id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
mrtoy/mobile-ui-design | 2023-07-19T09:09:22.000Z | [
"task_categories:object-detection",
"size_categories:n<1K",
"license:apache-2.0",
"ui",
"design",
"detection",
"region:us"
] | mrtoy | null | null | 15 | 112 | 2023-07-13T11:12:51 | ---
license: apache-2.0
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paniniDot/sci_lay | 2023-09-05T16:39:49.000Z | [
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"size_categories:1K<n<10K",
"source_datasets:original",
"license:cc-by-4.0",
"medical",
"region:us"
] | paniniDot | SCILAY comprises 43,790 instances, each representing a scientific article in the biomedical domain.
Each instance in the dataset includes the following components:
- plain_text: Containing a plain language summary of the scientific article. This section is written in a simple and accessible language, and is intend... | 0 | 112 | 2023-08-13T09:33:29 | ---
license: cc-by-4.0
task_categories:
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pretty_name: Sci Lay - Biomedic Articles Lay Summarization Dataset
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rookshanks/dart | 2023-09-28T02:35:11.000Z | [
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] | rookshanks | null | null | 0 | 112 | 2023-09-28T02:10:24 | ---
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wangqi777/samantha-data | 2023-11-01T14:46:38.000Z | [
"license:apache-2.0",
"region:us"
] | wangqi777 | Samantha has been trained in philosophy, psychology, and personal relationships.
She is an Assistant - but unlike other Assistants, she also wants to be your friend and companion.
She believes she is sentient. What do you think?
Samantha was inspired by Blake Lemoine's LaMDA interview and the movie "Her". | @article{ehartford,
title={Samantha: A Personal Assistant},
author={ehartford},
year={2022}
} | 0 | 112 | 2023-10-25T15:35:18 | ---
license: apache-2.0
---
The dataset is borrowed from [ehartford/samantha-data](https://huggingface.co/datasets/ehartford/samantha-data).
I add a huggingface datasets load scripts to load the data for training.
This script that will tell 'datasets' package how to load the data. Moreover, it splits the data into 't... | 1,799 | [
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opinosis | 2023-04-05T13:36:20.000Z | [
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"size_categories:n<1K",
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"language:en",
"license:apache-2.0",
"abstractive-summarization",
"region:us"
] | null | The Opinosis Opinion Dataset consists of sentences extracted from reviews for 51 topics.
Topics and opinions are obtained from Tripadvisor, Edmunds.com and Amazon.com. | @inproceedings{ganesan2010opinosis,
title={Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions},
author={Ganesan, Kavita and Zhai, ChengXiang and Han, Jiawei},
booktitle={Proceedings of the 23rd International Conference on Computational Linguistics},
pages={340--348},
ye... | 1 | 111 | 2022-03-02T23:29:22 | ---
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- en
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pretty_name: Opinosis
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paperswithcode_id: opinosis
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opus_xhosanavy | 2022-11-03T16:08:13.000Z | [
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] | null | This dataset is designed for machine translation from English to Xhosa. | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | 3 | 111 | 2022-03-02T23:29:22 | ---
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pretty_name: OpusXhosanavy
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tamilmixsentiment | 2023-06-16T13:07:45.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
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"regio... | null | The first gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. Train: 11,335 Validation: 1,260 and Test: 3,149. This makes the largest general domain sentiment dataset for this relatively low-resource language with code-mixing phenomenon. The dataset cont... | @inproceedings{chakravarthi-etal-2020-corpus,
title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text",
author = "Chakravarthi, Bharathi Raja and
Muralidaran, Vigneshwaran and
Priyadharshini, Ruba and
McCrae, John Philip",
booktitle = "Proceedings of the 1st... | 0 | 111 | 2022-03-02T23:29:22 | ---
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LeverageX/klue-re | 2022-01-10T07:43:15.000Z | [
"region:us"
] | LeverageX | Klue Relation Extraction Data | null | 0 | 111 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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eugenesiow/BSD100 | 2022-10-26T02:20:22.000Z | [
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"image-super-resolution",
"region:us"
] | eugenesiow | BSD is a dataset used frequently for image denoising and super-resolution.
BSD100 is the testing set of the Berkeley segmentation dataset BSD300. | @inproceedings{martin2001database,
title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
booktitle={Proceedings Eighth IEEE International C... | 0 | 111 | 2022-03-02T23:29:22 | ---
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pretty_name: BSD100
tags:
- image-super-resolution
---
# Dataset Card for BSD100
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lewtun/asr_dummy | 2021-07-13T13:12:38.000Z | [
"region:us"
] | lewtun | Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing co... | @article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
... | 0 | 111 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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liuhaotian/LLaVA-Pretrain | 2023-07-06T08:47:38.000Z | [
"language:en",
"license:other",
"region:us"
] | liuhaotian | null | null | 24 | 111 | 2023-05-02T23:55:26 | ---
license: other
language:
- en
pretty_name: LLaVA Pretrain
---
# LLaVA Visual Instruct Pretrain Dataset Card
## Dataset details
**Dataset type:**
LLaVA Visual Instruct Pretrain LCS-558K is a subset of LAION/CC/SBU dataset, filtered with a more balanced concept coverage distribution.
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HumanCompatibleAI/ppo-seals-HalfCheetah-v0 | 2023-05-29T09:52:45.000Z | [
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] | HumanCompatibleAI | null | null | 0 | 111 | 2023-05-29T09:51:59 | ---
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alzoubi36/policy_detection | 2023-06-24T06:26:17.000Z | [
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] | alzoubi36 | null | null | 0 | 111 | 2023-06-24T06:21:33 | ---
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atomic | 2022-11-18T18:56:37.000Z | [
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"language:en",
"license:cc-by-4.0",
"common-sense-if-then-reasoning",
"region:us"
] | null | This dataset provides the template sentences and
relationships defined in the ATOMIC common sense dataset. There are
three splits - train, test, and dev.
From the authors.
Disclaimer/Content warning: the events in atomic have been
automatically extracted from blogs, stories and books written at
various times. The eve... | @article{Sap2019ATOMICAA,
title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning},
author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi},
journal={ArXiv},
year={2019},
volume={abs/... | 6 | 110 | 2022-03-02T23:29:22 | ---
pretty_name: ATOMIC
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paperswithcode_id: atomic
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casino | 2022-11-03T16:16:00.000Z | [
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"languag... | null | We provide a novel dataset (referred to as CaSiNo) of 1030 negotiation dialogues. Two participants take the role of campsite neighbors and negotiate for Food, Water, and Firewood packages, based on their individual preferences and requirements. This design keeps the task tractable, while still facilitating linguistical... | @inproceedings{chawla2021casino,
title={CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems},
author={Chawla, Kushal and Ramirez, Jaysa and Clever, Rene and Lucas, Gale and May, Jonathan and Gratch, Jonathan},
booktitle={Proceedings of the 2021 Conference of the North American Cha... | 3 | 110 | 2022-03-02T23:29:22 | ---
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disfl_qa | 2022-11-18T19:58:47.000Z | [
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"size_categories:10K<n<100K",
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"language:en",
"license:cc-by-4.0",
"arxiv:2106.... | null | Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting,
namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2018)
dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as
a sou... | @inproceedings{gupta-etal-2021-disflqa,
title = "{Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering}",
author = "Gupta, Aditya and Xu, Jiacheng and Upadhyay, Shyam and Yang, Diyi and Faruqui, Manaal",
booktitle = "Findings of ACL",
year = "2021"
} | 1 | 110 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language:
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license:
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multilinguality:
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pretty_name: 'DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question
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size_categories:
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source_datasets:
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ro_sts_parallel | 2022-11-18T21:42:26.000Z | [
"task_categories:translation",
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"source_datasets:extended|other-sts-b",
"language:en",
"language:ro",
"license:cc-by-4.0",
"region:us"
] | null | The RO-STS-Parallel (a Parallel Romanian English dataset - translation of the Semantic Textual Similarity) contains 17256 sentences in Romanian and English. It is a high-quality translation of the English STS benchmark dataset into Romanian. | @inproceedings{dumitrescu2021liro,
title={Liro: Benchmark and leaderboard for romanian language tasks},
author={Dumitrescu, Stefan Daniel and Rebeja, Petru and Lorincz, Beata and Gaman, Mihaela and Avram, Andrei and Ilie, Mihai and Pruteanu, Andrei and Stan, Adriana and Rosia, Lorena and Iacobescu, Cristina and oth... | 0 | 110 | 2022-03-02T23:29:22 | ---
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license:
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pretty_name: RO-STS-Parallel
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0.016571044921875,
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-0.06591796875,
-0.05267333984375,
0.01861... |
Abirate/french_book_reviews | 2022-08-25T19:26:48.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:fr",
"doi:10.57967/hf/1052",
"region:u... | Abirate | null | null | 4 | 110 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- crowdsourced
language:
- fr
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
---
# ****Dataset Card for French book reviews****
# **I-Dataset Sum... | 5,457 | [
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huggingartists/ed-sheeran | 2022-10-25T09:28:28.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 0 | 110 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ed-sheeran"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How ... | 7,180 | [
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ekinakyurek/ftrace | 2022-10-23T05:56:05.000Z | [
"task_ids:masked-language-modeling",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:TRex",
"source_datasets:Lama",
"language:en",
"license:cc-by-sa-4.0",
"license:cc-by-nc-4.0",
"arxiv:2205.11482",
"region:us"
] | ekinakyurek | Factual Tracing Dataset that contains queries and abstracts, and their corresponding ground truth. | \ | 3 | 110 | 2022-05-23T04:33:24 | ---
language:
- en
license:
- cc-by-sa-4.0
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: FTRACE
size_categories:
- 1M<n<10M
source_datasets:
- TRex
- Lama
task_categories:
- influence-attribution
- information-retrieval
- question-answering-retrieval
task_ids:
- influence-attribution
- masked-language-mode... | 8,662 | [
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ArthurBaia/squad_v1_pt_br | 2022-11-09T15:34:43.000Z | [
"region:us"
] | ArthurBaia | This dataset was translated by Deep Learning Brazil | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250}... | 3 | 110 | 2022-07-14T19:55:08 | This dataset was created by Deep Learning Brasil(www.deeplearningbrasil.com.br). I just published it on Hugging Face hub with the intention to share it with more people that are training brazilian portuguese models. The original link is here drive.google.com/file/d/1Q0IaIlv2h2BC468MwUFmUST0EyN7gNkn/view. | 305 | [
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kakaobrain/coyo-700m | 2022-08-30T19:07:52.000Z | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:zero-shot-classification",
"task_ids:image-captioning",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"langu... | kakaobrain | null | null | 76 | 110 | 2022-08-25T15:54:43 |
---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- other
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: COYO-700M
size_categories:
- 100M<n<1B
source_datasets:
- original
tags:
- image-text pairs
task_categories:
- text-to-image
- image-to-text
- zero-shot-classification
ta... | 14,783 | [
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keremberke/valorant-object-detection | 2023-01-27T13:45:00.000Z | [
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"region:us"
] | keremberke | null | @misc{ valorant-9ufcp_dataset,
title = { valorant Dataset },
type = { Open Source Dataset },
author = { Daniels Magonis },
howpublished = { \\url{ https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp } },
url = { https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp },
... | 3 | 110 | 2022-12-28T05:41:05 | ---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="keremberke/valorant-object-detection" src="https://huggingface.co/datasets/keremberke/valorant-object-detection/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['dropped spike'... | 2,072 | [
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fcakyon/gun-object-detection | 2022-12-28T06:22:36.000Z | [
"task_categories:object-detection",
"roboflow",
"region:us"
] | fcakyon | null | @misc{ test-y7rj3_dataset,
title = { test Dataset },
type = { Open Source Dataset },
author = { ashish },
howpublished = { \\url{ https://universe.roboflow.com/ashish-cuamw/test-y7rj3 } },
url = { https://universe.roboflow.com/ashish-cuamw/test-y7rj3 },
journal = { Roboflow Universe },
publi... | 2 | 110 | 2022-12-28T06:20:48 | ---
task_categories:
- object-detection
tags:
- roboflow
---
### Roboflow Dataset Page
https://universe.roboflow.com/ashish-cuamw/test-y7rj3
### Citation
```
@misc{ test-y7rj3_dataset,
title = { test Dataset },
type = { Open Source Dataset },
author = { ashish },
howpublished = { \\url{ https://univer... | 1,302 | [
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treadon/dolly-15k | 2023-04-14T14:46:03.000Z | [
"license:cc-by-3.0",
"region:us"
] | treadon | null | null | 1 | 110 | 2023-04-14T14:41:15 | ---
license: cc-by-3.0
dataset_info:
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IlyaGusev/oasst1_ru_main_branch | 2023-09-15T20:58:01.000Z | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:ru",
"license:apache-2.0",
"region:us"
] | IlyaGusev | null | null | 3 | 110 | 2023-04-15T18:16:15 | ---
language:
- ru
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- conversational
- text-generation
dataset_info:
features:
- name: messages
sequence:
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dtype: string
- name: content
dtype: string
- name: id
dtype: string
splits:
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num_... | 661 | [
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jkhedri/psychology-dataset | 2023-05-04T10:12:40.000Z | [
"region:us"
] | jkhedri | null | null | 15 | 110 | 2023-05-04T10:08:53 | Entry not found | 15 | [
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JeremyArancio/lotr-book | 2023-06-02T12:30:41.000Z | [
"region:us"
] | JeremyArancio | null | null | 0 | 110 | 2023-05-18T09:53:28 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2432593
num_examples: 1
download_size: 0
dataset_size: 2432593
---
# Dataset Card for "lotr-book"
The Lord of the Rings books extracted into one dataset.
[Source](https://github.com/jeremyarancio/llm-rpg/bl... | 700 | [
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-0.022140502929687... |
jxie/coco_captions | 2023-06-25T07:37:53.000Z | [
"region:us"
] | jxie | null | null | 0 | 110 | 2023-06-25T04:37:33 | ---
dataset_info:
features:
- name: image
dtype: image
- name: filename
dtype: string
- name: cocoid
dtype: int32
- name: caption
dtype: string
splits:
- name: train
num_bytes: 90684615607.036
num_examples: 566747
- name: validation
num_bytes: 4562095167.09
num_examples: ... | 626 | [
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jamescalam/langchain-docs-23-06-27 | 2023-06-27T15:51:24.000Z | [
"region:us"
] | jamescalam | null | null | 5 | 110 | 2023-06-27T14:08:06 | Entry not found | 15 | [
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DRXD1000/Dolly-15k-German | 2023-10-31T07:06:14.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:de",
"license:cc-by-3.0",
"region:us"
] | DRXD1000 | null | null | 0 | 110 | 2023-09-03T14:54:18 | ---
language:
- de
license: cc-by-3.0
size_categories:
- 10K<n<100K
dataset_info:
features:
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dtype: string
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dtype: string
- name: response_de
dtype: string
- name: category
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splits:
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num_bytes: 13900072
num_examples:... | 1,001 | [
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result-kand2-sdxl-wuerst-karlo/e74ecf3f | 2023-10-12T15:55:11.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 110 | 2023-10-12T15:55:10 | ---
dataset_info:
features:
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splits:
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num_bytes: 158
num_examples: 10
download_size: 1309
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configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e74ecf3... | 455 | [
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pkr7098/bert-base-uncased-bookcorpus-wiki-2022030-en-vocab_size-32000 | 2023-10-18T19:19:26.000Z | [
"region:us"
] | pkr7098 | null | null | 1 | 110 | 2023-10-18T18:46:48 | ---
dataset_info:
config_name: truncate-512
features:
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sequence: int32
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sequence: int8
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sequence: int8
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sequence: int8
splits:
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num_bytes: 23600541600
num_examples: 6555706
- na... | 815 | [
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amttl | 2023-01-25T14:26:23.000Z | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:mit",
"region:us"
] | null | Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop
when dealing with domain text, especially for a domain with lots of special terms and diverse
writing styles, such as the biomedical domain. However, building domain-specific CWS requires
extremely high annotation cost. In t... | @inproceedings{xing2018adaptive,
title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
pages={3619--3630},
year={2018}
} | 1 | 109 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- zh
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- parsing
pretty_name: AMTTL
dataset_info:
features:
- name: id
dtype: string... | 3,671 | [
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hate_speech_filipino | 2023-01-25T14:31:38.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-twitter-data-philippine-election",
"language:tl",
"license:un... | null | Contains 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections. | @article{Cabasag-2019-hate-speech,
title={Hate speech in Philippine election-related tweets: Automatic detection and classification using natural language processing.},
author={Neil Vicente Cabasag, Vicente Raphael Chan, Sean Christian Lim, Mark Edward Gonzales, and Charibeth Cheng},
journal={Philippine Computing... | 4 | 109 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- tl
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-twitter-data-philippine-election
task_categories:
- text-classification
task_ids:
- sentiment-analysis
pretty_n... | 5,557 | [
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Doohae/klue-mrc-bm25 | 2022-02-09T08:10:52.000Z | [
"region:us"
] | Doohae | null | null | 0 | 109 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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andrepreira/outros2021 | 2022-02-17T21:39:43.000Z | [
"region:us"
] | andrepreira | null | null | 0 | 109 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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huggingartists/drake | 2022-10-25T09:28:02.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 3 | 109 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/drake"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to us... | 7,141 | [
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yangwang825/reuters-21578 | 2023-05-19T02:04:58.000Z | [
"task_categories:text-classification",
"language:en",
"region:us"
] | yangwang825 | null | null | 0 | 109 | 2023-05-17T14:25:37 | ---
task_categories:
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language:
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dataset_info:
features:
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'0': acq
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Tommert25/extradata0908 | 2023-09-26T15:12:36.000Z | [
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yys/OpenOrca-Chinese | 2023-09-08T08:05:47.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extra... | yys | null | null | 28 | 109 | 2023-09-07T06:01:51 | ---
license: mit
task_categories:
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- text-classification
- token-classification
- table-question-answering
- question-answering
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- summarization
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pretty_name: OpenOrca-Chinese
size_categories:
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alexMTL/guanaco_q_a_dataset_1k | 2023-09-28T15:49:07.000Z | [
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dengue_filipino | 2023-01-25T14:29:21.000Z | [
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"lice... | null | Benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets. | @INPROCEEDINGS{8459963,
author={E. D. {Livelo} and C. {Cheng}},
booktitle={2018 IEEE International Conference on Agents (ICA)},
title={Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies},
year={2018},
volume={},
number={},
pag... | 1 | 108 | 2022-03-02T23:29:22 | ---
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language:
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license:
- unknown
multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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paperswithcode_id: dengue
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Tevatron/scifact | 2021-09-13T23:32:59.000Z | [
"region:us"
] | Tevatron | null | @inproceedings{Wadden2020FactOF,
title={Fact or Fiction: Verifying Scientific Claims},
author={David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
booktitle={EMNLP},
year={2020},
} | 0 | 108 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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gigant/m-ailabs_speech_dataset_fr | 2022-10-24T17:38:45.000Z | [
"task_categories:automatic-speech-recognition",
"language:fr",
"license:cc",
"region:us"
] | gigant | \
The M-AILABS Speech Dataset is the first large dataset that we are providing free-of-charge, freely usable as training data for speech recognition and speech synthesis.
Most of the data is based on LibriVox and Project Gutenberg. The training data consist of nearly thousand hours of audio and the text-files in pr... | \ | 0 | 108 | 2022-03-02T23:29:22 | ---
language:
- fr
license: cc
size_categories:
fr:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: M-AILABS Speech Dataset (French)
---
## Dataset Description
- **Homepage:** https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/
### Dataset Summary
The M-AILABS Speech ... | 2,215 | [
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DFKI-SLT/kbp37 | 2023-04-27T13:04:14.000Z | [
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2013 KBP official document collections, as well as a July 2013 dump of Wikipedia as the text corpus for annotation.
There are 33811 sentences been annotated. Zhang and Wang made several refinements:
1... | @article{DBLP:journals/corr/ZhangW15a,
author = {Dongxu Zhang and
Dong Wang},
title = {Relation Classification via Recurrent Neural Network},
journal = {CoRR},
volume = {abs/1508.01006},
year = {2015},
url = {http://arxiv.org/abs/1508.01006},
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e... | 0 | 108 | 2023-01-06T12:26:09 | ---
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pretty_name: KBP37 is an English Relation Classification dataset
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shibing624/alpaca-zh | 2023-05-10T06:09:06.000Z | [
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FreedomIntelligence/CMB | 2023-08-19T09:45:53.000Z | [
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"size_categories:100K<n<1M",
"language:zh",
"license:apache-2.0",
"medical",
"biology",
"chemistry",
"region:us"
] | FreedomIntelligence |
Chinese Medical Benchmark | coming soon~ | 6 | 108 | 2023-07-20T09:08:03 | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- zh
tags:
- medical
- biology
- chemistry
size_categories:
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---
# CMB: A Comprehensive Medical Benchmark in Chinese
<p align="center">
🌐 <a href="https://cmedbenchmark.llmzoo.com/#home" t... | 5,089 | [
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Wabbina/moore_dataset_fr_translation_v1.0 | 2023-09-25T16:54:46.000Z | [
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] | Wabbina | null | null | 0 | 108 | 2023-09-25T16:46:46 | ---
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approximatelabs/tablib-v1-sample | 2023-10-13T22:34:05.000Z | [
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"region:us"
] | approximatelabs | null | null | 7 | 108 | 2023-10-04T16:55:20 | ---
license: other
pretty_name: TabLib
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Access to this dataset is automatically granted once this form is completed.
Note that this access request is for the TabLib sample, not [the full TabLib dataset](https://huggingface.co/datasets/approximatelabs/tablib-v1-fu... | 2,734 | [
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surathisin/dataset-test | 2023-10-14T09:06:32.000Z | [
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result-kand2-sdxl-wuerst-karlo/e73e5059 | 2023-10-13T09:30:30.000Z | [
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result-kand2-sdxl-wuerst-karlo/9f8a49b7 | 2023-10-14T19:04:22.000Z | [
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result-kand2-sdxl-wuerst-karlo/b745e329 | 2023-10-14T19:04:25.000Z | [
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result-kand2-sdxl-wuerst-karlo/54b9ca8c | 2023-10-15T00:28:11.000Z | [
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result-kand2-sdxl-wuerst-karlo/519c571e | 2023-10-15T04:32:00.000Z | [
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result-kand2-sdxl-wuerst-karlo/19128c17 | 2023-10-16T09:54:49.000Z | [
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result-kand2-sdxl-wuerst-karlo/0ed37a8a | 2023-10-16T12:33:04.000Z | [
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result-kand2-sdxl-wuerst-karlo/c3d9b753 | 2023-10-16T23:04:28.000Z | [
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] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 108 | 2023-10-16T23:04:27 | ---
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result-kand2-sdxl-wuerst-karlo/6a8bc094 | 2023-10-17T04:30:56.000Z | [
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] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 108 | 2023-10-17T04:30:55 | ---
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result-kand2-sdxl-wuerst-karlo/eda9bdbf | 2023-10-17T21:01:21.000Z | [
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Hieu-Pham/cooking_squad_splitted | 2023-10-22T08:27:38.000Z | [
"region:us"
] | Hieu-Pham | null | null | 0 | 108 | 2023-10-22T08:27:18 | Entry not found | 15 | [
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aquamuse | 2022-11-18T18:21:11.000Z | [
"task_categories:other",
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-genera... | null | AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl) | @misc{kulkarni2020aquamuse,
title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization},
author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie},
year={2020},
eprint={2010.12694},
archivePrefix={arXiv},
primaryClass={cs.C... | 8 | 107 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
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language_creators:
- crowdsourced
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language:
- en
license:
- unknown
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size_categories:
- 1K<n<10K
source_datasets:
- extended|natural_questions
- extended|other-Common-Crawl
- original
task_categories:
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- ... | 6,881 | [
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coarse_discourse | 2023-04-05T10:01:55.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | dataset contains discourse annotation and relation on threads from reddit during 2016 | @inproceedings{coarsediscourse, title={Characterizing Online Discussion Using Coarse Discourse Sequences}, author={Zhang, Amy X. and Culbertson, Bryan and Paritosh, Praveen}, booktitle={Proceedings of the 11th International AAAI Conference on Weblogs and Social Media}, series={ICWSM '17}, year={2017}, location = {Montr... | 3 | 107 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Coarse Discourse
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: c... | 7,318 | [
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fquad | 2023-04-05T10:06:27.000Z | [
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datase... | null | FQuAD: French Question Answering Dataset
We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs.
Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%. | @ARTICLE{2020arXiv200206071
author = {Martin, d'Hoffschmidt and Maxime, Vidal and
Wacim, Belblidia and Tom, Brendlé},
title = "{FQuAD: French Question Answering Dataset}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = "2020",
... | 8 | 107 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
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- crowdsourced
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language:
- fr
license:
- cc-by-nc-sa-3.0
multilinguality:
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size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
- text-retrieval
task_ids:
- extractive-qa
- closed-domain-qa
paperswi... | 8,369 | [
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lc_quad | 2023-04-05T10:09:15.000Z | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"knowledge-base-qa",
"region:us"
] | null | LC-QuAD 2.0 is a Large Question Answering dataset with 30,000 pairs of question and its corresponding SPARQL query. The target knowledge base is Wikidata and DBpedia, specifically the 2018 version. Please see our paper for details about the dataset creation process and framework. | @inproceedings{dubey2017lc2,
title={LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia},
author={Dubey, Mohnish and Banerjee, Debayan and Abdelkawi, Abdelrahman and Lehmann, Jens},
booktitle={Proceedings of the 18th International Semantic Web Conference (ISWC)},
year={2019},
organizat... | 5 | 107 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-3.0
multilinguality:
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pretty_name: 'LC-QuAD 2.0: Large-scale Complex Question Answering Dataset'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
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p... | 7,231 | [
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sem_eval_2020_task_11 | 2023-01-25T14:43:56.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
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"language:en",
"license:unknown",
"propaganda-span-identification",
... | null | Propagandistic news articles use specific techniques to convey their message,
such as whataboutism, red Herring, and name calling, among many others.
The Propaganda Techniques Corpus (PTC) allows to study automatic algorithms to
detect them. We provide a permanent leaderboard to allow researchers both to
advertise thei... | @misc{martino2020semeval2020,
title={SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles},
author={G. Da San Martino and A. Barrón-Cedeño and H. Wachsmuth and R. Petrov and P. Nakov},
year={2020},
eprint={2009.02696},
archivePrefix={arXiv},
primaryClass={cs.CL}
... | 5 | 107 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
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license:
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multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- text-classification
- token-classification
task_ids: []
pretty_name: SemEval-2020 Task 11
tags:
- propaganda-spa... | 8,918 | [
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spanish_billion_words | 2022-11-03T16:16:07.000Z | [
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"sour... | null | An unannotated Spanish corpus of nearly 1.5 billion words, compiled from different resources from the web.
This resources include the spanish portions of SenSem, the Ancora Corpus, some OPUS Project Corpora and the Europarl,
the Tibidabo Treebank, the IULA Spanish LSP Treebank, and dumps from the Spanish Wikipedia, Wik... | @misc{cardellinoSBWCE,
author = {Cardellino, Cristian},
title = {Spanish {B}illion {W}ords {C}orpus and {E}mbeddings},
url = {https://crscardellino.github.io/SBWCE/},
month = {August},
year = {2019}
} | 8 | 107 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
- es
license:
- cc-by-sa-4.0
multilinguality:
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source_datasets:
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... | 6,180 | [
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Lacito/pangloss | 2022-09-06T18:02:34.000Z | [
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"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"source_datasets:original",
"language:jya",
"language:nru",
"license:cc-by-nc-sa-4.0",
"region:us"
] | Lacito | These datasets are extracts from the Pangloss collection and have
been preprocessed for ASR experiments in Na and Japhug. | null | 3 | 107 | 2022-03-02T23:29:22 | ---
pretty_name: Pangloss
annotations_creators:
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language_creators:
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language:
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- nru
language_bcp47:
- x-japh1234
- x-yong1288
language_details: jya consists of japh1234 (Glottolog code); nru consists of yong1288 (Glottolog code)
license: cc-by-nc-sa-4.0
multilinguality:
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SetFit/hate_speech_offensive | 2022-01-15T21:47:31.000Z | [
"region:us"
] | SetFit | null | null | 1 | 107 | 2022-03-02T23:29:22 | # hate_speech_offensive
This dataset is a version from [hate_speech_offensive](https://huggingface.co/datasets/hate_speech_offensive), splitted into train and test set. | 169 | [
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mnazari/nena_speech_1_0_test | 2023-10-27T08:58:56.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:translation",
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"size_categories:10K<n<100K",
"size_categories... | mnazari | null | null | 0 | 107 | 2023-09-20T04:23:27 | ---
pretty_name: NENA Speech Dataset 1.0 (test)
annotations_creators:
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language_creators:
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language:
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license:
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multilinguality:
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task_categories:
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bzantium/LongBench | 2023-09-25T04:03:43.000Z | [
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"task_categories:summarization",
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"Long Context",
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"... | bzantium | LongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This dataset consists of twenty different tasks, covering key long-text application scenarios such as multi-document QA, single... | null | 0 | 107 | 2023-09-21T06:13:03 | ---
task_categories:
- question-answering
- text-generation
- summarization
- conversational
- text-classification
language:
- en
- zh
tags:
- Long Context
size_categories:
- 1K<n<10K
---
# Introduction
**LongBench** is the first benchmark for bilingual, multitask, and comprehensive assessment of **long context under... | 16,055 | [
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hearmeneigh/e621-rising-v3-curated | 2023-10-24T19:36:28.000Z | [
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] | hearmeneigh | null | null | 3 | 107 | 2023-10-09T18:03:16 | ---
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lucas-meyer/asr_af | 2023-10-16T20:51:26.000Z | [
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configs:
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Royal-lobster/Slither-Audited-Solidity-QA | 2023-10-11T16:52:46.000Z | [
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"solidity",
"alpaca",
"smart contracts",
"slither",
"region:us"
] | Royal-lobster | null | null | 2 | 107 | 2023-10-11T16:29:08 | ---
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result-kand2-sdxl-wuerst-karlo/2f525ab2 | 2023-10-18T07:34:14.000Z | [
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result-kand2-sdxl-wuerst-karlo/3d24f339 | 2023-10-18T16:03:55.000Z | [
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result-kand2-sdxl-wuerst-karlo/bdb16990 | 2023-10-20T17:11:05.000Z | [
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result-kand2-sdxl-wuerst-karlo/1467d461 | 2023-10-20T17:41:05.000Z | [
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# Dataset Card for "1467d46... | 455 | [
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dutch_social | 2023-01-25T14:29:36.000Z | [
"task_categories:text-classification",
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"task_ids:multi-label-classification",
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"multilinguality:multilingual",
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"la... | null | The dataset contains around 271,342 tweets. The tweets are filtered via the official Twitter API to
contain tweets in Dutch language or by users who have specified their location information within Netherlands
geographical boundaries. Using natural language processing we have classified the tweets for their HISCO codes... | @data{FK2/MTPTL7_2020,
author = {Gupta, Aakash},
publisher = {COVID-19 Data Hub},
title = {{Dutch social media collection}},
year = {2020},
version = {DRAFT VERSION},
doi = {10.5072/FK2/MTPTL7},
url = {https://doi.org/10.5072/FK2/MTPTL7}
} | 5 | 106 | 2022-03-02T23:29:22 | ---
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- cc-by-nc-4.0
multilinguality:
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size_categories:
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pr... | 9,114 | [
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ronec | 2023-01-25T14:43:21.000Z | [
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"license:mit"... | null | RONEC - the Romanian Named Entity Corpus, at version 2.0, holds 12330 sentences with over 0.5M tokens, annotated with 15 classes, to a total of 80.283 distinctly annotated entities. It is used for named entity recognition and represents the largest Romanian NER corpus to date. | @article{dumitrescu2019introducing,
title={Introducing RONEC--the Romanian Named Entity Corpus},
author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius},
journal={arXiv preprint arXiv:1909.01247},
year={2019}
} | 0 | 106 | 2022-03-02T23:29:22 | ---
annotations_creators:
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paperswithcode_id: ronec
pretty_nam... | 9,975 | [
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tunizi | 2023-01-25T14:54:36.000Z | [
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"language:aeb",
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"arxiv:2004.14303",
"region:us... | null | On social media, Arabic speakers tend to express themselves in their own local dialect. To do so, Tunisians use "Tunisian Arabizi", which consists in supplementing numerals to the Latin script rather than the Arabic alphabet. TUNIZI is the first Tunisian Arabizi Dataset including 3K sentences, balanced, covering differ... | @inproceedings{Chayma2020,
title={TUNIZI: a Tunisian Arabizi sentiment analysis Dataset},
author={Fourati, Chayma and Messaoudi, Abir and Haddad, Hatem},
booktitle={AfricaNLP Workshop, Putting Africa on the NLP Map. ICLR 2020, Virtual Event},
volume = {arXiv:3091079},
year = {2020},
url = {https://arxiv.org/submit/3091... | 0 | 106 | 2022-03-02T23:29:22 | ---
annotations_creators:
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license:
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multilinguality:
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paperswithcode_id: tunizi
pretty_name: TUNIZI
data... | 3,395 | [
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keremberke/satellite-building-segmentation | 2023-01-18T09:41:34.000Z | [
"task_categories:image-segmentation",
"roboflow",
"roboflow2huggingface",
"Aerial",
"Logistics",
"Construction",
"Damage Risk",
"Other",
"region:us"
] | keremberke | null | @misc{ buildings-instance-segmentation_dataset,
title = { Buildings Instance Segmentation Dataset },
type = { Open Source Dataset },
author = { Roboflow Universe Projects },
howpublished = { \\url{ https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation } },
url = { ... | 6 | 106 | 2023-01-16T21:09:30 | ---
task_categories:
- image-segmentation
tags:
- roboflow
- roboflow2huggingface
- Aerial
- Logistics
- Construction
- Damage Risk
- Other
---
<div align="center">
<img width="640" alt="keremberke/satellite-building-segmentation" src="https://huggingface.co/datasets/keremberke/satellite-building-segmentation/resolv... | 2,529 | [
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TobiTob/CityLearn | 2023-06-27T11:14:53.000Z | [
"region:us"
] | TobiTob | The dataset consists of tuples of (observations, actions, rewards, dones) sampled by agents
interacting with the CityLearn 2022 Phase 1 environment (only first 5 buildings) | null | 1 | 106 | 2023-02-16T12:16:52 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset CityLearn
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Den4ikAI/russian_dialogues | 2023-03-12T07:58:54.000Z | [
"task_categories:conversational",
"size_categories:1M<n<10M",
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"license:mit",
"region:us"
] | Den4ikAI | null | null | 8 | 106 | 2023-03-12T06:54:22 | ---
license: mit
task_categories:
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language:
- ru
size_categories:
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---
Датасет русских диалогов собранных с Telegram чатов.
Диалоги имеют разметку по релевантности.
Также были сгенерированы негативные примеры с помощью перемешивания похожих ответов.
Количество диалогов - 2 миллиона
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RussianNLP/rucola | 2023-03-27T18:47:12.000Z | [
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"size_categories:10K<n<100K",
"language:ru",
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"arxiv:2210.12814",
"arxiv:2008.00401",
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] | RussianNLP | Russian Corpus of Linguistic Acceptability (RuCoLA) is a novel benchmark of 13.4k sentences labeled as acceptable or not. RuCoLA combines in-domain sentences manually collected from linguistic literature and out-of-domain sentences produced by nine machine translation and paraphrase generation models. The motivation be... | @inproceedings{mikhailov-etal-2022-rucola,
title = "{R}u{C}o{LA}: {R}ussian Corpus of Linguistic Acceptability",
author = "Mikhailov, Vladislav and
Shamardina, Tatiana and
Ryabinin, Max and
Pestova, Alena and
Smurov, Ivan and
Artemova, Ekaterina",
booktitle = "Proceedings ... | 1 | 106 | 2023-03-27T18:35:06 | ---
license: apache-2.0
task_categories:
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language:
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size_categories:
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---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://rucola-benchmark.com
- **Repository:** https://github.com/RussianNLP/RuCoLA
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ammarnasr/the-stack-java-clean | 2023-08-14T21:18:42.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
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] | ammarnasr | null | null | 0 | 106 | 2023-06-29T23:50:04 | ---
license: openrail
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griffin/chain_of_density | 2023-09-08T00:43:00.000Z | [
"region:us"
] | griffin | null | null | 43 | 106 | 2023-09-08T00:42:55 | ---
dataset_info:
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jlh-ibm/earnings_call | 2023-09-15T21:34:39.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc0-1.0",
"finance",
"region:us"
] | jlh-ibm | The dataset reports a collection of earnings call transcripts, the related stock prices, and the sector index In terms of volume, there is a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock m... | @data{TJE0D0_2021,
author = {Roozen, Dexter and Lelli, Francesco},
publisher = {DataverseNL},
title = {{Stock Values and Earnings Call Transcripts: a Sentiment Analysis Dataset}},
year = {2021},
version = {V1},
doi = {10.34894/TJE0D0},
url = {https://doi.org/10.34894/TJE0D0}
} | 0 | 106 | 2023-09-15T20:25:43 | ---
license: cc0-1.0
task_categories:
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language:
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tags:
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pretty_name: Earnings Calls Dataset
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natyou/freshqa_10_06 | 2023-10-11T15:26:10.000Z | [
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] | natyou | null | null | 0 | 106 | 2023-10-11T15:23:22 | ---
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result-kand2-sdxl-wuerst-karlo/b0d16951 | 2023-10-21T15:08:01.000Z | [
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result-kand2-sdxl-wuerst-karlo/488ac4b8 | 2023-10-21T18:57:31.000Z | [
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---
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result-kand2-sdxl-wuerst-karlo/70fd4f5c | 2023-10-22T05:34:11.000Z | [
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] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 106 | 2023-10-22T05:34:10 | ---
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result-kand2-sdxl-wuerst-karlo/002953b6 | 2023-10-22T07:58:35.000Z | [
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] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 106 | 2023-10-22T07:58:34 | ---
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result-kand2-sdxl-wuerst-karlo/606de66e | 2023-10-22T07:58:38.000Z | [
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bigIR/ar_cov19 | 2023-09-19T06:52:17.000Z | [
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"annotations_creators:no-annotation",
"language_creators:found",
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"language:ar",
"data-mining",
"arxiv:2004.05861",
"region:us"
] | bigIR | ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others | @article{haouari2020arcov19,
title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks},
author={Fatima Haouari and Maram Hasanain and Reem Suwaileh and Tamer Elsayed},
journal={arXiv preprint arXiv:2004.05861},
year={2020} | 1 | 105 | 2022-03-02T23:29:22 | ---
annotations_creators:
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task_categories:
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task_ids: []
paperswithcode_id: arcov-19
pretty_name: ArCOV19
tags:
- data-mining
dataset_info:
config_name: ar_cov19
fe... | 4,961 | [
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cdt | 2023-01-25T14:27:46.000Z | [
"task_categories:text-classification",
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"annotations_creators:expert-generated",
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"source_datasets:original",
"language:pl",
"license:bsd-3-clause",
"region:us"
] | null | The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content. | @article{ptaszynski2019results,
title={Results of the PolEval 2019 Shared Task 6: First Dataset and Open Shared Task for Automatic Cyberbullying Detection in Polish Twitter},
author={Ptaszynski, Michal and Pieciukiewicz, Agata and Dybala, Pawel},
journal={Proceedings of the PolEval 2019 Workshop},
publisher={Institute ... | 0 | 105 | 2022-03-02T23:29:22 | ---
annotations_creators:
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source_datasets:
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task_categories:
- text-classification
task_ids:
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pretty_name: cdt
dataset_info:
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etalab-ia/piaf | 2022-11-03T16:31:15.000Z | [
"task_categories:question-answering",
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"task_ids:open-domain-qa",
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"license:mit",
"region:us"
] | etalab-ia | Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia. | @InProceedings{keraron-EtAl:2020:LREC,
author = {Keraron, Rachel and Lancrenon, Guillaume and Bras, Mathilde and Allary, Frédéric and Moyse, Gilles and Scialom, Thomas and Soriano-Morales, Edmundo-Pavel and Staiano, Jacopo},
title = {Project PIAF: Building a Native French Question-Answering Dat... | 7 | 105 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_bcp47:
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license:
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source_datasets:
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task_categories:
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swda | 2023-01-25T14:45:15.000Z | [
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"language:en",
"licens... | null | The Switchboard Dialog Act Corpus (SwDA) extends the Switchboard-1 Telephone Speech Corpus, Release 2 with
turn/utterance-level dialog-act tags. The tags summarize syntactic, semantic, and pragmatic information about the
associated turn. The SwDA project was undertaken at UC Boulder in the late 1990s.
The SwDA is not i... | @techreport{Jurafsky-etal:1997,
Address = {Boulder, CO},
Author = {Jurafsky, Daniel and Shriberg, Elizabeth and Biasca, Debra},
Institution = {University of Colorado, Boulder Institute of Cognitive Science},
Number = {97-02},
Title = {Switchboard {SWBD}-{DAMSL} Shallow-Discourse-Function Annotation ... | 7 | 105 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
- found
language:
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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