-SLT/wikitext_linked
false
[ "task_categories:fill-mask", "task_categories:token-classification", "task_categories:text-classification", "task_ids:masked-language-modeling", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "task_ids:lemmatization", "task_ids:parsing", "task_ids:entity-linking-classification", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:extended|wikitext", "language:en", "license:cc-by-sa-4.0", "arxiv:1609.07843" ]
The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. Dependency Relations, POS, NER tags are marked with trankit and entities are linked with entity-fishing. The dataset is available under the Creative Commons Attribution-ShareAlike License.
13
1
hongdijk/kluekornlu
false
[ "license:other" ]
null
0
0
cahya/paper_abstract
false
[]
null
0
0
hongdijk/AUGAUG
false
[ "license:other" ]
null
0
0
mteb/mind_small
false
[]
null
127
0
yananchen/few_nerd_seq2seq
false
[]
null
0
0
mteb/sts22-crosslingual-sts
false
[ "language:ar", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:pl", "language:ru", "language:tr", "language:zh" ]
SemEval 2022 Task 8: Multilingual News Article Similarity
27,198
3
cestwc/lsnli-full-uncertain
false
[]
null
0
0
prajdabre/KreolMorisienMT
false
[ "license:cc" ]
null
0
1
declare-lab/cicero
false
[ "license:mit", "arxiv:2203.13926", "arxiv:1710.03957", "arxiv:1902.00164", "arxiv:2004.04494" ]
null
8
1
zdreiosis/ffa_grab_1
false
[ "license:other" ]
null
0
0
mbazaNLP/kinyarwanda-language-model-dataset
false
[]
null
0
0
ibm/vira-intents
false
[]
null
3
0
twnlp/mydataset
false
[]
null
0
0
mikehemberger/topex_dataset
false
[]
null
0
0
cestwc/quora
false
[]
null
0
0
mikehemberger/topex_dennis
false
[]
null
0
0
fpaulino/portuguese-tweets
false
[]
null
0
0
biwi_kinect_head_pose
false
[ "task_categories:other", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:other", "head-pose-estimation" ]
The Biwi Kinect Head Pose Database is acquired with the Microsoft Kinect sensor, a structured IR light device.It contains 15K images of 20 people with 6 females and 14 males where 4 people were recorded twice.
0
0
blinoff/restaurants_reviews
false
[ "task_categories:text-classification", "task_ids:sentiment-classification", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:ru" ]
null
0
0
myvision/yuanchuan-synthetic-dataset-final
false
[]
null
0
0
jakka/warehouse_part1
false
[]
null
0
0
mikehemberger/topex
false
[]
null
0
0
martinolmos/discursos_peron
false
[ "license:cc-by-sa-4.0" ]
null
3
0
dcfidalgo/liar_sentiment
false
[]
null
0
0
Rauljj95/Rubrix
false
[]
null
0
0
angie-chen55/python-github-code
false
[]
null
0
1
JoseMiguel/sa
false
[]
null
0
0
aleon215/sentiment-banking
false
[]
null
0
0
nferruz/dataset_fastas
false
[ "license:mit" ]
null
2
0
arize-ai/ecommerce_reviews_with_language_drift
false
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|imdb", "language:en", "license:mit" ]
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on product reviews from an e-commerce store. The reviews are labeled on a scale from 1 to 5 (stars). The training & validation sets are fully composed by reviews written in english. However, the production set has some reviews written in spanish. At Arize, we work to surface this issue and help you solve it.
22
1
lyakaap/laion2B-japanese-subset
false
[]
null
2
1
PoolC/1-fold-clone-detection-600k-5fold
false
[]
null
143
2
PoolC/2-fold-clone-detection-600k-5fold
false
[]
null
26
0