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class | downloads float64 1 1M ⌀ | gated bool 2
classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2
classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Datatang | null | null | null | false | 320 | false | Datatang/multi_language | 2022-06-24T09:45:56.000Z | null | false | 66ac3d01e6ded0147dddaa279b57097149497dc6 | [] | [] | https://huggingface.co/datasets/Datatang/multi_language/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for multi_language
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported ... |
Datatang | null | null | null | false | 323 | false | Datatang/multi_language_conversation | 2022-06-24T09:46:13.000Z | null | false | 045455c29adcdcf6f703786da9d45ad9eba242e2 | [] | [] | https://huggingface.co/datasets/Datatang/multi_language_conversation/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for multi_language_conversation
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
... |
DelgadoPanadero | null | null | null | false | 332 | false | DelgadoPanadero/Pokemon | 2022-01-03T10:10:40.000Z | null | false | 89349b4f6b2b3dc5e3b4da9a505c969421da3e6c | [] | [] | https://huggingface.co/datasets/DelgadoPanadero/Pokemon/resolve/main/README.md | # Pokemon Dataset
This dataset contains a text representation of more that 10k pokemon sprites from different pokemon videogames (red, yellow, gold, ruby,...). The original images are from 40 to 96 pixel of size and every pixel is represented with an ASCII character depending to its color.
# Supported Tasks
* Text ... |
DeskDown | null | null | null | false | 319 | false | DeskDown/ALTDataset | 2022-02-13T17:03:25.000Z | null | false | 8d33120c04ada67489ab862d4a8e1438a1114316 | [] | [] | https://huggingface.co/datasets/DeskDown/ALTDataset/resolve/main/README.md | # Asian Language Treebank (ALT)
This is a **subset** of ALT dataset published by Riza et al.
It included following low-resource languages:
- fil
- vi
- id
- ms
- khm
- th
- hi
- my
It also includes ja and zh languages. |
DeskDown | null | null | null | false | 318 | false | DeskDown/ALTDataset_en-to-fil-vi-id-ms-ja-khm | 2022-01-03T22:31:36.000Z | null | false | 27aeb98712ca9cded7d7fadd0027afdbe4f22746 | [] | [] | https://huggingface.co/datasets/DeskDown/ALTDataset_en-to-fil-vi-id-ms-ja-khm/resolve/main/README.md | __Introduction__
The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques
through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY
as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016... |
DiFronzo | null | null | null | false | 320 | false | DiFronzo/Human_Activity_Recognition | 2022-02-08T11:18:07.000Z | null | false | 578af74e6d9abf50e091ad2292a79dda85998e0f | [] | [] | https://huggingface.co/datasets/DiFronzo/Human_Activity_Recognition/resolve/main/README.md | Human Activity Recognition (HAR) using smartphones dataset. Classifying the type of movement amongst five categories:
- WALKING,
- WALKING_UPSTAIRS,
- WALKING_DOWNSTAIRS,
- SITTING,
- STANDING
The experiments have been carried out with a group of 16 volunteers within an age bracket of 19-26 years. Each person performe... |
Doohae | null | null | null | false | 320 | false | Doohae/modern_music_re | 2021-12-06T05:58:20.000Z | null | false | 54e3bc2eb96a5f8c346ca715909f717f02eba22b | [] | [] | https://huggingface.co/datasets/Doohae/modern_music_re/resolve/main/README.md | Datasets for Relation Extraction Task
Source from Wikipedia (CC-BY-2.0)
Contributors : Doohae Jung, Hyesu Kim, Bosung Kim, Isaac Park, Miwon Jeon, Dagon Lee, Jihoo Kim |
Dumiiii | null | null | null | false | 320 | false | Dumiiii/common-voice-romaniarss | 2022-01-11T11:29:09.000Z | null | false | 2933270d52e548c9efd75451f085034d145c748c | [] | [] | https://huggingface.co/datasets/Dumiiii/common-voice-romaniarss/resolve/main/README.md | This datasets consists in the last version of the common-voice-dataset for romanian language.
Also contains data from RSS (Romanian Speech Synthesis Dataset) from this site http://romaniantts.com/ |
EMBO | null | @Unpublished{
huggingface: dataset,
title = {biolang},
authors={Thomas Lemberger, EMBO},
year={2021}
} | This dataset is based on abstracts from the open access section of EuropePubMed Central to train language models in the domain of biology. | false | 794 | false | EMBO/biolang | 2022-07-20T07:01:04.000Z | null | false | b85cac37ad447319a91ea886aa86b38aa9c00a14 | [] | [
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:n>1M",
"task_ids:language-modeling"
] | https://huggingface.co/datasets/EMBO/biolang/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n>1M
source_datasets: []
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
# Dataset Card for BioLang
## Table of Contents
- [D... |
EMBO | null | @Unpublished{
huggingface: dataset,
title = {SourceData NLP},
authors={Thomas Lemberger, EMBO},
year={2021}
} | This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain. | false | 964 | false | EMBO/sd-nlp | 2022-10-21T15:34:09.000Z | null | false | 57e2a4a23b36d518584a1f1266a4d6ad3348b8a5 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:named-entity-recognition",
"task_i... | https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
- structure-prediction
- text-classification
task_ids:
- multi-class-clas... |
Emanuel | null | null | null | false | 319 | false | Emanuel/UD_Portuguese-Bosque | 2022-10-25T08:54:18.000Z | null | false | e74389b1a35970f7fe695080919cf801d90e54cd | [] | [
"language:pt"
] | https://huggingface.co/datasets/Emanuel/UD_Portuguese-Bosque/resolve/main/README.md | ---
language:
- pt
---
# AutoNLP Dataset for project: pos-tag-bosque
## Table of content
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
## Datase... |
Emma121 | null | null | null | false | 10 | false | Emma121/aaaaa | 2022-02-24T14:29:51.000Z | null | false | bf65eded17cf05019710d65fdfb3d0c0b5a31729 | [] | [
"license:bsd-3-clause-clear"
] | https://huggingface.co/datasets/Emma121/aaaaa/resolve/main/README.md | ---
license: bsd-3-clause-clear
---
|
Emon | null | null | null | false | 163 | false | Emon/sobuj | 2021-08-19T08:07:52.000Z | null | false | 111f199cb7527ca797e2e1cfcef282da5e21ff03 | [] | [] | https://huggingface.co/datasets/Emon/sobuj/resolve/main/README.md | Pacquiao VS Ugas Live
https://www.graphicartsmedia.com/advert/live-free-pacquiao-vs-ugas-manny-vs-pacquiao-live-streams-21-august-2021-yordenis-ugas-vs-manny-pacquiao-live-stream-free/ |
Exr0n | null | null | null | false | 1,107 | false | Exr0n/wiki-entity-similarity | 2022-08-19T18:51:04.000Z | null | false | cbc67fdf71a5181de1aae304d98335276f236144 | [] | [
"arxiv:2004.04906",
"arxiv:2202.13581",
"annotations_creators:found",
"language:en",
"language_creators:found",
"license:mit",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"tags:named entities",
"tags:similarity",
"tags:paraphrasing",
"tags:synonym... | https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/README.md | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: 'Wiki Entity Similarity
'
size_categories:
- 10M<n<100M
source_datasets:
- original
tags:
- named entities
- similarity
- paraphrasing
- synonyms
- wikipedia
task_categories: []
task... |
Eymen3455 | null | null | null | false | 162 | false | Eymen3455/xsum_tr | 2021-02-25T11:32:10.000Z | null | false | 5fe4b51b75e3979056b483f93922c3e5f6939065 | [] | [] | https://huggingface.co/datasets/Eymen3455/xsum_tr/resolve/main/README.md | |
FIG-Loneliness | null | null | null | false | 320 | false | FIG-Loneliness/FIG-Loneliness | 2022-07-14T23:14:43.000Z | null | false | 1d89c0235a300f314ddb4fd33d779d57eb24b63c | [] | [] | https://huggingface.co/datasets/FIG-Loneliness/FIG-Loneliness/resolve/main/README.md | # Dataset Card for FIG-Loneliness
## 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)
- [Dataset Structure](#dataset-str... |
Felix-ML | null | @inproceedings{muzny2017two,
title={A two-stage sieve approach for quote attribution},
author={Muzny, Grace and Fang, Michael and Chang, Angel and Jurafsky, Dan},
booktitle={Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
pages... | This dataset is a representation of Muzny et al.'s QuoteLi3 dataset as a Huggingface dataset. It can be best used for
quote attribution. | false | 162 | false | Felix-ML/quoteli3 | 2022-10-25T08:54:20.000Z | null | false | a8e3cae5b222602746835ca60d9542ac1b42fc43 | [] | [
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K"
] | https://huggingface.co/datasets/Felix-ML/quoteli3/resolve/main/README.md | ---
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets: []
---
# Dataset Card for quoteli3
## Dataset Description
- **Homepage:** https://nlp.stanford.edu/~muzny/quoteli.html
- **Repository:** https://nlp.stanford.edu/~muzny/quoteli.html
- **Paper:** Muzny,... |
Finnish-NLP | null | null | null | false | 324 | false | Finnish-NLP/mc4_fi_cleaned | 2022-10-21T16:57:34.000Z | null | false | 995422a2cfafdaf9a5340a94aff16e7efe5b7846 | [] | [
"language:fi",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|mc4",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling"
] | https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned/resolve/main/README.md | ---
annotations_creators: []
language_creators: []
language:
- fi
license: []
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- extended|mc4
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: mC4 Finnish Cleaned
---
# Dataset... |
Firoj | null | @inproceedings{humaid2020,
Author = {Firoj Alam, Umair Qazi, Muhammad Imran, Ferda Ofli},
booktitle={Proceedings of the Fifteenth International AAAI Conference on Web and Social Media},
series={ICWSM~'21},
Keywords = {Social Media, Crisis Computing, Tweet Text Classification, Disaster Response},
Title = {HumAID: Human-... | The HumAID Twitter dataset consists of several thousands of manually annotated tweets that has been collected during 19 major natural disaster events including earthquakes, hurricanes, wildfires, and floods, which happened from 2016 to 2019 across different parts of the World. The annotations in the provided datasets c... | false | 317 | false | Firoj/HumAID | 2022-05-18T04:45:03.000Z | null | false | 6ae265697cb5e7d7bde15a79a51a25bae9b92758 | [] | [] | https://huggingface.co/datasets/Firoj/HumAID/resolve/main/README.md | # Dataset Card for HumAID
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#da... |
Fraser | null | @dataset{dataset,
author = {Fraser Greenlee},
year = {2021},
month = {1},
pages = {},
title = {MNIST text dataset.},
doi = {}
} | MNIST dataset adapted to a text-based representation.
This allows testing interpolation quality for Transformer-VAEs.
System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM
Works by quantising each MNIST pixel into one of 64 characters.
Every sample has an up & down version to encourage t... | false | 320 | false | Fraser/mnist-text-default | 2021-02-22T10:48:20.000Z | null | false | 79f97a8d8943cabb0127b0e97d6c25afdb6887fb | [] | [] | https://huggingface.co/datasets/Fraser/mnist-text-default/resolve/main/README.md | MNIST dataset adapted to a text-based representation.
This allows testing interpolation quality for Transformer-VAEs.
System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM
Works by quantising each MNIST pixel into one of 64 characters.
Every sample has an up & down version to encourage t... |
Fraser | null | @dataset{dataset,
author = {Fraser Greenlee},
year = {2021},
month = {1},
pages = {},
title = {MNIST small text dataset.},
doi = {}
} | MNIST dataset adapted to a text-based representation.
*Modified images to be ~1/4 the original area.*
Done by taking a max pool.
This allows testing interpolation quality for Transformer-VAEs.
System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM
Works by quantising each MNIST pixel int... | false | 319 | false | Fraser/mnist-text-small | 2021-02-22T10:21:37.000Z | null | false | da9c9262c1b62f55a948a194cba107448a7575c1 | [] | [] | https://huggingface.co/datasets/Fraser/mnist-text-small/resolve/main/README.md | MNIST dataset adapted to a text-based representation.
Modified images to be ~1/4 the original area.
Done by taking a max pool.
This allows testing interpolation quality for Transformer-VAEs.
System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM
Works by quantising each MNIST pixel into ... |
Fraser | null | null | null | false | 319 | false | Fraser/dream-coder | 2022-04-25T10:49:02.000Z | null | false | cd3a8930eecb7ea8d01fabd09353e70121223176 | [] | [
"language:en",
"thumbnail:https://huggingface.co/datasets/Fraser/dream-coder/resolve/main/img.png",
"tags:program-synthesis",
"license:mit",
"datasets:program-synthesis"
] | https://huggingface.co/datasets/Fraser/dream-coder/resolve/main/README.md | ---
language:
- en
thumbnail: "https://huggingface.co/datasets/Fraser/dream-coder/resolve/main/img.png"
tags:
- program-synthesis
license: "mit"
datasets:
- program-synthesis
---
# Program Synthesis Data
Generated program synthesis datasets used to train [dreamcoder](https://github.com/ellisk42/ec).
Currently just... |
Fraser | null | @dataset{dataset,
author = {Fraser Greenlee},
year = {2020},
month = {12},
pages = {},
title = {Python single line dataset.},
doi = {}
} | Dataset of single lines of Python code taken from the [CodeSearchNet](https://github.com/github/CodeSearchNet) dataset.
Context
This dataset allows checking the validity of Variational-Autoencoder latent spaces by testing what percentage of random/intermediate latent points can be greedily decoded into valid Python c... | false | 320 | false | Fraser/python-lines | 2021-02-22T10:20:34.000Z | null | false | c1dd899291e00d83b4eecc9b1e02ae64b809ee2c | [] | [] | https://huggingface.co/datasets/Fraser/python-lines/resolve/main/README.md | Dataset of single lines of Python code taken from the [CodeSearchNet](https://github.com/github/CodeSearchNet) dataset.
Context
This dataset allows checking the validity of Variational-Autoencoder latent spaces by testing what percentage of random/intermediate latent points can be greedily decoded into valid Python c... |
Fraser | null | null | Python state changes from a single line of code. | false | 642 | false | Fraser/python-state-changes | 2022-10-11T17:04:35.000Z | null | false | ef06b5d8cf560595e3812cff361f8c9be35714cd | [] | [
"language:code"
] | https://huggingface.co/datasets/Fraser/python-state-changes/resolve/main/README.md | ---
language:
- code
---
# Python State Changes
State changes from the execution of single lines of Python code.
All code was taken from Python HackerRank solutions.
Scraped from my dataset of traced HackerRank solutions. https://www.kaggle.com/frasergreenlee/ran-hackerrank-solutions
```json
{"start": "g = 100; i =... |
Fraser | null | null | Copy of [Kaggle dataset](https://www.kaggle.com/abhinavmoudgil95/short-jokes), adding to Huggingface for ease of use.
Description from Kaggle:
Context
Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generat... | false | 336 | false | Fraser/short-jokes | 2021-02-24T08:31:31.000Z | null | false | 114769d1463bf9e45744be2b729b39dd06ded2c1 | [] | [] | https://huggingface.co/datasets/Fraser/short-jokes/resolve/main/README.md | Copy of [Kaggle dataset](https://www.kaggle.com/abhinavmoudgil95/short-jokes), adding to Huggingface for ease of use.
Description from Kaggle:
Context
Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generat... |
Fraser | null | null | null | false | 323 | false | Fraser/wiki_sentences | 2021-07-21T07:43:08.000Z | null | false | 7e4b5aadfd65fc31b5b0dd50f94f0857e040f0b1 | [] | [] | https://huggingface.co/datasets/Fraser/wiki_sentences/resolve/main/README.md | # Wiki Sentences
A dataset of all english sentences in Wikipedia.
Taken from the OPTIMUS project. https://github.com/ChunyuanLI/Optimus/blob/master/download_datasets.md
The dataset is 11.8GB so best to load it using streaming:
```python
from datasets import load_dataset
dataset = load_dataset("Fraser/wiki_sentences... |
GEM | null | @InProceedings{anli,
author = {Chandra, Bhagavatula and Ronan, Le Bras and Chaitanya, Malaviya and Keisuke, Sakaguchi and Ari, Holtzman
and Hannah, Rashkin and Doug, Downey and Scott, Wen-tau Yih and Yejin, Choi},
title = {Abductive Commonsense Reasoning},
year = {2020}
} | the Abductive Natural Language Generation Dataset from AI2 | false | 322 | false | GEM/ART | 2022-10-24T13:01:25.000Z | null | false | 8fbd9eb015c80542700c19c2e0d8ee023f8431f5 | [] | [
"arxiv:1908.05739",
"arxiv:1906.05317",
"annotations_creators:automatically-created",
"language_creators:unknown",
"language:en",
"license:apache-2.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"tags:reasoning"
] | https://huggingface.co/datasets/GEM/ART/resolve/main/README.md | ---
annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: ART
tags:
- reasoning
---
# Dataset Card for GEM/ART
## Dataset Descr... |
GEM | null | @inproceedings{kim-etal-2021-bisect,
title = "{B}i{SECT}: Learning to Split and Rephrase Sentences with Bitexts",
author = "Kim, Joongwon and
Maddela, Mounica and
Kriz, Reno and
Xu, Wei and
Callison-Burch, Chris",
booktitle = "Proceedings of the 2021 Conference on Empirical Metho... | BiSECT is a Split and Rephrase corpus created via bilingual pivoting. | false | 905 | false | GEM/BiSECT | 2022-09-02T21:58:17.000Z | null | false | 875b884d264ba3b0e7657432b8e963e1acefd723 | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:de",
"language:en",
"language:fr",
"language:es",
"license:other",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_ids:unknown"
] | https://huggingface.co/datasets/GEM/BiSECT/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- de
- en
- fr
- es
license:
- other
multilinguality:
- unknown
pretty_name: BiSECT
size_categories:
- unknown
source_datasets:
- original
task_categories:
- simplification
task_ids:
- unknown
---
# Dataset Card for GEM/BiSECT
## Dataset Descript... |
GEM | null | @article{zhu2020crosswoz,
author = {Qi Zhu and Kaili Huang and Zheng Zhang and Xiaoyan Zhu and Minlie Huang},
title = {Cross{WOZ}: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset},
journal = {Transactions of the Association for Computational Linguistics},
year = {2020}
} | CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and sys... | false | 337 | false | GEM/CrossWOZ | 2022-10-24T15:29:55.000Z | null | false | 0c6f57946a15c70c44b28b81ae5fad9558abae01 | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:zh",
"license:apache-2.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:conversational",
"tags:dialog-response-generation"
] | https://huggingface.co/datasets/GEM/CrossWOZ/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- zh
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: CrossWOZ
tags:
- dialog-response-generation
---
# Dataset Card for GEM/CrossWO... |
GEM | null | @inproceedings{kamal-eddine-etal-2021-barthez,
title = "{BART}hez: a Skilled Pretrained {F}rench Sequence-to-Sequence Model",
author = "Kamal Eddine, Moussa and
Tixier, Antoine and
Vazirgiannis, Michalis",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language... | The OrangeSum dataset was inspired by the XSum dataset. It was created by scraping the "Orange Actu" website: https://actu.orange.fr/. Orange S.A. is a large French multinational telecommunications corporation, with 266M customers worldwide. Scraped pages cover almost a decade from Feb 2011 to Sep 2020. They belong to ... | false | 532 | false | GEM/OrangeSum | 2022-09-03T18:26:49.000Z | null | false | a31c7e3152cc0e15151549bf4f09d5a3438093ed | [] | [
"annotations_creators:unknown",
"language_creators:unknown",
"language:fr",
"license:other",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:summarization",
"task_ids:unknown"
] | https://huggingface.co/datasets/GEM/OrangeSum/resolve/main/README.md | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- fr
license:
- other
multilinguality:
- unknown
pretty_name: OrangeSum
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids:
- unknown
---
# Dataset Card for GEM/OrangeSum
## Dataset Description
- ... |
GEM | null | @inproceedings{quan-etal-2020-risawoz,
title = "{R}i{SAWOZ}: A Large-Scale Multi-Domain {W}izard-of-{O}z Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling",
author = "Quan, Jun and
Zhang, Shian and
Cao, Qian and
Li, Zizhong and
Xiong, Deyi",
booktitle = "... | RiSAWOZ contains 11.2K human-to-human (H2H) multiturn semantically annotated dialogues, with more than 150K utterances spanning over 12 domains, which is larger than all previous annotated H2H conversational datasets.Both single- and multi-domain dialogues are constructed, accounting for 65% and 35%, respectively. | false | 322 | false | GEM/RiSAWOZ | 2022-10-24T15:30:01.000Z | null | false | d9287b21928a811281a349655655ee4be964292a | [] | [
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"language:zh",
"license:cc-by-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:conversational",
"tags:dialog-response-generation"
] | https://huggingface.co/datasets/GEM/RiSAWOZ/resolve/main/README.md | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- zh
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: RiSAWOZ
tags:
- dialog-response-generation
---
# Dataset Card for GEM/... |
GEM | null | @article{hayashi2019findings,
title={Findings of the Third Workshop on Neural Generation and Translation},
author={Hayashi, Hiroaki and Oda, Yusuke and Birch, Alexandra and Konstas, Ioannis and Finch, Andrew and Luong, Minh-Thang and Neubig, Graham and Sudoh, Katsuhito},
journal={EMNLP-IJCNLP 2019},
pages={1},
... | Dataset for the WNGT 2019 DGT shared task on "Document-Level Generation and Translation”. | false | 322 | false | GEM/RotoWire_English-German | 2022-10-24T15:30:03.000Z | null | false | 4d297ffc7cffbb280b7b4cc8dbc60ba35fa2b1b9 | [] | [
"annotations_creators:automatically-created",
"language_creators:unknown",
"language:en",
"language:de",
"license:cc-by-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:table-to-text",
"tags:data-to-text"
] | https://huggingface.co/datasets/GEM/RotoWire_English-German/resolve/main/README.md | ---
annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
- de
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: RotoWire_English-German
tags:
- data-to-text
---
# Dataset... |
GEM | null | @article{tonelli2016simpitiki,
title={SIMPITIKI: a Simplification corpus for Italian},
author={Tonelli, Sara and Aprosio, Alessio Palmero and Saltori, Francesca},
journal={Proceedings of CLiC-it},
year={2016}
} | SIMPITIKI is a Simplification corpus for Italian and it consists of two sets of simplified pairs: the first one is harvested from the Italian Wikipedia in a semi-automatic way; the second one is manually annotated sentence-by-sentence from documents in the administrative domain. | false | 165 | false | GEM/SIMPITIKI | 2022-10-24T15:30:05.000Z | null | false | 399cf2a6baa63c6f96a57b464f89023f19d046f2 | [] | [
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"language:it",
"license:cc-by-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:text2text-generation",
"task_ids:text-simplification"
] | https://huggingface.co/datasets/GEM/SIMPITIKI/resolve/main/README.md | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- it
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: SIMPITIKI
---
# Dataset Card for GEM/SIMPITIK... |
GEM | null | @inproceedings{sun-etal-2021-d2s,
title = "{D}2{S}: Document-to-Slide Generation Via Query-Based Text Summarization",
author = "Sun, Edward and
Hou, Yufang and
Wang, Dakuo and
Zhang, Yunfeng and
Wang, Nancy X. R.",
booktitle = "Proceedings of the 2021 Conference of the North Amer... | SciDuet is the first publicaly available dataset for the challenging task of document2slides generation,
The dataset integrated into GEM is the ACL portion of the whole dataset described in "https://aclanthology.org/2021.naacl-main.111.pdf".
It contains the full Dev and Test sets, and a portion of the Train dataset.
W... | false | 323 | false | GEM/SciDuet | 2022-10-24T15:30:06.000Z | null | false | 23c4a628af8312f25fe40efcc094d6502d1198e8 | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:apache-2.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"tags:text-to-slide"
] | https://huggingface.co/datasets/GEM/SciDuet/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: SciDuet
tags:
- text-to-slide
---
# Dataset Card for GEM/SciDuet
## Dataset Descriptio... |
GEM | null | @article{byrne2020tickettalk,
title={TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems},
author={Byrne, Bill and Krishnamoorthi, Karthik and Ganesh, Saravanan and Kale, Mihir Sanjay},
journal={arXiv preprint arXiv:2012.12458},
year={2020}
} | The Taskmaster-3 (aka TicketTalk) dataset consists of 23,789 movie ticketing dialogs
(located in Taskmaster/TM-3-2020/data/). By "movie ticketing" we mean conversations
where the customer's goal is to purchase tickets after deciding on theater, time,
movie name, number of tickets, and date, or opt out of the transactio... | false | 321 | false | GEM/Taskmaster | 2022-10-24T15:30:09.000Z | null | false | 2298950c4ca70c9fdf8c34e4129b998704f4429a | [] | [
"arxiv:2012.12458",
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:cc-by-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:conversational",
"tags:dialog-response-generation"
] | https://huggingface.co/datasets/GEM/Taskmaster/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: Taskmaster
tags:
- dialog-response-generation
---
# Dataset Card for GEM/Taskma... |
GEM | null | @inproceedings{devaraj-etal-2021-paragraph,
title = "Paragraph-level Simplification of Medical Texts",
author = "Devaraj, Ashwin and
Marshall, Iain and
Wallace, Byron and
Li, Junyi Jessy",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association f... | This dataset measures the ability for a model to simplify paragraphs of medical text through the omission non-salient information and simplification of medical jargon. | false | 321 | false | GEM/cochrane-simplification | 2022-10-24T15:30:10.000Z | null | false | 75a92ae445171fa1b7641a229bfe3c77c0d8723d | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:cc-by-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:text2text-generation",
"task_ids:text-simplification"
] | https://huggingface.co/datasets/GEM/cochrane-simplification/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: cochrane-simplification
---
# Dataset Card for GEM/coc... |
GEM | null | @inproceedings{lin-etal-2020-commongen,
title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
author = "Lin, Bill Yuchen and
Zhou, Wangchunshu and
Shen, Ming and
Zhou, Pei and
Bhagavatula, Chandra and
Choi, Yejin and
Ren,... | CommonGen is a constrained text generation task, associated with a benchmark
dataset, to explicitly test machines for the ability of generative commonsense
reasoning. Given a set of common concepts; the task is to generate a coherent
sentence describing an everyday scenario using these concepts. | false | 351 | false | GEM/common_gen | 2022-10-24T15:30:11.000Z | null | false | 586b0f50565225fbc748b0001a992d1672d62440 | [] | [
"arxiv:1911.03705",
"arxiv:1910.13461",
"arxiv:2009.12677",
"arxiv:2012.00366",
"arxiv:1910.10683",
"arxiv:2006.08315",
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:mit",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
... | https://huggingface.co/datasets/GEM/common_gen/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- mit
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: common_gen
tags:
- reasoning
---
# Dataset Card for GEM/common_gen
## Dataset Description
- ... |
GEM | null | @inproceedings{balakrishnan-etal-2019-constrained,
title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
author = "Balakrishnan, Anusha and
Rao, Jinfeng and
Upasani, Kartikeya and
White, Michael and
Subba, Rajen",
booktitle = "Proceedings... | The Conversational Weather dataset is designed for generation of responses to weather queries based on a structured input data. The input allows specifying data attributes such as dates, times, locations, weather conditions, and errors, and also offers control over structure of response through discourse relations such... | false | 476 | false | GEM/conversational_weather | 2022-10-24T15:30:13.000Z | null | false | 3cbe8a7f0b4e42f42e76c1922ef43e142cf51b78 | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:cc-by-nc-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:table-to-text",
"tags:data-to-text"
] | https://huggingface.co/datasets/GEM/conversational_weather/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: conversational_weather
tags:
- data-to-text
---
# Dataset Card for GEM/conver... |
GEM | null | @inproceedings{cs_restaurants,
address = {Tokyo, Japan},
title = {Neural {Generation} for {Czech}: {Data} and {Baselines}},
shorttitle = {Neural {Generation} for {Czech}},
url = {https://www.aclweb.org/anthology/W19-8670/},
urldate = {2019-10-18},
booktitle = {Proceedings of the 12th {International} {Conference} ... | The task is generating responses in the context of a (hypothetical) dialogue
system that provides information about restaurants. The input is a basic
intent/dialogue act type and a list of slots (attributes) and their values.
The output is a natural language sentence. | false | 328 | false | GEM/cs_restaurants | 2022-10-24T15:30:14.000Z | null | false | e9cd3c2f515a919d0ca0734c4711e2f849c82036 | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:cs",
"license:cc-by-sa-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:conversational",
"tags:dialog-response-generation"
] | https://huggingface.co/datasets/GEM/cs_restaurants/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- cs
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: cs_restaurants
tags:
- dialog-response-generation
---
# Dataset Card for GEM... |
GEM | null | @inproceedings{nan-etal-2021-dart,
title = "{DART}: Open-Domain Structured Data Record to Text Generation",
author = "Nan, Linyong and
Radev, Dragomir and
Zhang, Rui and
Rau, Amrit and
Sivaprasad, Abhinand and
Hsieh, Chiachun and
Tang, Xiangru and
Vyas, Aadit an... | DART is a large and open-domain structured DAta Record to Text generation corpus
with high-quality sentence annotations with each input being a set of
entity-relation triples following a tree-structured ontology. It consists of
82191 examples across different domains with each input being a semantic RDF
triple set deri... | false | 1,605 | false | GEM/dart | 2022-10-24T15:30:16.000Z | null | false | 0e97cc8d6efa6858ae6a510a2a65a37271ba1309 | [] | [
"arxiv:1910.13461",
"arxiv:1908.09022",
"arxiv:2007.02871",
"arxiv:1709.00103",
"arxiv:1706.09254",
"arxiv:1810.01170",
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:mit",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
... | https://huggingface.co/datasets/GEM/dart/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- mit
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: dart
tags:
- data-to-text
---
# Dataset Card for GEM/dart
## Dataset Description
- *... |
GEM | null | @article{kim2020domain,
title={Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access},
author={Seokhwan Kim and Mihail Eric and Karthik Gopalakrishnan and Behnam Hedayatnia and Yang Liu and Dilek Hakkani-Tur},
journal={arXiv preprint arXiv:2006.03533}
year={2020}
} | \ | false | 322 | false | GEM/dstc10_track2_task2 | 2022-10-24T15:30:17.000Z | null | false | b36aa581cacc373d1f79c6be6cf757df67d9a8db | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:apache-2.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:conversational",
"tags:dialog-response-generation"
] | https://huggingface.co/datasets/GEM/dstc10_track2_task2/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: dstc10_track2_task2
tags:
- dialog-response-generation
---
# Dataset Card for ... |
GEM | null | @inproceedings{e2e_cleaned,
address = {Tokyo, Japan},
title = {Semantic {Noise} {Matters} for {Neural} {Natural} {Language} {Generation}},
url = {https://www.aclweb.org/anthology/W19-8652/},
booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
autho... | The E2E dataset is designed for a limited-domain data-to-text task --
generation of restaurant descriptions/recommendations based on up to 8 different
attributes (name, area, price range etc.). | false | 411 | false | GEM/e2e_nlg | 2022-10-24T15:30:18.000Z | null | false | 0e089c2ba61c3a0d183815c87c2c95e98fb446a6 | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:table-to-text",
"tags:data-to-text"
] | https://huggingface.co/datasets/GEM/e2e_nlg/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: e2e_nlg
tags:
- data-to-text
---
# Dataset Card for GEM/e2e_nlg
## Dataset D... |
GEM | null | @inproceedings{puduppully-etal-2019-data,
title = "Data-to-text Generation with Entity Modeling",
author = "Puduppully, Ratish and
Dong, Li and
Lapata, Mirella",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "... | The MLB dataset for data to text generation contains Major League Baseball games statistics and
their human-written summaries. | false | 322 | false | GEM/mlb_data_to_text | 2022-10-24T15:30:20.000Z | null | false | f91b5cc190443a15488ca3d54d1e32c28d90c30b | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:other",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:table-to-text",
"tags:data-to-text"
] | https://huggingface.co/datasets/GEM/mlb_data_to_text/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- other
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: mlb_data_to_text
tags:
- data-to-text
---
# Dataset Card for GEM/mlb_data_to_text
#... |
GEM | null | @article{scialom2020mlsum,
title={MLSUM: The Multilingual Summarization Corpus},
author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
journal={arXiv preprint arXiv:2004.14900},
year={2020}
} | This is the MLSUM subset of the GEM benchmark. MLSUM is the first large-scale MultiLingual SUMmarization dataset.
Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish.
Together with English newspapers from the popular C... | false | 494 | false | GEM/mlsum | 2022-10-24T15:30:21.000Z | null | false | 8a22421bf1327ac793893604a8109d65da29eabf | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:de",
"language:es",
"license:other",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:summarization"
] | https://huggingface.co/datasets/GEM/mlsum/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- de
- es
license:
- other
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: mlsum
---
# Dataset Card for GEM/mlsum
## Dataset Description
- **Homepage:**... |
GEM | null | @InProceedings{creutz:lrec2018,
title = {Open Subtitles Paraphrase Corpus for Six Languages},
author={Mathias Creutz},
booktitle={Proceedings of the 11th edition of the Language Resources
and Evaluation Conference (LREC 2018)},
year={2018},
month = {May 7-12},
address = {Miyazaki, Japan},
editor = {Nico... | Opusparcus is a paraphrase corpus for six European languages: German,
English, Finnish, French, Russian, and Swedish. The paraphrases are
extracted from the OpenSubtitles2016 corpus, which contains subtitles
from movies and TV shows. | false | 8,549 | false | GEM/opusparcus | 2022-10-24T15:30:22.000Z | null | false | 9e9b1f8ef51616073f47f306f7f47dd91663f86a | [] | [
"annotations_creators:expert-created",
"language_creators:unknown",
"language:de",
"language:en",
"language:fi",
"language:fr",
"language:ru",
"language:sv",
"license:cc-by-nc-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"... | https://huggingface.co/datasets/GEM/opusparcus/resolve/main/README.md | ---
annotations_creators:
- expert-created
language_creators:
- unknown
language:
- de
- en
- fi
- fr
- ru
- sv
license:
- cc-by-nc-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: opusparcus
tags:
- paraphrasing
---
# Dataset Card... |
GEM | null | null | null | false | 321 | false | GEM/references | 2022-06-23T19:32:57.000Z | null | false | 97e4c5cebd637f24fa1319b5fbea008b8bd7f04b | [] | [] | https://huggingface.co/datasets/GEM/references/resolve/main/README.md | # GEM References
## What is it?
This repository contains all the reference datasets that are used for running evaluation on the GEM benchmark. Some of these datasets were originally hosted as a [GitHub release](https://github.com/GEM-benchmark/GEM-metrics/releases) on the [`GEM-metrics`](https://github.com/GEM-benchm... |
GEM | null | @inproceedings{rastogi2020towards,
title={Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset},
author={Rastogi, Abhinav and Zang, Xiaoxue and Sunkara, Srinivas and Gupta, Raghav and Khaitan, Pranav},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
... | The Schema-Guided Dialogue (SGD) dataset contains 18K multi-domain task-oriented
dialogues between a human and a virtual assistant, which covers 17 domains
ranging from banks and events to media, calendar, travel, and weather. The
language presents in the datset is only English. The SGD dataset provides a
challenging t... | false | 318 | false | GEM/schema_guided_dialog | 2022-10-24T15:30:26.000Z | null | false | f272b927123ba5c2ead8db0b4437c2f8316b4704 | [] | [
"arxiv:1909.05855",
"arxiv:2004.15006",
"arxiv:2002.01359",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:conversational",
"tags:dialog... | https://huggingface.co/datasets/GEM/schema_guided_dialog/resolve/main/README.md | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: schema_guided_dialog
tags:
- dialog-response-generation
---
# Datas... |
GEM | null | @inproceedings{thomson-etal-2020-sportsett,
title = "{S}port{S}ett:Basketball - A robust and maintainable data-set for Natural Language Generation",
author = "Thomson, Craig and
Reiter, Ehud and
Sripada, Somayajulu",
booktitle = "Proceedings of the Workshop on Intelligent Information Processin... | SportSett:Basketball dataset for Data-to-Text Generation contains NBA games stats aligned with their human written summaries. | false | 356 | false | GEM/sportsett_basketball | 2022-10-24T15:30:28.000Z | null | false | 24aa031eecf0e19eb782cda91ea159de12dd131b | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:mit",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:table-to-text",
"tags:data-to-text"
] | https://huggingface.co/datasets/GEM/sportsett_basketball/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- mit
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: sportsett_basketball
tags:
- data-to-text
---
# Dataset Card for GEM/sportsett_basketb... |
GEM | null | @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}... | SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
also determine when no answer is supported by the paragraph and absta... | false | 1,267 | false | GEM/squad_v2 | 2022-10-24T15:30:29.000Z | null | false | 67199807729e631955056c71c258b7acbee548a3 | [] | [
"arxiv:1806.03822",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"tags:question-generation"
] | https://huggingface.co/datasets/GEM/squad_v2/resolve/main/README.md | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: squad_v2
tags:
- question-generation
---
# Dataset Card for GEM/squad_v2
##... |
GEM | null | null | null | false | 321 | false | GEM/surface_realisation_st_2020 | 2022-10-24T15:30:30.000Z | null | false | f294829c59e8b1aa74aaf9f672e523f0b5c0536f | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:ar",
"language:zh",
"language:en",
"language:fr",
"language:hi",
"language:id",
"language:ja",
"language:ko",
"language:pt",
"language:ru",
"language:es",
"license:cc-by-2.5",
"multilinguality:unknown",
"size_categorie... | https://huggingface.co/datasets/GEM/surface_realisation_st_2020/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- ar
- zh
- en
- fr
- hi
- id
- ja
- ko
- pt
- ru
- es
license:
- cc-by-2.5
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: surface_realisation_st_2020
tag... |
GEM | null | \@inproceedings{parikh2020totto,
title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
booktitle={Proceedings of EMNLP},
year={2020}
} | ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. | false | 320 | false | GEM/totto | 2022-10-24T15:30:32.000Z | null | false | 5e745cedfd0050cc18aa143e5325d03061941d7d | [] | [
"arxiv:1603.07771",
"arxiv:2007.02871",
"arxiv:2005.10433",
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:table-to-text",
"tags:data-to-text"
] | https://huggingface.co/datasets/GEM/totto/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: totto
tags:
- data-to-text
---
# Dataset Card for GEM/totto
## Dataset Descr... |
GEM | null | @inproceedings{kanerva2019newsgen,
Title = {Template-free Data-to-Text Generation of Finnish Sports News},
Author = {Jenna Kanerva and Samuel R{\"o}nnqvist and Riina Kekki and Tapio Salakoski and Filip Ginter},
booktitle = {Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa’19)},
y... | The Turku Hockey Data2Text corpus was developed as a benchmark for evaluating template-free, machine learning methods on Finnish news generation in the area of ice hockey reporting. This dataset is a collection of 3,454 ice hockey games, each including game statistics and a news article describing the game. Each game i... | false | 477 | false | GEM/turku_hockey_data2text | 2022-10-24T15:30:33.000Z | null | false | 25220fbd8d12efac81f22ffa0e5dc919de34dd16 | [] | [
"annotations_creators:expert-created",
"language_creators:unknown",
"language:fi",
"license:cc-by-nc-sa-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:table-to-text",
"tags:data-to-text"
] | https://huggingface.co/datasets/GEM/turku_hockey_data2text/resolve/main/README.md | ---
annotations_creators:
- expert-created
language_creators:
- unknown
language:
- fi
license:
- cc-by-nc-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: turku_hockey_data2text
tags:
- data-to-text
---
# Dataset Card f... |
GEM | null | @inproceedings{kanerva-etal-2021-finnish,
title = {Finnish Paraphrase Corpus},
author = {Kanerva, Jenna and Ginter, Filip and Chang, Li-Hsin and Rastas, Iiro and Skantsi, Valtteri and Kilpeläinen, Jemina and Kupari, Hanna-Mari and Saarni, Jenna and Sevón, Maija and Tarkka, Otto},
booktitle = {Proceedings of the 2... | Turku Paraphrase Corpus is a dataset of 104,645 manually annotated Finnish paraphrases. The vast majority of the data is classified as a paraphrase either in the given context, or universally. | false | 632 | false | GEM/turku_paraphrase_corpus | 2022-10-24T15:29:45.000Z | null | false | 0125222c79749bbe4caab3e480aa0f9373b5899e | [] | [
"annotations_creators:expert-created",
"language_creators:unknown",
"language:fi",
"license:cc-by-sa-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:other",
"tags:paraphrasing"
] | https://huggingface.co/datasets/GEM/turku_paraphrase_corpus/resolve/main/README.md | ---
annotations_creators:
- expert-created
language_creators:
- unknown
language:
- fi
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: turku_paraphrase_corpus
tags:
- paraphrasing
---
# Dataset Card for GEM/tur... |
GEM | null | @inproceedings{juraska-etal-2019-viggo,
title = "{V}i{GGO}: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation",
author = "Juraska, Juraj and
Bowden, Kevin and
Walker, Marilyn",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generatio... | ViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can al... | false | 321 | false | GEM/viggo | 2022-10-24T15:31:07.000Z | null | false | c851cd5ff2ee92f0137fcf24014e37427a2d30b7 | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:table-to-text",
"tags:data-to-text"
] | https://huggingface.co/datasets/GEM/viggo/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: viggo
tags:
- data-to-text
---
# Dataset Card for GEM/viggo
## Dataset Descr... |
GEM | null | @inproceedings{castro-ferreira20:bilin-bi-direc-webnl-shared,
title={The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)},
author={Castro Ferreira, Thiago and
Gardent, Claire and
Ilinykh, Nikolai and
van der Lee, Chris and
Mille, Simon ... | WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets
and short texts that cover about 450 different DBpedia properties. The WebNLG data
was originally created to promote the development of RDF verbalisers able to
generate short text and to handle micro-planning (i.e., sentence segmentation ... | false | 1,268 | false | GEM/web_nlg | 2022-10-24T15:31:09.000Z | null | false | 1d41f28b06efb62d39cc83a0c00b231e825720fe | [] | [
"annotations_creators:unknown",
"language_creators:unknown",
"language:en",
"license:cc-by-nc-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:table-to-text",
"tags:data-to-text"
] | https://huggingface.co/datasets/GEM/web_nlg/resolve/main/README.md | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: web_nlg
tags:
- data-to-text
---
# Dataset Card for GEM/web_nlg
## Datase... |
GEM | null | @inproceedings{jiang-etal-2020-neural,
title = "Neural {CRF} Model for Sentence Alignment in Text Simplification",
author = "Jiang, Chao and
Maddela, Mounica and
Lan, Wuwei and
Zhong, Yang and
Xu, Wei",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Co... | WikiAuto provides a set of aligned sentences from English Wikipedia and Simple
English Wikipedia as a resource to train sentence simplification systems.
The authors first crowd-sourced a set of manual alignments between sentences in
a subset of the Simple English Wikipedia and their corresponding versions in
English W... | false | 1,356 | false | GEM/wiki_auto_asset_turk | 2022-10-24T15:31:10.000Z | null | false | a7cc8c6bd2f5738386363dd48fb97dfbfd37da69 | [] | [
"arxiv:1910.02677",
"arxiv:2005.00352",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"language:en",
"license:other",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:text2text-generation",
"task_ids:text-simplification"
] | https://huggingface.co/datasets/GEM/wiki_auto_asset_turk/resolve/main/README.md | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- en
license:
- other
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: wiki_auto_asset_turk
---
# Dataset Card for GEM/w... |
GEM | null | @inproceedings{perez2019generating,
title={Generating Summaries with Topic Templates and Structured Convolutional Decoders},
author={Perez-Beltrachini, Laura and Liu, Yang and Lapata, Mirella},
booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
pages={5107--5116... | Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents. | false | 729 | false | GEM/wiki_cat_sum | 2022-10-24T15:31:11.000Z | null | false | e732d1703eaad5b34a56370fd137b9d09921a94b | [] | [
"arxiv:1906.04687",
"arxiv:1801.10198",
"arxiv:2009.07032",
"annotations_creators:automatically-created",
"language_creators:unknown",
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:summarization"
] | https://huggingface.co/datasets/GEM/wiki_cat_sum/resolve/main/README.md | ---
annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: wiki_cat_sum
---
# Dataset Card for GEM/wiki_cat_sum
## Dat... |
GEM | null | @article{ladhak-wiki-2020,
title = {WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization},
authors = {Faisal Ladhak, Esin Durmus, Claire Cardie and Kathleen McKeown},
journal = {arXiv preprint arXiv:2010.03093},
year = {2020},
url = {https://arxiv.org/abs/2010.03093}
} | WikiLingua is a large-scale multilingual dataset for the evaluation of
crosslingual abstractive summarization systems. The dataset includes ~770k
article and summary pairs in 18 languages from WikiHow. The gold-standard
article-summary alignments across languages was done by aligning the images
that are used to describ... | false | 54,498 | false | GEM/wiki_lingua | 2022-10-24T15:31:13.000Z | null | false | 9d5b7d8d1aa0912edf6a112d889b34f493a3c5b0 | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:ar",
"language:cs",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:id",
"language:it",
"language:ja",
"language:ko",
"language:nl",
"language:pt",
"language:ru",
"language:th",
... | https://huggingface.co/datasets/GEM/wiki_lingua/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- ar
- cs
- de
- en
- es
- fr
- hi
- id
- it
- ja
- ko
- nl
- pt
- ru
- th
- tr
- vi
- zh
license:
- cc-by-3.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pret... |
GEM | null | @inproceedings{hasan-etal-2021-xl,
title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Islam, Md. Saiful and
Mubasshir, Kazi and
Li, Yuan-Fang and
Kang, Yong-Bin and
Rahman, M. Soh... | We present XLSum, a comprehensive and diverse dataset comprising 1.35 million professionally
annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics.
The dataset covers 45 languages ranging from low to high-resource, for many of which no
public dataset is currently available. XL... | false | 7,336 | false | GEM/xlsum | 2022-10-24T15:31:33.000Z | null | false | b276583480e84c2cf2a17b306f0d1d1ccec546e3 | [] | [
"arxiv:1607.01759",
"annotations_creators:none",
"language_creators:unknown",
"language:und",
"license:cc-by-nc-sa-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:summarization"
] | https://huggingface.co/datasets/GEM/xlsum/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- und
license:
- cc-by-nc-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: xlsum
---
# Dataset Card for GEM/xlsum
## Dataset Description
- **Homep... |
GEM | null | @inproceedings{narayan-etal-2018-dont,
title = "Don{'}t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization",
author = "Narayan, Shashi and
Cohen, Shay B. and
Lapata, Mirella",
booktitle = "Proceedings of the 2018 Conference on Empirical M... | This is the XSUM subset of the GEM benchmark. | false | 8,761 | false | GEM/xsum | 2022-10-24T15:31:30.000Z | null | false | 46dd444dde879b9ae7770f23d0d5496c4281da8e | [] | [
"annotations_creators:none",
"language_creators:unknown",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"task_categories:summarization"
] | https://huggingface.co/datasets/GEM/xsum/resolve/main/README.md | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: xsum
---
# Dataset Card for GEM/xsum
## Dataset Description
- **Homepage:**... |
GEM-submissions | null | null | null | false | 319 | false | GEM-submissions/GEM__bart_base_schema_guided_dialog__1645547915 | 2022-02-22T16:38:38.000Z | null | false | 2652b476ad41e11512d3f377cc5f7a5be04daffe | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:BART_BASE_schema_guided_dialog"
] | https://huggingface.co/datasets/GEM-submissions/GEM__bart_base_schema_guided_dialog__1645547915/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: BART_BASE_schema_guided_dialog
---
|
GEM-submissions | null | null | null | false | 18 | false | GEM-submissions/Leo__bart-large__1645784880 | 2022-02-25T10:28:03.000Z | null | false | bb821ee030599de62c897ae4aa414a5d1b8a94fb | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:bart-large"
] | https://huggingface.co/datasets/GEM-submissions/Leo__bart-large__1645784880/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: bart-large
---
|
GEM-submissions | null | null | null | false | 20 | false | GEM-submissions/Leo__mbart-large-cc25__1645802644 | 2022-02-25T15:24:07.000Z | null | false | aea82ab50578224e35d77f61df1ce6539a04d525 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:mbart-large-cc25"
] | https://huggingface.co/datasets/GEM-submissions/Leo__mbart-large-cc25__1645802644/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: mbart-large-cc25
---
|
GEM-submissions | null | null | null | false | 319 | false | GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645558682 | 2022-02-22T19:38:08.000Z | null | false | 3162f569ba2d639018f912b3f7692823d3b1148a | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Hugging Face test T5-base.outputs.json 36bf2a59"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645558682/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
|
GEM-submissions | null | null | null | false | 319 | false | GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645559101 | 2022-02-22T19:45:04.000Z | null | false | 3b0d72d19fba40c0727b4c079aa3bda0fec7df73 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Hugging Face test T5-base.outputs.json 36bf2a59"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645559101/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
|
GEM-submissions | null | null | null | false | 19 | false | GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645800191 | 2022-02-25T14:43:14.000Z | null | false | cf294c60f19da32d9e2176e3fa49e15c6b424371 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Hugging Face test T5-base.outputs.json 36bf2a59"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645800191/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
|
GEM-submissions | null | null | null | false | 19 | false | GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049378 | 2022-02-28T11:56:24.000Z | null | false | d5a99f3c5b7ebf8761eead10467cb71b1f585788 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Hugging Face test T5-base.outputs.json 36bf2a59"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049378/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
|
GEM-submissions | null | null | null | false | 19 | false | GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049424 | 2022-02-28T11:57:10.000Z | null | false | 3ff9f774a842be955c9778c958888ef84f41e133 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Hugging Face test T5-base.outputs.json 36bf2a59"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049424/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
|
GEM-submissions | null | null | null | false | 20 | false | GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049601 | 2022-02-28T12:00:08.000Z | null | false | 82ae005198ca1ff242991f4cb92b34f7cb6ff75d | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Hugging Face test T5-base.outputs.json 36bf2a59"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049601/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
|
GEM-submissions | null | null | null | false | 19 | false | GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049876 | 2022-02-28T12:04:41.000Z | null | false | 2e3ef0f26379d9834b9a781e8f818b4a80bd7661 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Hugging Face test T5-base.outputs.json 36bf2a59"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646049876/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
|
GEM-submissions | null | null | null | false | 18 | false | GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646050898 | 2022-02-28T12:21:46.000Z | null | false | 29f32802e91739ac57320dab41d2ecc3e7e54c0d | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Hugging Face test T5-base.outputs.json 36bf2a59"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646050898/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
|
GEM-submissions | null | null | null | false | 19 | false | GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646051364 | 2022-02-28T12:29:31.000Z | null | false | feff335cba3d911b20577b49b96939bc734bb51e | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Hugging Face test T5-base.outputs.json 36bf2a59"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646051364/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
|
GEM-submissions | null | null | null | false | 20 | false | GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646052073 | 2022-02-28T12:41:18.000Z | null | false | 73dec4f4060c6d823f4b7a1d301975ad3a2deeee | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Hugging Face test T5-base.outputs.json 36bf2a59",
"tags:evaluation",
"tags:benchmark"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646052073/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: Hugging Face test T5-base.outputs.json 36bf2a59
|
GEM-submissions | null | null | null | false | 20 | false | GEM-submissions/lewtun__this-is-a-test__1646052811 | 2022-02-28T12:53:35.000Z | null | false | 352216dec1617b91422fa52efd7ae49665ea700d | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:This is a test",
"tags:evaluation",
"tags:benchmark"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__this-is-a-test__1646052811/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: This is a test
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test
|
GEM-submissions | null | null | null | false | 19 | false | GEM-submissions/lewtun__this-is-a-test__1646230987 | 2022-03-02T14:23:10.000Z | null | false | acbf7c6b792c8fc6102b7768e4d4d7cfcc028985 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:This is a test",
"tags:evaluation",
"tags:benchmark"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__this-is-a-test__1646230987/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: This is a test
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test
|
GEM-submissions | null | null | null | false | 321 | false | GEM-submissions/ratishsp | 2022-02-11T16:04:09.000Z | null | false | d98b4675a0211bea520e386f012d8e379405007f | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:Template"
] | https://huggingface.co/datasets/GEM-submissions/ratishsp/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: Template
---
|
Gabriel | null | null | null | false | 321 | false | Gabriel/quora_swe | 2022-10-22T09:39:38.000Z | null | false | 3412ec62b847f65f3cafe01bdf59c8ca4d25580c | [] | [
"language:sv",
"license:mit",
"size_categories:10K<n<100K",
"task_categories:text-retrieval",
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"tags:question-pairing",
"tags:semantic-search"
] | https://huggingface.co/datasets/Gabriel/quora_swe/resolve/main/README.md | ---
language:
- sv
license:
- mit
size_categories:
- 10K<n<100K
task_categories:
- text-retrieval
- text-classification
task_ids:
- semantic-similarity-classification
tags:
- question-pairing
- semantic-search
---
# Dataset Card for "quora_swe"
The dataset quora_swe is a subset of the automatically translated (MNT) S... |
Gauravadlakha1509 | null | null | null | false | 319 | false | Gauravadlakha1509/new_one | 2021-09-20T06:39:46.000Z | null | false | ab65249db39707bddc4543cbb99544c0b935ad11 | [] | [] | https://huggingface.co/datasets/Gauravadlakha1509/new_one/resolve/main/README.md | test |
GonzaloA | null | null | null | false | 387 | false | GonzaloA/fake_news | 2022-07-04T18:09:58.000Z | null | false | d653ddbf8eecee268bf6bc6e2fb2d0433704fedf | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:30k<n<50k",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:fact-checking",
"task_ids:intent-classification"
] | https://huggingface.co/datasets/GonzaloA/fake_news/resolve/main/README.md | TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 30k<n<50k
source_dataset... |
Graphcore | null | @inproceedings{hudson2019gqa,
title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
author={Hudson, Drew A and Manning, Christopher D},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={6700--6709},
year={2019}
} | GQA is a new dataset for real-world visual reasoning and compositional question answering,
seeking to address key shortcomings of previous visual question answering (VQA) datasets. | false | 323 | false | Graphcore/gqa-lxmert | 2022-10-25T08:59:20.000Z | null | false | fb1f36b705567f8169a74b417ec90e4faacaf962 | [] | [
"language:en",
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Graphcore/gqa-lxmert/resolve/main/README.md | ---
language:
- en
license:
- cc-by-4.0
---
|
Graphcore | null | @inproceedings{hudson2019gqa,
title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
author={Hudson, Drew A and Manning, Christopher D},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={6700--6709},
year={2019}
} | GQA is a new dataset for real-world visual reasoning and compositional question answering,
seeking to address key shortcomings of previous visual question answering (VQA) datasets. | false | 325 | false | Graphcore/gqa | 2022-10-25T08:59:27.000Z | null | false | fc749b6b8c48e41fd00e323bdee4f56ae49e701c | [] | [
"language:en",
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Graphcore/gqa/resolve/main/README.md | ---
language:
- en
license:
- cc-by-4.0
---
|
Graphcore | null | @inproceedings{antol2015vqa,
title={Vqa: Visual question answering},
author={Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C Lawrence and Parikh, Devi},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={2425--243... | VQA is a new dataset containing open-ended questions about images.
These questions require an understanding of vision, language and commonsense knowledge to answer. | false | 321 | false | Graphcore/vqa-lxmert | 2022-10-25T08:59:34.000Z | null | false | 07d470e8413557334a011dca15132bccca77b660 | [] | [
"language:en",
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Graphcore/vqa-lxmert/resolve/main/README.md | ---
language:
- en
license:
- cc-by-4.0
---
|
Graphcore | null | @inproceedings{antol2015vqa,
title={Vqa: Visual question answering},
author={Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C Lawrence and Parikh, Devi},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={2425--243... | VQA is a new dataset containing open-ended questions about images.
These questions require an understanding of vision, language and commonsense knowledge to answer. | false | 325 | false | Graphcore/vqa | 2022-10-25T08:41:02.000Z | null | false | fbc7f35b80f500333eefb764b31f643c299674fd | [] | [
"language:en",
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Graphcore/vqa/resolve/main/README.md | ---
language:
- en
license:
- cc-by-4.0
---
|
Graphcore | null | null | null | false | 450 | false | Graphcore/wikipedia-bert-128 | 2022-09-07T14:42:32.000Z | null | false | d5e4c9b09eccf298c2d90f27d360a459e48ba344 | [] | [
"language:en",
"license:cc-by-sa-3.0"
] | https://huggingface.co/datasets/Graphcore/wikipedia-bert-128/resolve/main/README.md | ---
language:
- en
license:
- cc-by-sa-3.0
--- |
Graphcore | null | null | null | false | 333 | false | Graphcore/wikipedia-bert-512 | 2022-09-07T14:43:02.000Z | null | false | 6beac0eaa412cabb4b8dba22df241683da1d9921 | [] | [
"language:en",
"license:cc-by-sa-3.0"
] | https://huggingface.co/datasets/Graphcore/wikipedia-bert-512/resolve/main/README.md | ---
language:
- en
license:
- cc-by-sa-3.0
--- |
GroNLP | null | No citation information available. | This dataset contains a sample of sentences taken from the FLORES-101 dataset that were either translated
from scratch or post-edited from an existing automatic translation by three human translators.
Translation were performed for the English-Italian language pair, and translators' behavioral data
(keystrokes, paus... | false | 320 | false | GroNLP/ik-nlp-22_pestyle | 2022-10-25T09:06:27.000Z | null | false | 50f82b9244b61d9e9ec68dea1e93669ce5ee617e | [] | [
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"language:it",
"license:other",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/GroNLP/ik-nlp-22_pestyle/resolve/main/README.md | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
language:
- en
- it
license:
- other
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
pretty_name: iknlp22-pestyle
---
# Dataset Card for IK-NLP-22 Project 1:... |
GroNLP | null | @book{slp3ed-iknlp2022,
author = {Jurafsky, Daniel and Martin, James},
year = {2021},
month = {12},
pages = {1--235, 1--19},
title = {Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition},
volume = {3}
} | Paragraphs from the Speech and Language Processing book (3ed) by Jurafsky and Martin extracted semi-automatically
from Chapters 2 to 11 of the original book draft. | false | 484 | false | GroNLP/ik-nlp-22_slp | 2022-10-23T09:00:48.000Z | null | false | af8696751d437bda514cd7fa85da6266472d961b | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-retri... | https://huggingface.co/datasets/GroNLP/ik-nlp-22_slp/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
- summarization
- text-retrieval
pretty_name: slp3ed-iknlp2022
tags:
- questio... |
GroNLP | null | @incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. L... | The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
include human-annotated natural language explanations of the entailment
relations. This version includes an automatic translation to Dutch and two quality estimation annotations
for each translated field. | false | 322 | false | GroNLP/ik-nlp-22_transqe | 2022-10-21T08:06:50.000Z | null | false | a632c66397917c1494b0bb090e2b2fa0b7e98868 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language:en",
"language:nl",
"license:apache-2.0",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|esnli",
"task_categories:text-classification... | https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- machine-generated
language:
- en
- nl
license:
- apache-2.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- extended|esnli
task_categories:
- text-classification
task_ids:
- natural-language-inference
prett... |
GroNLP | null | null | null | false | 411 | false | GroNLP/ik-nlp-22_winemag | 2022-02-13T11:03:27.000Z | null | false | 90eb39f35fc64e556fc17f06d4137a4a69ec3297 | [] | [
"license:cc-by-sa-4.0"
] | https://huggingface.co/datasets/GroNLP/ik-nlp-22_winemag/resolve/main/README.md | ---
license: cc-by-sa-4.0
---
|
HHousen | null | null | null | false | 326 | false | HHousen/ParaSCI | 2021-11-24T03:38:25.000Z | null | false | 5bbaa2ae85bfd49ac6ec872314d49a3b195a2f2b | [] | [
"arxiv:2101.08382"
] | https://huggingface.co/datasets/HHousen/ParaSCI/resolve/main/README.md | Reformatted version of the ParaSCI dataset from [ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation](https://arxiv.org/abs/2101.08382). Data retrieved from [dqxiu/ParaSCI](https://github.com/dqxiu/ParaSCI). |
HUPD | null | @InProceedings{suzgun2021:hupd,
title = {The Harvard USPTO Patent Dataset},
authors={Mirac Suzgun and Suproteem Sarkar and Luke Melas-Kyriazi and Scott Kominers and Stuart Shieber},
year={2021}
} | The Harvard USPTO Patent Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus
of English-language patent applications filed to the United States Patent and Trademark Office (USPTO)
between 2004 and 2018. With more than 4.5 million patent documents, HUPD is two to three times larger
than compara... | false | 392 | false | HUPD/hupd | 2022-10-24T15:47:30.000Z | null | false | f570a84b03663180b6034c1f7f4c15864f94385e | [] | [
"arxiv:2207.04043",
"language:en",
"license:cc-by-sa-4.0",
"task_categories:fill-mask",
"task_categories:summarization",
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:masked-language-modeling",
"task_ids:multi-class-classification",
"task_ids:topic-classi... | https://huggingface.co/datasets/HUPD/hupd/resolve/main/README.md | ---
language:
- en
license:
- cc-by-sa-4.0
task_categories:
- fill-mask
- summarization
- text-classification
- token-classification
task_ids:
- masked-language-modeling
- multi-class-classification
- topic-classification
- named-entity-recognition
pretty_name: "HUPD"
tags:
- patents
---
# Dataset Card for The Harvard... |
Hellisotherpeople | null | null | null | false | 418 | false | Hellisotherpeople/DebateSum | 2022-11-10T22:28:27.000Z | null | false | a2b38dc37778da421bd2659e5c62b56bb8350e60 | [] | [
"arxiv:2011.07251",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language:en",
"language_bcp47:en-US",
"license:mit",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-retrieval",
"task_categories:question... | https://huggingface.co/datasets/Hellisotherpeople/DebateSum/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
language_bcp47:
- en-US
license:
- mit
multilinguality:
- monolingual
pretty_name: 'DebateSum: A large-scale argument mining and summarization dataset'
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:... |
Helsinki-NLP | null | @inproceedings{tiedemann-2020-tatoeba,
title = "The {T}atoeba {T}ranslation {C}hallenge {--} {R}ealistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
publis... | The Tatoeba Translation Challenge is a multilingual data set of
machine translation benchmarks derived from user-contributed
translations collected by [Tatoeba.org](https://tatoeba.org/) and
provided as parallel corpus from [OPUS](https://opus.nlpl.eu/). This
dataset includes test and development data sorted by languag... | false | 130,516 | false | Helsinki-NLP/tatoeba_mt | 2022-10-21T15:50:25.000Z | null | false | 9635372e5421ccacda7db58e88741617867a9204 | [] | [
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"language:af",
"language:ar",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:bs",
"language:ca",
"language:ch",
"language:cs",
"language:cv",
"language:cy",
"language:da",
"l... | https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- af
- ar
- az
- be
- bg
- bn
- br
- bs
- ca
- ch
- cs
- cv
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fo
- fr
- fy
- ga
- gd
- gl
- gn
- he
- hi
- hr
- hu
- hy
- ia
- id
- ie
- io
- is
- it
- ja
- jv
- ka
- kk
- km
- ko... |
HenryAI | null | null | null | false | 323 | false | HenryAI/KerasAPIReference.txt | 2021-12-15T15:55:07.000Z | null | false | 945bea40d4783692fca28bb4fed101a57b922a2f | [] | [] | https://huggingface.co/datasets/HenryAI/KerasAPIReference.txt/resolve/main/README.md | Keras API from https://keras.io/api/ <br />
Formatted into .txt file for input to https://huggingface.co/blog/how-to-train |
HenryAI | null | null | null | false | 323 | false | HenryAI/KerasCodeExamples.txt | 2021-12-15T15:57:06.000Z | null | false | 2dd5beae4e31f10590a4860025d26edf36ac8512 | [] | [] | https://huggingface.co/datasets/HenryAI/KerasCodeExamples.txt/resolve/main/README.md | Keras Code Examples from https://keras.io/examples/ <br />
organized as .txt file for input to this HF tutorial: <br />
https://huggingface.co/blog/how-to-train |
HenryAI | null | null | null | false | 167 | false | HenryAI/KerasDeveloperGuides.txt | 2021-12-15T15:56:47.000Z | null | false | 8d270a68da1c0eedee946f5d7dc3261aab36237c | [] | [] | https://huggingface.co/datasets/HenryAI/KerasDeveloperGuides.txt/resolve/main/README.md | Keras developer guides from https://keras.io/guides/ <br />
Formatted for input to: https://huggingface.co/blog/how-to-train |
IFSTalfredoswald | null | null | null | false | 325 | false | IFSTalfredoswald/MBTI | 2021-10-25T10:40:02.000Z | null | false | 16f3d2dbe10c17fed80f76ac6f757edacce7d82d | [] | [] | https://huggingface.co/datasets/IFSTalfredoswald/MBTI/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported ... |
Iftoo95 | null | null | null | false | 166 | false | Iftoo95/Arabic_Sentiment_and_Topics | 2021-11-20T14:50:45.000Z | null | false | d858e3bf6145217b36e151636805daa733a77eb2 | [] | [] | https://huggingface.co/datasets/Iftoo95/Arabic_Sentiment_and_Topics/resolve/main/README.md | Arabic Twitter based dataset with multi-labels that contains two classes:
1. Sentiment class: classifies tweets as Positive, Negative and Neutral
2. Topic class: Classifies tweets as Politics, Business and Health |
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 | false | 436 | false | IlyaGusev/gazeta | 2022-10-21T15:52:29.000Z | gazeta | false | b0995ad62a8644be6b04a05e7ca6847f56494e90 | [] | [
"arxiv:2006.11063",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"language:ru",
"language_bcp47:ru-RU",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:orig... | https://huggingface.co/datasets/IlyaGusev/gazeta/resolve/main/README.md | ---
YAML tags:
annotations_creators:
- expert-generated
- found
language_creators:
- expert-generated
- found
language:
- ru
language_bcp47:
- ru-RU
license:
- unknown
multilinguality:
- monolingual
pretty_name: Gazeta
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conditional-text-generat... |
IlyaGusev | null | @misc{gusev2021headlinecause,
title={HeadlineCause: A Dataset of News Headlines for Detecting Casualties},
author={Ilya Gusev and Alexey Tikhonov},
year={2021},
eprint={2108.12626},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | false | 815 | false | IlyaGusev/headline_cause | 2022-07-28T10:13:45.000Z | null | false | e08a8d7b563cfc8ea9d0b5de51770105766cf219 | [] | [
"arxiv:2108.12626",
"annotations_creators:crowdsourced",
"language_creators:found",
"language:ru",
"language:en",
"license:cc0-1.0",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classificati... | https://huggingface.co/datasets/IlyaGusev/headline_cause/resolve/main/README.md | ---
YAML tags:
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ru
- en
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: HeadlineCause
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- causa... |
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