id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
albertvillanova/lm_en_dummy3 | albertvillanova | 2022-02-02T08:58:10Z | 298 | 0 | null | [
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albertvillanova/lm_en_dummy4 | albertvillanova | 2022-02-03T06:58:10Z | 298 | 0 | null | [
"region:us"
] | 2022-02-03T06:58:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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albertvillanova/tests-public-raw-jsonl | albertvillanova | 2021-07-28T15:32:34Z | 298 | 0 | null | [
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allegro/polish-question-passage-pairs | allegro | 2021-09-23T19:55:15Z | 298 | 4 | null | [
"region:us"
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allegro/summarization-polish-summaries-corpus | allegro | 2021-09-24T14:17:27Z | 298 | 2 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
alperbayram/TwitterDuygu | alperbayram | 2021-11-19T21:00:07Z | 298 | 0 | null | [
"region:us"
] | 2021-11-19T21:00:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 |
language:
- tr
# negatif 54%
# pozitif 46%
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andrepreira/outros2021 | andrepreira | 2022-02-17T21:39:43Z | 298 | 0 | null | [
"region:us"
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anechaev/med_history | anechaev | 2022-02-07T13:47:33Z | 298 | 0 | null | [
"license:mit",
"region:us"
] | 2022-02-07T13:47:33Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: mit
---
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anechaev/ru_med_history | anechaev | 2022-02-12T08:51:53Z | 298 | 1 | null | [
"region:us"
] | 2022-02-12T08:51:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Medical Histories Ru-ru
Medical Histories from Russian medical textbooks.
A text dataset with medical histories.
All dates were masked into <DATE>. | [
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anton-l/common_language | anton-l | 2022-10-21T16:20:41Z | 298 | 0 | null | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|common_voice",
"language:ar",
"language:br",
"language:ca",
"language:cnh",
"language:cs",
"language:cv",
"language:cy",
"language:de",
"language:dv",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fr",
"language:fy",
"language:ia",
"language:id",
"language:it",
"language:ja",
"language:ka",
"language:kab",
"language:ky",
"language:lv",
"language:mn",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:rm",
"language:ro",
"language:ru",
"language:rw",
"language:sah",
"language:sl",
"language:sv",
"language:ta",
"language:tr",
"language:tt",
"language:uk",
"language:zh",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-10-21T16:20:41Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: Common Language
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
- br
- ca
- cnh
- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fr
- fy
- ia
- id
- it
- ja
- ka
- kab
- ky
- lv
- mn
- mt
- nl
- pl
- pt
- rm
- ro
- ru
- rw
- sah
- sl
- sv
- ta
- tr
- tt
- uk
- zh
language_bcp47:
- ar
- br
- ca
- cnh
- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fr
- fy-NL
- ia
- id
- it
- ja
- ka
- kab
- ky
- lv
- mn
- mt
- nl
- pl
- pt
- rm-sursilv
- ro
- ru
- rw
- sah
- sl
- sv-SE
- ta
- tr
- tt
- uk
- zh-CN
- zh-HK
- zh-TW
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|common_voice
task_categories:
- speech-processing
task_ids:
- speech-classification
---
# Dataset Card for common_language
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://zenodo.org/record/5036977
- **Repository:** https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonLanguage
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
This dataset is composed of speech recordings from languages that were carefully selected from the CommonVoice database. The total duration of audio recordings is 45.1 hours (i.e., 1 hour of material for each language). The dataset has been extracted from CommonVoice to train language-id systems.
### Supported Tasks and Leaderboards
The baselines for language-id are available in the SpeechBrain toolkit (see recipes/CommonLanguage):
https://github.com/speechbrain/speechbrain
### Languages
List of included language:
```
Arabic, Basque, Breton, Catalan, Chinese_China, Chinese_Hongkong, Chinese_Taiwan, Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, French, Frisian, Georgian, German, Greek, Hakha_Chin, Indonesian, Interlingua, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Maltese, Mongolian, Persian, Polish, Portuguese, Romanian, Romansh_Sursilvan, Russian, Sakha, Slovenian, Spanish, Swedish, Tamil, Tatar, Turkish, Ukranian, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file, and its label `language`. Additional fields include `age`, `client_id`, `gender` and `sentence`.
```python
{
'client_id': 'itln_trn_sp_175',
'path': '/path/common_voice_kpd/Italian/train/itln_trn_sp_175/common_voice_it_18279446.wav',
'sentence': 'Con gli studenti è leggermente simile.',
'age': 'not_defined',
'gender': 'not_defined',
'language': 22
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`language` (`ClassLabel`): The language of the recording (see the `Languages` section above)
`sentence` (`string`): The sentence the user was prompted to speak
`age` (`string`): The age of the speaker.
`gender` (`string`): The gender of the speaker
### Data Splits
The dataset is already balanced and split into train, dev (validation) and test sets.
| Name | Train | Dev | Test |
|:---------------------------------:|:------:|:------:|:-----:|
| **# of utterances** | 177552 | 47104 | 47704 |
| **# unique speakers** | 11189 | 1297 | 1322 |
| **Total duration, hr** | 30.04 | 7.53 | 7.53 |
| **Min duration, sec** | 0.86 | 0.98 | 0.89 |
| **Mean duration, sec** | 4.87 | 4.61 | 4.55 |
| **Max duration, sec** | 21.72 | 105.67 | 29.83 |
| **Duration per language, min** | ~40 | ~10 | ~10 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
The Mongolian and Ukrainian languages are spelled as "Mangolian" and "Ukranian" in this version of the dataset.
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@dataset{ganesh_sinisetty_2021_5036977,
author = {Ganesh Sinisetty and
Pavlo Ruban and
Oleksandr Dymov and
Mirco Ravanelli},
title = {CommonLanguage},
month = jun,
year = 2021,
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5036977},
url = {https://doi.org/10.5281/zenodo.5036977}
}
```
### Contributions
Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset.
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anuragshas/lv_opus100_processed | anuragshas | 2022-02-01T09:33:15Z | 298 | 0 | null | [
"region:us"
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arjunth2001/online_privacy_qna | arjunth2001 | 2021-11-10T08:53:10Z | 298 | 2 | null | [
"region:us"
] | 2021-11-10T08:53:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Online Privacy Policy QnA Dataset
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artemis13fowl/github-issues | artemis13fowl | 2022-01-15T05:56:13Z | 298 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
aseifert/merlin | aseifert | 2022-10-21T16:21:58Z | 298 | 1 | null | [
"multilinguality:translation",
"size_categories:unknown",
"language:cz",
"language:de",
"language:it",
"region:us"
] | 2022-10-21T16:21:58Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators: []
language_creators: []
language:
- cz
- de
- it
license: []
multilinguality:
- translation
pretty_name: merlin
size_categories:
- unknown
source_datasets: []
task_categories:
- conditional-text-generation
task_ids:
- machine-translation
---
# MERLIN corpus
Project URL: https://merlin-platform.eu/C_mcorpus.php
Dataset URL: https://clarin.eurac.edu/repository/xmlui/handle/20.500.12124/6
The MERLIN corpus is a written learner corpus for Czech, German, and Italian that has been designed to illustrate the Common European Framework of Reference for Languages (CEFR) with authentic learner data. The corpus contains learner texts produced in standardized language certifications covering CEFR levels A1-C1. The MERLIN annotation scheme includes a wide range of language characteristics that provide researchers with concrete examples of learner performance and progress across multiple proficiency levels. | [
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azuur/es_corpora_parliament_processed | azuur | 2022-01-26T16:58:53Z | 298 | 0 | null | [
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azuur/gn_wiki_cleaned | azuur | 2022-02-09T17:02:12Z | 298 | 0 | null | [
"region:us"
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benjaminbeilharz/daily_dialog_w_turn_templates | benjaminbeilharz | 2022-02-26T17:54:18Z | 298 | 1 | null | [
"region:us"
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bhadresh-savani/web_split | bhadresh-savani | 2021-10-15T06:42:18Z | 298 | 1 | null | [
"region:us"
] | 2021-10-15T06:42:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Work In progress! | [
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birgermoell/sv_corpora_parliament_processed | birgermoell | 2022-01-17T15:48:56Z | 298 | 0 | null | [
"region:us"
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bobbydylan/top2k | bobbydylan | 2022-02-01T02:59:04Z | 298 | 0 | null | [
"region:us"
] | 2022-02-01T02:59:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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brunodorneles/ner | brunodorneles | 2021-11-03T13:14:46Z | 298 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bs-modeling-metadata/OSCAR_Entity_13_000 | bs-modeling-metadata | 2021-09-15T14:20:53Z | 298 | 0 | null | [
"region:us"
] | 2021-09-15T14:20:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | The dataset is in the form of a json lines file with 10,657 examples, where an example consists of text (extracted from the first 13,000 rows of OSCAR unshuffled English dataset) and metadata fields (entities).
Structure of an example.
```
{
"text": "This is exactly the sort of article to raise the profile of the club around the Midlands. Very positive and really focusses on how the club has improved over a short period of time and the bright prospects for the future \n\"Oxford Town\" - professional as always at the Birmingham Mail. Not only is Oxford a city, but Oxford United are pretty recognisable name to anyone who has ever taken even a vague interest in English football.",
"metadata": [
{
"key": "entity",
"type": "local",
"char_start_idx": 80,
"char_end_idx": 88,
"value": "Midlands"
},
{
"key": "entity",
"type": "local",
"char_start_idx": 225,
"char_end_idx": 236,
"value": "Oxford Town"
},
{
"key": "entity",
"type": "local",
"char_start_idx": 270,
"char_end_idx": 285,
"value": "Birmingham_Mail"
},
{
"key": "entity",
"type": "local",
"char_start_idx": 299,
"char_end_idx": 305,
"value": "Oxford"
},
{
"key": "entity",
"type": "local",
"char_start_idx": 318,
"char_end_idx": 331,
"value": "Oxford_United_Stars_F.C."
},
{
"key": "entity",
"type": "local",
"char_start_idx": 415,
"char_end_idx": 422,
"value": "England"
}
]
}
```
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bs-modeling-metadata/website_metadata_c4 | bs-modeling-metadata | 2021-11-24T14:04:30Z | 298 | 1 | null | [
"region:us"
] | 2021-11-24T14:04:30Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | The dataset is in the form of a json lines file with 1,20,000 examples, where an example consists of text (extracted from C4 English dataset) and metadata fields (website description extracted from Wikipedia).
Example:
```
{
"text": "US10289222B2 - Handling of touch events in a browser environment - Google Patents\nHandling of touch events in a browser environment Download PDF\nUS10289222B2\nUS10289222B2 US13/857,848 US201313857848A US10289222B2 US 10289222 B2 US10289222 B2 US 10289222B2 US 201313857848 A US201313857848 A US 201313857848A US 10289222 B2 US10289222 B2 US 10289222B2\nUS13/857,848\nUS20130222244A1 (en\nEli Joshua FIDLER\nMichael Thomas Winkler\nMatthew Nicholaos STAIKOS\nJoseph Charles MASON\n2011-01-05 Priority to US12/985,337 priority Critical patent/US8438473B2/en\n2013-04-05 Application filed by BlackBerry Ltd filed Critical BlackBerry Ltd\n2013-04-05 Priority to US13/857,848 priority patent/US10289222B2/en\n2013-06-26 Assigned to RESEARCH IN MOTION CORPORATION reassignment RESEARCH IN MOTION CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: Winkler, Michael Thomas\n2013-06-26 Assigned to RESEARCH IN MOTION LIMITED reassignment RESEARCH IN MOTION LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: Fidler, Eli Joshua, Mak, Genevieve Elizabeth, Mason, Joseph Charles, STAIKOS, MATTHEW\n2013-08-29 Publication of US20130222244A1 publication Critical patent/US20130222244A1/en\n2016-03-08 Assigned to RESEARCH IN MOTION LIMITED reassignment RESEARCH IN MOTION LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: RESEARCH IN MOTION CORPORATION\n2019-05-14 Publication of US10289222B2 publication Critical patent/US10289222B2/en\nHandling of touch events in a browser environment is disclosed. An example method includes, while a document is displayed on a touchscreen display of a device, detecting a touch event at the touchscreen display, and selectively processing the detected touch event using one of a default hander, a touch event handler, and a conversion to one or more mouse events, according to a touch event handling property defined for the document.\nThe present application relates generally to the processing of detected user input events in a web browser.\nComputing devices such as desktop computers are typically equipped with external pointing devices, such as a mouse, to permit cursor-based user interaction with content executing on the computer.,
"metadata": [
{
"key": "website_description",
"type": "global",
"value": "Google Patents is a search engine from Google that indexes patents and patent applications."
}
]
}
``` | [
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bstad/github-issues | bstad | 2022-01-17T18:07:38Z | 298 | 1 | null | [
"region:us"
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calvpang/github-issues | calvpang | 2022-01-27T04:11:18Z | 298 | 0 | null | [
"region:us"
] | 2022-01-27T04:11:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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cestwc/adapted-sentcomp | cestwc | 2022-01-02T17:21:39Z | 298 | 0 | null | [
"region:us"
] | 2022-01-02T17:21:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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cestwc/adapted-wordnet | cestwc | 2021-12-31T18:40:29Z | 298 | 1 | null | [
"region:us"
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cestwc/cnn_dailymail-metaeval100 | cestwc | 2022-01-07T10:55:08Z | 298 | 0 | null | [
"region:us"
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cestwc/sac-approx-1 | cestwc | 2022-01-02T19:14:27Z | 298 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
chau/ink_test01 | chau | 2022-02-15T09:15:56Z | 298 | 0 | null | [
"license:other",
"region:us"
] | 2022-02-15T09:15:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: other
---
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chitra/contradiction | chitra | 2022-01-19T11:46:58Z | 298 | 0 | null | [
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chopey/dhivehi | chopey | 2021-11-30T03:41:11Z | 298 | 0 | null | [
"region:us"
] | 2021-11-30T03:41:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Dhivehi dataset for MNT | [
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corypaik/coda | corypaik | 2022-10-20T16:57:23Z | 298 | 2 | coda | [
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2110.08182",
"region:us"
] | 2022-10-20T16:57:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: CoDa
paperswithcode_id: coda
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-scoring
task_ids:
- text-scoring-other-distribution-prediction
---
# Dataset Card for CoDa
## 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-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [nala-cub/coda](https://github.com/nala-cub/coda)
- **Paper:** [The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color](https://arxiv.org/abs/2110.08182)
- **Point of Contact:** [Cory Paik](cory.paik@colorado.edu)
### Dataset Summary
*The Color Dataset* (CoDa) is a probing dataset to evaluate the representation of visual properties in language models. CoDa consists of color distributions for 521 common objects, which are split into 3 groups. We denote these groups as Single, Multi, and Any, which represents the typical object of each group.
The default configuration of CoDa uses 10 CLIP-style templates (e.g. "A photo of a [object]"), and 10 cloze-style templates (e.g. "Everyone knows most [object] are
[color]." )
### Supported Tasks and Leaderboards
This version of the dataset consists of the filtered and templated examples as cloze style questions. See the [GitHub](https://github.com/nala-cub/coda) repo for the raw data (e.g. unfiltered annotations) as well as example usage with GPT-2, RoBERTa, ALBERT, and CLIP.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en-US`.
## Dataset Structure
### Data Instances
An example looks like this:
```json
{
"text": "All rulers are [MASK].",
"label": [
0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352,
0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636,
0.301363647
],
"template_group": 1,
"template_idx": 0,
"class_id": "/m/0hdln",
"display_name": "Ruler",
"object_group": 2,
"ngram": "ruler"
}
```
### Data Fields
- `text`: The templated example. What this is depends on the value of `template_group`.
- `template_group=0`: A CLIP style example. There are no `[MASK]` tokens in these examples.
- `template_group=1`: A cloze style example. Note that all templates have `[MASK]` as the last word, but in most cases, the period should be included.
- `label`: A list of probability values for the 11 colors. Note that these are sorted by the alphabetic order of the 11 colors (black, blue, brown, gray, green, orange, pink, purple, red, white, yellow).
- `template_group`: Type of template, `0` corresponds to A CLIP style template (`clip-imagenet`), and `1` corresponds to A cloze style templates (`text-masked`).
- `template_idx`: The index of the template out of all templates
- `class_id`: The Corresponding [OpenImages v6](https://storage.googleapis.com/openimages/web/index.html) `ClassID`.
- `display_name`: The Corresponding [OpenImages v6](https://storage.googleapis.com/openimages/web/index.html) `DisplayName`.
- `object_group`: Object Group, values correspond to `Single`, `Multi`, and `Any`.
- `ngram`: Corresponding n-gram used for lookups.
### Data Splits
Object Splits:
| Group | All | Train | Valid | Test |
| ------ | --- | ----- | ----- | ---- |
| Single | 198 | 118 | 39 | 41 |
| Multi | 208 | 124 | 41 | 43 |
| Any | 115 | 69 | 23 | 23 |
| Total | 521 | 311 | 103 | 107 |
Example Splits:
| Group | All | Train | Valid | Test |
| ------ | ----- | ----- | ----- | ---- |
| Single | 3946 | 2346 | 780 | 820 |
| Multi | 4146 | 2466 | 820 | 860 |
| Any | 2265 | 1352 | 460 | 453 |
| Total | 10357 | 6164 | 2060 | 2133 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CoDa is licensed under the Apache 2.0 license.
### Citation Information
```
@misc{paik2021world,
title={The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color},
author={Cory Paik and Stéphane Aroca-Ouellette and Alessandro Roncone and Katharina Kann},
year={2021},
eprint={2110.08182},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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dalle-mini/open-images | dalle-mini | 2021-09-10T07:09:01Z | 298 | 5 | null | [
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DebateLabKIT/deepa2 | DebateLabKIT | 2022-12-16T14:49:35Z | 298 | 3 | null | [
"task_categories:text-retrieval",
"task_categories:text-generation",
"task_ids:text-simplification",
"task_ids:parsing",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"language:en",
"license:other",
"argument-mining",
"summarization",
"conditional-text-generation",
"structure-prediction",
"arxiv:2110.01509",
"region:us"
] | 2022-12-16T14:49:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators: []
language_creators:
- other
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-retrieval
- text-generation
task_ids:
- text-simplification
- parsing
pretty_name: deepa2
tags:
- argument-mining
- summarization
- conditional-text-generation
- structure-prediction
---
# `deepa2` Datasets Collection
## Table of Contents
- [`deepa2` Datasets Collection](#deepa2-datasets-collection)
- [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-structure)
- [Sub-Datasets](#sub-datasets)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [blog post](https://debatelab.github.io/journal/deepa2.html)
- **Repository:** [github](https://github.com/debatelab/deepa2)
- **Paper:** [arxiv](https://arxiv.org/abs/2110.01509)
- **Point of Contact:** [Gregor Betz](gregor.betz@kit.edu)
### Dataset Summary
This is a growing, curated collection of `deepa2` datasets, i.e. datasets that contain comprehensive logical analyses of argumentative texts. The collection comprises:
* datasets that are built from existing NLP datasets by means of the [`deepa2 bake`](https://github.com/debatelab/deepa2) tool.
* original `deepa2` datasets specifically created for this collection.
The tool [`deepa2 serve`](https://github.com/debatelab/deepa2#integrating-deepa2-into-your-training-pipeline) may be used to render the data in this collection as text2text examples.
### Supported Tasks and Leaderboards
For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the `task-category-tag` with an appropriate `other:other-task-name`).
- `conditional-text-generation`: The dataset can be used to train models to generate a fully reconstruction of an argument from a source text, making, e.g., its implicit assumptions explicit.
- `structure-prediction`: The dataset can be used to train models to formalize sentences.
- `text-retrieval`: The dataset can be used to train models to extract reason statements and conjectures from a given source text.
### Languages
English. Will be extended to cover other languages in the futures.
## Dataset Structure
### Sub-Datasets
This collection contains the following `deepa2` datasets:
* `esnli`: created from e-SNLI with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/esnli.md).
* `enbank` (`task_1`, `task_2`): created from Entailment Bank with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/enbank.md).
* `argq`: created from IBM-ArgQ with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/argq.md).
* `argkp`: created from IBM-KPA with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/argkp.md).
* `aifdb` (`moral-maze`, `us2016`, `vacc-itc`): created from AIFdb with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/aifdb.md).
* `aaac` (`aaac01` and `aaac02`): original, machine-generated contribution; based on an an improved and extended algorithm that backs https://huggingface.co/datasets/debatelab/aaac.
### Data Instances
see: https://github.com/debatelab/deepa2/tree/main/docs
### Data Fields
see: https://github.com/debatelab/deepa2/tree/main/docs
|feature|esnli|enbank|aifdb|aaac|argq|argkp|
|--|--|--|--|--|--|--|
| `source_text` | x | x | x | x | x | x |
| `title` | | x | | x | | |
| `gist` | x | x | | x | | x |
| `source_paraphrase` | x | x | x | x | | |
| `context` | | x | | x | | x |
| `reasons` | x | x | x | x | x | |
| `conjectures` | x | x | x | x | x | |
| `argdown_reconstruction` | x | x | | x | | x |
| `erroneous_argdown` | x | | | x | | |
| `premises` | x | x | | x | | x |
| `intermediary_conclusion` | | | | x | | |
| `conclusion` | x | x | | x | | x |
| `premises_formalized` | x | | | x | | x |
| `intermediary_conclusion_formalized` | | | | x | | |
| `conclusion_formalized` | x | | | x | | x |
| `predicate_placeholders` | | | | x | | |
| `entity_placeholders` | | | | x | | |
| `misc_placeholders` | x | | | x | | x |
| `plchd_substitutions` | x | | | x | | x |
### Data Splits
Each sub-dataset contains three splits: `train`, `validation`, and `test`.
## Dataset Creation
### Curation Rationale
Many NLP datasets focus on tasks that are relevant for logical analysis and argument reconstruction. This collection is the attempt to unify these resources in a common framework.
### Source Data
See: [Sub-Datasets](#sub-datasets)
## Additional Information
### Dataset Curators
Gregor Betz, KIT; Kyle Richardson, Allen AI
### Licensing Information
We re-distribute the the imported sub-datasets under their original license:
|Sub-dataset|License|
|--|--|
|esnli|MIT|
|aifdb|free for academic use ([TOU](https://arg-tech.org/index.php/research/argument-corpora/))|
|enbank|CC BY 4.0|
|aaac|CC BY 4.0|
|argq|CC BY SA 4.0|
|argkp|Apache|
### Citation Information
```
@article{betz2021deepa2,
title={DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models},
author={Gregor Betz and Kyle Richardson},
year={2021},
eprint={2110.01509},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--If the dataset has a [DOI](https://www.doi.org/), please provide it here.-->
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dragosnicolae555/RoITD | dragosnicolae555 | 2022-10-25T09:07:43Z | 298 | 0 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:cc-by-4.0",
"region:us"
] | 2022-10-25T09:07:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ro-RO
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'RoITD: Romanian IT Question Answering Dataset'
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
---
## Dataset Summary
We introduce a Romanian IT Dataset (RoITD) resembling SQuAD 1.1. RoITD consists of 9575 Romanian QA pairs formulated by crowd workers. QA pairs are based on 5043 articles from Romanian Wikipedia articles describing IT and household products. Of the total number of questions, 5103 are possible (i.e. the correct answer can be found within the paragraph) and 4472 are not possible (i.e. the given answer is a "plausible answer" and not correct)
## Dataset Structure
The data structure follows the format of SQuAD, which contains several attributes such as **question**, **id**, **text**, `**answer_start**, **is_impossible** and **context**. The paragraph provided to crowd sourcing workers is stored in the field **context**. This incorporates manually-selected paragraphs from Wikipedia. The field **id** is comprised of a randomly assigned unique identification number for the answer-question pair. Only the numbers "0" and "1" are allowed in the **is_impossible** field. The category "A" is assigned the value "0", indicating that the answer is correct. The value "1" corresponds to the category "U", indicating a plausible answer. The question posed by the source crowd source worker is represented by the field **question**. The field **answer_start** keeps track of the character index marking the beginning of an answer.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
emrecan/stsb-mt-turkish | emrecan | 2022-10-25T10:55:24Z | 298 | 3 | null | [
"task_categories:text-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"language_creators:machine-generated",
"size_categories:1K<n<10K",
"source_datasets:extended|other-sts-b",
"language:tr",
"region:us"
] | 2022-10-25T10:55:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language_creators:
- machine-generated
language:
- tr
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-sts-b
task_categories:
- text-classification
task_ids:
- semantic-similarity-scoring
- text-scoring
---
# STSb Turkish
Semantic textual similarity dataset for the Turkish language. It is a machine translation (Azure) of the [STSb English](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark) dataset. This dataset is not reviewed by expert human translators.
Uploaded from [this repository](https://github.com/emrecncelik/sts-benchmark-tr). | [
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espejelomar/code_search_net_python_10000_examples | espejelomar | 2022-02-20T03:42:13Z | 298 | 8 | null | [
"license:cc",
"region:us"
] | 2022-02-20T03:42:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: cc
---
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flax-community/multilingual-vqa | flax-community | 2021-07-21T06:27:40Z | 298 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/coldplay | huggingartists | 2022-10-25T09:26:52Z | 298 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:26:52Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/coldplay"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.451347 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/e4f988f1ee26618c5dd41b59b8ff2b43.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/coldplay">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Coldplay</div>
<a href="https://genius.com/artists/coldplay">
<div style="text-align: center; font-size: 14px;">@coldplay</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/coldplay).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/coldplay")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|432| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/coldplay")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/fear-factory | huggingartists | 2022-10-25T09:29:44Z | 298 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:29:44Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/fear-factory"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.178617 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/5c2952ca198d8eda91b478829b867fd6.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/fear-factory">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Fear Factory</div>
<a href="https://genius.com/artists/fear-factory">
<div style="text-align: center; font-size: 14px;">@fear-factory</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/fear-factory).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/fear-factory")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|197| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/fear-factory")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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huggingface/documentation-images | huggingface | 2023-11-28T16:49:05Z | 298 | 18 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-11-28T16:49:05Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: cc-by-nc-sa-4.0
---
### This dataset contains images used in the documentation of HuggingFace's libraries.
HF Team: Please make sure you optimize the assets before uploading them.
My favorite tool for this is https://tinypng.com/.
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ia-bentebib/doc2dial_fr | ia-bentebib | 2022-02-10T20:53:16Z | 298 | 0 | null | [
"region:us"
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iarfmoose/qa_evaluator | iarfmoose | 2021-11-29T05:20:10Z | 298 | 0 | null | [
"region:us"
] | 2021-11-29T05:20:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This is the same dataset as the question_generator dataset but with the context removed and the question and answer in separate fields. This is intended to be used with the [question_generator](https://github.com/AMontgomerie/question_generator) repo to train the qa_evaluator model which predicts whether a question and answer pair makes sense. | [
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jgammack/MTL-abstracts | jgammack | 2022-02-07T18:41:20Z | 298 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MLCommons/peoples_speech | MLCommons | 2023-05-16T16:11:10Z | 298 | 30 | null | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1T<n",
"source_datasets:original",
"language:en",
"license:cc-by-2.0",
"license:cc-by-2.5",
"license:cc-by-3.0",
"license:cc-by-4.0",
"license:cc-by-sa-3.0",
"license:cc-by-sa-4.0",
"robust-speech-recognition",
"noisy-speech-recognition",
"speech-recognition",
"arxiv:2111.09344",
"region:us"
] | 2023-05-16T16:11:10Z | 2022-08-16T14:21:49.000Z | 2022-08-16T14:21:49 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- cc-by-2.0
- cc-by-2.5
- cc-by-3.0
- cc-by-4.0
- cc-by-sa-3.0
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1T<n
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: People's Speech
tags:
- robust-speech-recognition
- noisy-speech-recognition
- speech-recognition
---
# Dataset Card for People's Speech
## 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](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://mlcommons.org/en/peoples-speech/
- **Repository:** https://github.com/mlcommons/peoples-speech
- **Paper:** https://arxiv.org/abs/2111.09344
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [datasets@mlcommons.org](mailto:datasets@mlcommons.org)
### Dataset Summary
The People's Speech Dataset is among the world's largest English speech recognition corpus today that is licensed for academic and commercial usage under CC-BY-SA and CC-BY 4.0. It includes 30,000+ hours of transcribed speech in English languages with a diverse set of speakers. This open dataset is large enough to train speech-to-text systems and crucially is available with a permissive license.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
{
"id": "gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac",
"audio": {
"path": "gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac"
"array": array([-6.10351562e-05, ...]),
"sampling_rate": 16000
}
"duration_ms": 14490,
"text": "contends that the suspension clause requires a [...]"
}
### Data Fields
{
"id": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"duration_ms": datasets.Value("int32"),
"text": datasets.Value("string"),
}
### Data Splits
We provide the following configurations for the dataset: `cc-by-clean`, `cc-by-dirty`, `cc-by-sa-clean`, `cc-by-sa-dirty`, and `microset`. We don't provide splits for any of the configurations.
## Dataset Creation
### Curation Rationale
See our [paper](https://arxiv.org/abs/2111.09344).
### Source Data
#### Initial Data Collection and Normalization
Data was downloaded via the archive.org API. No data inference was done.
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
No manual annotation is done. We download only source audio with already existing transcripts.
#### Who are the annotators?
For the test and dev sets, we paid native American English speakers to do transcriptions. We do not know the identities of the transcriptionists for data in the training set. For the training set, we have noticed that some transcriptions are likely to be the output of automatic speech recognition systems.
### Personal and Sensitive Information
Several of our sources are legal and government proceedings, spoken histories, speeches, and so on. Given that these were intended as public documents and licensed as such, it is natural that the involved individuals are aware of this.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset could be used for speech synthesis. However, this requires careful cleaning of the dataset, as background noise is not tolerable for speech synthesis.
The dataset could be used for keyword spotting tasks as well. In particular, this is good use case for the non-English audio in the dataset.
Our sincere hope is that the large breadth of sources our dataset incorporates reduces existing quality of service issues today, like speech recognition system’s poor understanding of non-native English accents. We cannot think of any unfair treatment that come from using this dataset at this time.
### Discussion of Biases
Our data is downloaded from archive.org. As such, the data is biased towards whatever users decide to upload there.
Almost all of our data is American accented English.
### Other Known Limitations
As of version 1.0, a portion of data in the training, test, and dev sets is poorly aligned. Specifically, some words appear in the transcript, but not the audio, or some words appear in the audio, but not the transcript. We are working on it.
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
We provide CC-BY and CC-BY-SA subsets of the dataset.
### Citation Information
Please cite:
```
@article{DBLP:journals/corr/abs-2111-09344,
author = {Daniel Galvez and
Greg Diamos and
Juan Ciro and
Juan Felipe Cer{\'{o}}n and
Keith Achorn and
Anjali Gopi and
David Kanter and
Maximilian Lam and
Mark Mazumder and
Vijay Janapa Reddi},
title = {The People's Speech: {A} Large-Scale Diverse English Speech Recognition
Dataset for Commercial Usage},
journal = {CoRR},
volume = {abs/2111.09344},
year = {2021},
url = {https://arxiv.org/abs/2111.09344},
eprinttype = {arXiv},
eprint = {2111.09344},
timestamp = {Mon, 22 Nov 2021 16:44:07 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2111-09344.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mstz/diamonds | mstz | 2023-04-16T17:27:20Z | 298 | 0 | null | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"student performance",
"tabular_classification",
"multiclass_classification",
"UCI",
"region:us"
] | 2023-04-16T17:27:20Z | 2023-03-24T01:12:26.000Z | 2023-03-24T01:12:26 | ---
language:
- en
tags:
- student performance
- tabular_classification
- multiclass_classification
- UCI
pretty_name: Diamond
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- encoding
- cut
- cut_binary
license: cc
---
# Diamonds
The [Diamonds dataset](https://www.kaggle.com/datasets/ulrikthygepedersen/diamonds) from Kaggle.
Dataset collecting properties of cut diamonds to determine the cut quality.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| cut | Multiclass classification | Predict the cut quality of the diamond. |
| cut_binary | Binary classification | Is the cut quality at least very good?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/diamonds", "cut")["train"]
```
# Features
|**Feature** |**Description**|
|-----------------------------------|---------------|
|`carat` | `float32` |
|`color` | `string` |
|`clarity` | `float32` |
|`depth` | `float32` |
|`table` | `float32` |
|`price` | `float32` |
|`observation_point_on_axis_x` | `float32` |
|`observation_point_on_axis_y` | `float32` |
|`observation_point_on_axis_z` | `float32` |
|`cut` | `int8` | | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hezarai/xlsum-fa | hezarai | 2023-11-14T14:10:21Z | 298 | 0 | null | [
"task_categories:summarization",
"language:fa",
"region:us"
] | 2023-11-14T14:10:21Z | 2023-07-31T07:58:00.000Z | 2023-07-31T07:58:00 | ---
task_categories:
- summarization
language:
- fa
pretty_name: XLSum Persian
---
The Persian portion of the [XLSum](https://huggingface.co/datasets/csebuetnlp/xlsum) dataset.
### Citation
```bibtex
@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. Sohel and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.413",
pages = "4693--4703",
}
``` | [
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hrwac | null | 2022-11-03T16:15:15Z | 297 | 0 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1B<n<10B",
"source_datasets:original",
"language:hr",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-11-03T16:15:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- hr
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1B<n<10B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: HrWac
dataset_info:
features:
- name: sentence
dtype: string
config_name: hrwac
splits:
- name: train
num_bytes: 43994569015
num_examples: 1736944727
download_size: 9217221471
dataset_size: 43994569015
---
# Dataset Card for HrWac
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://nlp.ffzg.hr/resources/corpora/hrwac/
- **Repository:** https://www.clarin.si/repository/xmlui/handle/11356/1064
- **Paper:** http://nlp.ffzg.hr/data/publications/nljubesi/ljubesic11-hrwac.pdf
- **Leaderboard:**
- **Point of Contact:** [Nikola Ljubešič](mailto:nikola.ljubesic@ffzg.hr)
### Dataset Summary
The Croatian web corpus hrWaC was built by crawling the .hr top-level domain in 2011 and again in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Croatian vs. Serbian).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is monolingual in Croatian language.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- sentence: sentences as strings
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@misc{11356/1064,
title = {Croatian web corpus {hrWaC} 2.1},
author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
url = {http://hdl.handle.net/11356/1064},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
year = {2016} }
```
### Contributions
Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
opus_dogc | null | 2022-11-03T16:07:43Z | 297 | 0 | null | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"language:es",
"license:cc0-1.0",
"region:us"
] | 2022-11-03T16:07:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- ca
- es
license:
- cc0-1.0
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OPUS DOGC
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- ca
- es
config_name: tmx
splits:
- name: train
num_bytes: 1258924464
num_examples: 4763575
download_size: 331724078
dataset_size: 1258924464
---
# Dataset Card for OPUS DOGC
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/DOGC.php
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OPUS DOGC is a collection of documents from the Official Journal of the Government of Catalonia, in Catalan and Spanish languages, provided by Antoni Oliver Gonzalez from the Universitat Oberta de Catalunya.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is multilingual with parallel text in:
- Catalan
- Spanish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
A data instance contains the following fields:
- `ca`: the Catalan text
- `es`: the aligned Spanish text
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is in the Public Domain under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/).
### Citation Information
```
@inproceedings{tiedemann-2012-parallel,
title = "Parallel Data, Tools and Interfaces in {OPUS}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf",
pages = "2214--2218",
abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.",
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
psc | null | 2023-01-25T14:42:57Z | 297 | 1 | null | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-3.0",
"region:us"
] | 2023-01-25T14:42:57Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
pretty_name: psc
dataset_info:
features:
- name: extract_text
dtype: string
- name: summary_text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 5026582
num_examples: 4302
- name: test
num_bytes: 1292103
num_examples: 1078
download_size: 2357808
dataset_size: 6318685
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
http://zil.ipipan.waw.pl/PolishSummariesCorpus
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Polish Summaries Corpus contains news articles and their summaries. We used summaries of the same article as positive pairs and sampled the most similar summaries of different articles as negatives.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Polish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- extract_text: text to summarise
- summary_text: summary of extracted text
- label: 1 indicates summary is similar, 0 means that it is not similar
### Data Splits
Data is splitted in train and test dataset. Test dataset doesn't have label column, so -1 is set instead.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC BY-SA 3.0
### Citation Information
@inproceedings{ogro:kop:14:lrec,
title={The {P}olish {S}ummaries {C}orpus},
author={Ogrodniczuk, Maciej and Kope{\'c}, Mateusz},
booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
year = "2014",
}
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. | [
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ApiInferenceTest/asr_dummy | ApiInferenceTest | 2022-02-14T11:18:56Z | 297 | 0 | null | [
"region:us"
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FIG-Loneliness/FIG-Loneliness | FIG-Loneliness | 2022-07-14T23:14:43Z | 297 | 1 | null | [
"region:us"
] | 2022-07-14T23:14:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # 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-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [FIG-Loneliness](https://ojs.aaai.org/index.php/ICWSM/article/view/19302)
- **Paper:** [Many Ways to be Lonely](https://ojs.aaai.org/index.php/ICWSM/article/view/19302/19074)
- **Point of Contact:** [Sherry Yueyi Jiang](mailto:yujiang@ucsd.edu)
### Dataset Summary
FIG-Loneliness is a dataset for fine-grained loneliness characterization and model training. This dataset consists of 2633 lonely and 3000 non-lonely Reddit posts annotated by trained human annotators. For the lonely posts, we provide fine-grained category labels for the forms of loneliness including duration, context and interpersonal relationships, and for the coping strategies of the authors including reaching out, seeking advice, seeking validation and non-directed interaction.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is English.
## Dataset Structure
### Loading
To load the dataset, first clone this dataset repo:
```bash
git clone https://huggingface.co/datasets/FIG-Loneliness/FIG-Loneliness
```
Then we can load datasets using Huggingface Datasets API:
```python
import os
import datasets as hf_ds
ROOT = "dir/to/data/repo"
# load datasets
train_set = hf_ds.load_from_disk(os.path.join(ROOT, "train_set"))
dev_set = hf_ds.load_from_disk(os.path.join(ROOT, "dev_set"))
test_set = hf_ds.load_from_disk(os.path.join(ROOT, "test_set"))
```
### Data Instances
The `train_set` split contains 3,943 instances. The `dev_set` split contains 1,126. The `test_set` split contains 564 instances.
### Data Fields
Each instance contains 8 fields: `idx`, `unique_id`, `text`, `lonely`, `temporal`, `interaction`, `context_pri`, and `interpersonal_pri`.
| Field | Meaning |
|:---:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| `idx` | Integer index of this instance from our scrapped Reddit posts. |
| `unique_id` | Unique ID of this Reddit post. |
| `text` | Textual content of the Reddit post. |
| `lonely` | 2-len one-hot vector, representing [non-lonely, lonely]. |
| `temporal` | **Duration**. 4-len vector summarizing human annotators' votings in the order of [transient, enduring, ambiguous, NA]. |
| `interaction` | **Interaction**. 5-len vector summarizing human annotators' votings in the order of [seeking advice, providing help, seeking validation and affirmation, reaching out, non directed interaction] |
| `context_pri` | **Context**. 5-len vector summarizing human annotators' votings in the order of [social, physical, somatic, romantic, N/A] |
| `interpersonal_pri` | **Interpersonal**. 5-len vector summarizing human annotators' votings in the order of [romantic, friendship, family, colleagues, N/A] |
### Data Splits
The entire dataset is split into 3,943 training instances, 1,126 dev instances, and 564 test instances.
## Dataset Creation
### Curation Rationale
The data curation rationale is to capture **loneliness expressions** not only from a wider user base but also from users who specifically belong to the young adult age group (a vulnerable group for loneliness). We sampled data from Reddit and subsequently annotated the data with loneliness labels.
### Source Data
#### Initial Data Collection and Normalization
By using Reddit’s Pushshift API, we collected all posts from two loneliness specific subreddits (*r/loneliness*, *r/lonely*) and two subreddits for young adults (*r/youngadults*, *r/college*) from 2018 to 2020.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Who are the annotators?
Annotation labels were provided by trained undergraduate research assistants and Amazon’s Mechanical Turk workers (MTurkers) with a Master certification.
#### Annotation process
For the potential lonely samples:
We had research assistants labeled the sampled potential lonely posts. Each post was labeled by three of research assistants. A posts was first labeled on whether it contains an expression on self-disclosure of loneliness. If the majority of the annotators labeled a post as not containing such expression, the post would be discarded, otherwise it is further labeled according to a codebook that contains the following categories: (1) *duration*: the duration of the loneliness experience (transient, enduring, and ambiguous), (2) *context*: the contexts of the experience (social, physical, somatic, and romantic), (3) *interpresonal*: the interpersonal relationships involved in the experience (romantic, family, friendship, and peers), and (4) *interaction*: user interaction styles (seeking advice, providing support, seeking validation/affirmation, reaching out and non-directed interaction). The codebook is intended for dissecting different forms of loneliness and users’ coping strategies in the loneliness discourse. We also included a ‘not applicable’ (NA) label to accommodate situations that are not suitable for classification. For each category, the annotators gave *one value* which they think would best capture the source of loneliness in the post or the poster’s interaction intent.
For the potential non-lonely samples:
MTurkers were instructed to classify whether the Reddit posters express loneliness. Each post was annotated by three MTurkers, and only posts labeled as non-lonely by the majority would remain in the final annotated dataset.
All the labeled posts and annotations were included in FIG-Loneliness, which consists of roughly 3000 lonely and 3000 non-lonely posts.
#### Dataset Codebook
See code rule and example posts for the category labels [here](https://drive.google.com/file/d/1J6i72qyqirAIC40jWuJvDN-ZWU87XttH/view).
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
See **Limitation and Data Disclaimer** [here](https://ojs.aaai.org/index.php/ICWSM/article/view/19302/19074)
## Additional Information
### Dataset Curator
[More Information Needed]
### Licensing Information
[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
Jiang, Y., Jiang, Y., Leqi, L., & Winkielman, P. (2022). Many Ways to Be Lonely: Fine-Grained Characterization of Loneliness and Its Potential Changes in COVID-19. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 405-416. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/19302 | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Fraser/python-lines | Fraser | 2021-02-22T10:20:34Z | 297 | 1 | null | [
"region:us"
] | 2021-02-22T10:20:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | 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 code.
Content
Each row has a parsable line of source code.
{'text': '{python source code line}'}
Most lines are < 100 characters while all are under 125 characters.
Contains 2.6 million lines.
All code is in parsable into a python3 ast.
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GEM/CrossWOZ | GEM | 2022-10-24T15:29:55Z | 297 | 5 | null | [
"task_categories:conversational",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:zh",
"license:apache-2.0",
"dialog-response-generation",
"region:us"
] | 2022-10-24T15:29:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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/CrossWOZ
## Dataset Description
- **Homepage:** https://github.com/thu-coai/CrossWOZ
- **Repository:** https://github.com/thu-coai/CrossWOZ
- **Paper:** https://aclanthology.org/2020.tacl-1.19
- **Leaderboard:** N/A
- **Point of Contact:** Qi Zhu
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/CrossWOZ).
### Dataset Summary
CrossWOZ is a Chinese multi-domain task-oriented dialogue dataset . It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/CrossWOZ')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/CrossWOZ).
#### website
[Github](https://github.com/thu-coai/CrossWOZ)
#### paper
[ACL Anthology](https://aclanthology.org/2020.tacl-1.19)
#### authors
Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang from CoAI group, Tsinghua University
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/thu-coai/CrossWOZ)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/thu-coai/CrossWOZ)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2020.tacl-1.19)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@article{zhu-etal-2020-crosswoz,
title = "{C}ross{WOZ}: A Large-Scale {C}hinese Cross-Domain Task-Oriented Dialogue Dataset",
author = "Zhu, Qi and
Huang, Kaili and
Zhang, Zheng and
Zhu, Xiaoyan and
Huang, Minlie",
journal = "Transactions of the Association for Computational Linguistics",
volume = "8",
year = "2020",
url = "https://aclanthology.org/2020.tacl-1.19",
doi = "10.1162/tacl_a_00314",
pages = "281--295",
abstract = "To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, 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 on both user and system sides. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Qi Zhu
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
zhuq96@gmail.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Chinese`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
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 system sides. We also provide a user simulator and several benchmark models for pipelined taskoriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Generate a response according to the dialog context and database search results.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Tsinghua University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang from CoAI group, Tsinghua University
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
National Science Foundation of China, National Key R&D Program of China
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Qi Zhu (Tsinghua University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id` (string): GEM-CrossWOZ-{split}-{id}
- `dialog_id` (string): dialog ID
- `sys_id` (string): system annotator ID
- `usr_id` (string): user annotation ID
- `type` (string): dialog type
- `task description` (list of strings): natural language descriptions of the user goal
- `goal` (list of tuples), includes:
- `sub-goal id` (string)
- `domain name` (string)
- `slot name` (string)
- `constraint` if filled, else `requirement` (string)
- `whether be mentioned in previous turns` (string)
- `messages` (list of dict): dialog turns. Each turn contains:
- `content` (string): utterance
- `role` (string): user or system
- `dialog_act` (list of tuples), includes:
- `domain` (string)
- `intent` (string)
- `slot` (string)
- `value` (string)
- `user_state` (list of tuples): same format as "goal", can be viewed as dynamic goal.
- `sys_state_init` (dict): the first db query emitted, records user constraints faithfully. If the system find no result that matches, he/she may relax the constraints manually and search db multiple times.
- `domain` (dict): slot(string)-value(string) pairs
- `selectedResults` (list of string): db search result that would be used in this turn.
- `sys_state` (dict): the final db query emitted, records the db used by the system in this turn. Same format as sys_state_init. Note that this may not satisfy all user constraints.
- `final_goal` (list of tuples): user state/goal at the end of dialog. same format as "goal".
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{'dialog_id': '2303',
'final_goal': [['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
['2', '景点', '电话', '010-85007938', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
['3', '酒店', '电话', '010-84273030', 'True']],
'gem_id': 'GEM-CrossWOZ-test-0',
'goal': [['1', '餐馆', '人均消费', '50-100元', 'False'],
['1', '餐馆', '推荐菜', "['美食街']", 'False'],
['1', '餐馆', '名称', '', 'False'],
['1', '餐馆', '营业时间', '', 'False'],
['1', '餐馆', '周边景点', '[]', 'False'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
['2', '景点', '评分', '4.5分以上', 'False'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
'messages': {'content': ['你好，我想吃美食街，帮我推荐一个人均消费在50-100元的餐馆，谢谢。',
'为您推荐鲜鱼口老字号美食街，人均消费75元，有您想吃的美食街哦。',
'营业时间是什么时间？',
'周一至周日 10:00-22:00。',
'他家周边有什么景点吗？',
'有故宫, 前门大街, 恭王府, 天安门广场。',
'哦，我想在这些附近景点里找一个4.5分以上的，有吗？',
'故宫就是哦，4.7分。',
'好的，电话和地址告诉我一下。',
'010-85007938；北京市东城区景山前街4号。',
'好的，麻烦你帮我查一下桔子水晶酒店(北京安贞店)电话呗。',
'010-84273030。',
'好的，收到，谢谢你！',
'不客气。'],
'dialog_act': [[['General', 'greet', 'none', 'none'],
['General', 'thank', 'none', 'none'],
['Inform', '餐馆', '人均消费', '50-100元'],
['Inform', '餐馆', '推荐菜', '美食街'],
['Request', '餐馆', '名称', '']],
[['Inform', '餐馆', '人均消费', '75元'], ['Inform', '餐馆', '名称', '鲜鱼口老字号美食街']],
[['Request', '餐馆', '营业时间', '']],
[['Inform', '餐馆', '营业时间', '周一至周日 10:00-22:00']],
[['Request', '餐馆', '周边景点', '']],
[['Inform', '餐馆', '周边景点', '前门大街'],
['Inform', '餐馆', '周边景点', '天安门广场'],
['Inform', '餐馆', '周边景点', '恭王府'],
['Inform', '餐馆', '周边景点', '故宫']],
[['Inform', '景点', '评分', '4.5分以上'], ['Select', '景点', '源领域', '餐馆']],
[['Inform', '景点', '名称', '故宫'], ['Inform', '景点', '评分', '4.7分']],
[['Request', '景点', '地址', ''], ['Request', '景点', '电话', '']],
[['Inform', '景点', '地址', '北京市东城区景山前街4号'],
['Inform', '景点', '电话', '010-85007938']],
[['Inform', '酒店', '名称', '桔子水晶酒店(北京安贞店)'], ['Request', '酒店', '电话', '']],
[['Inform', '酒店', '电话', '010-84273030']],
[['General', 'thank', 'none', 'none']],
[['General', 'welcome', 'none', 'none']]],
'role': ['usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys'],
'sys_state': [{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
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['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', '[]', 'True'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
['2', '景点', '评分', '4.5分以上', 'False'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '', 'True'],
['2', '景点', '电话', '', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
['2', '景点', '电话', '010-85007938', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
['3', '酒店', '电话', '', 'True']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
['2', '景点', '电话', '010-85007938', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
['3', '酒店', '电话', '010-84273030', 'True']],
[]]},
'sys_id': 96,
'task description': ['你要去一个餐馆(id=1)用餐。你希望餐馆的人均消费是50-100元的。你想吃的菜肴是美食街。你想知道这个餐馆的名称、营业时间、周边景点。',
'你要去id=1附近的景点(id=2)游玩。你希望景点的评分是4.5分以上。你想知道这个景点的地址、电话。',
'你要去名叫桔子水晶酒店(北京安贞店)的酒店(id=3)住宿。你想知道这个酒店的电话。'],
'type': '不独立多领域',
'usr_id': 97}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
| Split | Train | Valid | Test |
| --------------------- | ------ | ----- | ----- |
| \# dialogues | 5,012 | 500 | 500 |
| \# Turns (utterances) | 84,692 | 8,458 | 8,476 |
| Vocab | 12,502 | 5,202 | 5,143 |
| Avg. sub-goals | 3.24 | 3.26 | 3.26 |
| Avg. semantic tuples | 14.8 | 14.9 | 15.0 |
| Avg. turns | 16.9 | 16.9 | 17.0 |
| Avg. tokens per turn | 16.3 | 16.3 | 16.2 |
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides, which can be used in a wide range of tasks.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Dialog understanding, dialog policy learning
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
To adapt to hugging face Datasets, we 1) separate user annotators' ID and system annotations' ID; 2) we convert the data type in goal/user state to string.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
[Code](https://github.com/thu-coai/Convlab-2)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
According to the type of user goal, we group the dialogues in the training set into five categories:
- S: 417 dialogues have only one sub-goal in HAR domains.
- M: 1573 dialogues have multiple sub-goals (2-3) in HAR domains. However, these sub-goals do not have cross-domain informable slots.
- M+T: 691 dialogues have multiple sub-goals in HAR domains and at least one sub-goal in the metro or taxi domain (3-5 sub-goals). The sub-goals in HAR domains do not have cross-domain informable slots.
- CM: 1,759 dialogues have multiple sub-goals (2-5) in HAR domains with cross-domain informable slots.
- CM+T: 572 dialogues have multiple sub-goals in HAR domains with cross-domain informable slots and at least one sub-goal in the metro or taxi domain (3-5 sub-goals).
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Dialog understanding, dialog policy learning
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
BLEU evaluates the generation quality.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Inform rate: how many entities in the gold response appear in the generated response.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
BLEU on MultiWOZ dataset.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Gather human-to-human dialog in Chinese.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Generate a response according to the dialog context and database search results.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Participatory experiment`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
An usr/sys ID indicates the creator of different data points.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
domains: attraction, hotel, restaurant, metro, taxi
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
Annotators agree using the dataset for research purpose.
#### Other Consented Downstream Use
<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
<!-- scope: microscope -->
Any
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. The corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides, which can be used in a wide range of tasks.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
Yes
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
No
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
No
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Model may not handle unknown values in the dialog
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
Responses can be diverse, which is not captured by BLEU
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GroNLP/ik-nlp-22_pestyle | GroNLP | 2022-10-25T09:06:27Z | 297 | 0 | null | [
"task_categories:translation",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:it",
"license:other",
"region:us"
] | 2022-10-25T09:06:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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: A Study in Post-Editing Stylometry
## Table of Contents
- [Dataset Card for IK-NLP-22 Project 1: A Study in Post-Editing Stylometry](#dataset-card-for-ik-nlp-22-project-1-a-study-in-post-editing-stylometry)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Train Split](#train-split)
- [Test splits](#test-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Source:** [FLORES-101](https://huggingface.co/datasets/gsarti/flores_101)
- **Point of Contact:** [Gabriele Sarti](mailto:ik-nlp-course@rug.nl)
### Dataset Summary
This dataset contains a sample of sentences taken from the [FLORES-101](https://huggingface.co/datasets/gsarti/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, pauses, editing times) were collected using the [PET](https://github.com/wilkeraziz/PET) platform.
This dataset is made available for final projects of the 2022 edition of the Natural Language Processing course at the [Information Science Master's Degree](https://www.rug.nl/masters/information-science/?lang=en) at the University of Groningen, taught by [Arianna Bisazza](https://research.rug.nl/en/persons/arianna-bisazza) and [Gabriele Sarti](https://research.rug.nl/en/persons/gabriele-sarti) with the assistance of [Anjali Nair](https://nl.linkedin.com/in/anjalinair012).
**Disclaimer**: *This repository is provided without direct data access due to currently unpublished results.* _**For this reason, it is strictly forbidden to share or publish all the data associated to this repository**_. *Students will be provided with a compressed folder containing the data upon choosing a project based on this dataset. To load the dataset using 🤗 Datasets, download and unzip the provided folder and pass it to the* `load_dataset` *method as:* `datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', 'full', data_dir='path/to/unzipped/folder')`
### Languages
The language data of is in English (BCP-47 `en`) and Italian (BCP-47 `it`)
## Dataset Structure
### Data Instances
The dataset contains four configurations: `full`, `test_mask_subject`, `test_mask_modality`, `test_mask_time`. `full` contains the main `train` split in which all fields are available. The other three, `test_mask_subject`, `test_mask_modality`, `test_mask_time`, contain a `test` split each with different fields removed to avoid information leaking during evaluation. See more details in the [Data Splits](#data-splits) section.
### Data Fields
The following fields are contained in the training set:
|Field|Description|
|-----|-----------|
|`item_id` | The sentence identifier. The first digits of the number represent the document containing the sentence, while the last digit of the number represents the sentence position inside the document. Documents can contain from 3 to 5 semantically-related sentences each. |
|`subject_id` | The identifier for the translator performing the translation from scratch or post-editing task. Values: `t1`, `t2` or `t3`. |
|`modality` | The modality of the translation task. Values: `ht` (translation from scratch), `pe1` (post-editing Google Translate translations), `pe2` (post-editing [mBART](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) translations). |
|`src_text` | The original source sentence extracted from Wikinews, wikibooks or wikivoyage. |
|`mt_text` | Missing if tasktype is `ht`. Otherwise, contains the automatically-translated sentence before post-editing. |
|`tgt_text` | Final sentence produced by the translator (either via translation from scratch of `sl_text` or post-editing `mt_text`) |
|`edit_time` | Total editing time for the translation in seconds. |
|`k_total` | Total number of keystrokes for the translation. |
|`k_letter` | Total number of letter keystrokes for the translation. |
|`k_digit` | Total number of digit keystrokes for the translation. |
|`k_white` | Total number of whitespace keystrokes for the translation. |
|`k_symbol` | Total number of symbol (punctuation, etc.) keystrokes for the translation. |
|`k_nav` | Total number of navigation keystrokes (left-right arrows, mouse clicks) for the translation. |
|`k_erase` | Total number of erase keystrokes (backspace, cancel) for the translation. |
|`k_copy` | Total number of copy (Ctrl + C) actions during the translation. |
|`k_cut` | Total number of cut (Ctrl + X) actions during the translation. |
|`k_paste` | Total number of paste (Ctrl + V) actions during the translation. |
|`n_pause_geq_300` | Number of pauses of 300ms or more during the translation. |
|`len_pause_geq_300` | Total duration of pauses of 300ms or more, in milliseconds. |
|`n_pause_geq_1000` | Number of pauses of 1s or more during the translation. |
|`len_pause_geq_1000` | Total duration of pauses of 1000ms or more, in milliseconds. |
|`num_annotations` | Number of times the translator focused the texbox for performing the translation of the sentence during the translation session. E.g. 1 means the translation was performed once and never revised. |
|`n_insert` | Number of post-editing insertions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`n_delete` | Number of post-editing deletions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`n_substitute` | Number of post-editing substitutions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`n_shift` | Number of post-editing shifts (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`bleu` | Sentence-level BLEU score between MT and post-edited fields (empty for modality `ht`) computed using the [SacreBLEU](https://github.com/mjpost/sacrebleu) library with default parameters. |
|`chrf` | Sentence-level chrF score between MT and post-edited fields (empty for modality `ht`) computed using the [SacreBLEU](https://github.com/mjpost/sacrebleu) library with default parameters. |
|`ter` | Sentence-level TER score between MT and post-edited fields (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`aligned_edit` | Aligned visual representation of REF (`mt_text`), HYP (`tl_text`) and edit operations (I = Insertion, D = Deletion, S = Substitution) performed on the field. Replace `\\n` with `\n` to show the three aligned rows.|
### Data Splits
| config| train| test|
|------:|-----:|----:|
|`main` | 1170 | 120 |
#### Train Split
The `train` split contains a total of 1170 triplets (or pairs, when translation from scratch is performed) annotated with behavioral data produced during the translation. The following is an example of the subject `t3` post-editing a machine translation produced by system 2 (tasktype `pe2`) taken from the `train` split. The field `aligned_edit` is showed over three lines to provide a visual understanding of its contents.
```json
{
"item_id": 1072,
"subject_id": "t3",
"tasktype": "pe2",
"src_text": "At the beginning dress was heavily influenced by the Byzantine culture in the east.",
"mt_text": "All'inizio il vestito era fortemente influenzato dalla cultura bizantina dell'est.",
"tgt+text": "Inizialmente, l'abbigliamento era fortemente influenzato dalla cultura bizantina orientale.",
"edit_time": 45.687,
"k_total": 51,
"k_letter": 31,
"k_digit": 0,
"k_white": 2,
"k_symbol": 3,
"k_nav": 7,
"k_erase": 3,
"k_copy": 0,
"k_cut": 0,
"k_paste": 0,
"n_pause_geq_300": 9,
"len_pause_geq_300": 40032,
"n_pause_geq_1000": 5,
"len_pause_geq_1000": 38392,
"num_annotations": 1,
"n_insert": 0.0,
"n_delete": 1.0,
"n_substitute": 3.0,
"n_shift": 0.0,
"bleu": 47.99,
"chrf": 62.05,
"ter": 40.0,
"aligned_edit: "REF: all'inizio il vestito era fortemente influenzato dalla cultura bizantina dell'est.\\n
HYP: ********** inizialmente, l'abbigliamento era fortemente influenzato dalla cultura bizantina orientale.\\n
EVAL: D S S S"
}
```
The text is provided as-is, without further preprocessing or tokenization.
#### Test splits
The three `test` splits (one per configuration) contain the same 120 entries each, following the same structure as `train`. Each test split omit some of the fields to prevent leakage of information:
- In `test_mask_subject` the `subject_id` is absent, for the main task of post-editor stylometry.
- In `test_mask_modality` the following fields are absent for the modality prediction extra task: `modality`, `mt_text`, `n_insert`, `n_delete`, `n_substitute`, `n_shift`, `ter`, `bleu`, `chrf`, `aligned_edit`.
- In `test_mask_time` the following fields are absent for the time and pause prediction extra task: `edit_time`, `n_pause_geq_300`, `len_pause_geq_300`, `n_pause_geq_1000`, and `len_pause_geq_1000`.
### Dataset Creation
The dataset was parsed from PET XML files into CSV format using a script adapted from the one by [Antonio Toral](https://research.rug.nl/en/persons/antonio-toral-ruiz) found at the following link: [https://github.com/antot/postediting_novel_frontiers](https://github.com/antot/postediting_novel_frontiers)
## Additional Information
### Dataset Curators
For problems related to this 🤗 Datasets version, please contact us at [ik-nlp-course@rug.nl](mailto:ik-nlp-course@rug.nl).
### Licensing Information
It is forbidden to share or publish the data associated with this 🤗 Dataset version.
### Citation Information
No citation information is provided for this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
KTH/nst | KTH | 2023-09-05T14:29:06Z | 297 | 0 | null | [
"task_categories:automatic-speech-recognition",
"language:sv",
"license:cc0-1.0",
"region:us"
] | 2023-09-05T14:29:06Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: cc0-1.0
task_categories:
- automatic-speech-recognition
language:
- sv
---
# NST Swedish ASR Database (16 kHz) – reorganized
This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish. In this updated version, the organization of the data have been altered to improve the usefulness of the database.
In the original version of the material, the files were organized in a specific folder structure where the folder names were meaningful. However, the file names were not meaningful, and there were also cases of files with identical names in different folders. This proved to be impractical, since users had to keep the original folder structure in order to use the data. The files have been renamed, such that the file names are unique and meaningful regardless of the folder structure. The original metadata files were in spl format. These have been converted to JSON format. The converted metadata files are also anonymized and the text encoding has been converted from ANSI to UTF-8.
See the documentation file for a full description of the data and the changes made to the database.
The data is originally hosted on the National Library of Norway website.
https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-56/
Hosting on Hugging Face datasets for convenience.
Licence
CC0 1.0 Universal (CC0 1.0)
Public Domain Dedication | [
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LeverageX/book-summarization | LeverageX | 2022-01-20T23:46:26Z | 297 | 0 | null | [
"region:us"
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NYTK/HuCOLA | NYTK | 2022-10-21T16:08:35Z | 297 | 0 | null | [
"task_ids:text-simplification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:hu",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-21T16:08:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
language:
- hu
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: HuCOLA
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conditional-text-generation
task_ids:
- machine-translation
- summarization
- text-simplification
---
# Dataset Card for HuCOLA
## 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-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
[HuCOLA dataset](https://github.com/nytud/HuCOLA)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
[lnnoemi](mailto:ligeti-nagy.noemi@nytud.hu)
### Dataset Summary
This is the dataset card for the Hungarian Corpus of Linguistic Acceptability (HuCOLA), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit [HuLU](hulu.nlp.nytud.hu).
### Supported Tasks and Leaderboards
### Languages
The BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.
## Dataset Structure
### Data Instances
For each instance, there is aN id, a sentence and a label.
An example:
```
{"Sent_id": "dev_0",
"Sent": "A földek eláradtak.",
"Label": "0"}
```
### Data Fields
- Sent_id: unique id of the instances, an integer between 1 and 1000;
- Sent: a Hungarian sentence;
- label: '0' for wrong, '1' for good sentences.
### Data Splits
HuCOLA has 3 splits: *train*, *validation* and *test*.
| Dataset split | Number of sentences in the split | Proportion of the split
|---------------|----------------------------------| ---------|
| train | 7276 | 80%|
| validation | 900 |10%|
| test | 900 |10%|
The test data is distributed without the labels. To evaluate your model, please [contact us](mailto:ligeti-nagy.noemi@nytud.hu), or check [HuLU's website](hulu.nlp.nytud.hu) for an automatic evaluation (this feature is under construction at the moment). The evaluation metric is Matthew's correlation coefficient.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The data was collected by two human annotators from 3 main linguistic books on Hungarian language:
- Kiefer Ferenc (ed.) (1992), Strukturális magyar nyelvtan 1. Mondattan. Budapest, Akadémiai Kiadó.
- Alberti, Gábor and Laczkó, Tibor (eds) (2018), Syntax of Hungarian Nouns and Noun Phrases. I., II. Comprehensive grammar resources. Amsterdam University Press, Amsterdam.
- Katalin É. Kiss and Veronika Hegedűs (eds) (2021), Postpositions and Postpositional Phrases. Amsterdam: Amsterdam University Press.
The process of collecting sentences partly followed the one described in Warstadt et. al (2018). The guideline of our process is available in the repository of [HuCOLA](https://github.com/nytud/HuCOLA).
### Annotations
#### Annotation process
Each instance was annotated by 4 human annotators for its acceptability (see the annotation guidelines in the repository of [HuCOLA](https://github.com/nytud/HuCOLA)).
#### Who are the annotators?
The annotators were native Hungarian speakers (of various ages, from 20 to 67) without any linguistic backround.
## Additional Information
### Licensing Information
HuCOLA is released under the CC-BY-SA 4.0 licence.
### Citation Information
If you use this resource or any part of its documentation, please refer to:
Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis
kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. (in press)
```
@inproceedings{ligetinagy2022hulu,
title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából},
author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Váradi, T.},
booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
year={2022}
}
```
### Contributions
Thanks to [lnnoemi](https://github.com/lnnoemi) for adding this dataset. | [
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Nuwaisir/Quran_speech_recognition_kaggle | Nuwaisir | 2022-02-20T13:07:06Z | 297 | 0 | null | [
"region:us"
] | 2022-02-20T13:07:06Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This dataset can be found in Kaggle | [
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XiangPan/snli_break | XiangPan | 2021-09-20T05:45:54Z | 297 | 0 | null | [
"region:us"
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albertvillanova/tests-raw-jsonl | albertvillanova | 2021-07-06T06:21:44Z | 297 | 0 | null | [
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aliabd/hello-world | aliabd | 2022-01-21T18:44:39Z | 297 | 0 | null | [
"region:us"
] | 2022-01-21T18:44:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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alistvt/coqa | alistvt | 2022-01-23T02:44:10Z | 297 | 0 | null | [
"region:us"
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alittleie/mis_238 | alittleie | 2021-10-03T02:05:08Z | 297 | 0 | null | [
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anuragshas/bg_opus100_processed | anuragshas | 2022-01-30T14:36:57Z | 297 | 0 | null | [
"region:us"
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anuragshas/hi_opus100_processed | anuragshas | 2022-01-30T15:14:09Z | 297 | 0 | null | [
"region:us"
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anuragshas/mt_opus100_processed | anuragshas | 2022-02-02T08:14:25Z | 297 | 0 | null | [
"region:us"
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anuragshas/pa_cc100_processed | anuragshas | 2022-02-04T10:50:24Z | 297 | 0 | null | [
"region:us"
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bitmorse/kickstarter_2022-2021 | bitmorse | 2022-02-10T06:28:18Z | 297 | 1 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
castorini/msmarco_v2_doc_doc2query-t5_expansions | castorini | 2021-11-11T17:41:32Z | 297 | 0 | null | [
"region:us"
] | 2021-11-11T17:41:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- English
license: "Apache License 2.0"
---
# Dataset Summary
The repo provides queries generated for the MS MARCO v2 document corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.
# Dataset Structure
All three folds (train, dev and test) share the same corpus.
An example data entry looks as follows:
```
{
'docid': '25#0',
'title': 'Autism',
'text': 'Autism is a developmental disorder characterized by difficulties with social interaction and communication, ...'
}
```
# Load Dataset
An example to load the dataset:
```
dataset = load_dataset('castorini/msmarco_v2_doc_doc2query-t5_expansions')
```
# Citation Information
```
@article{docTTTTTquery,
title={From doc2query to {docTTTTTquery}},
author={Nogueira, Rodrigo and Lin, Jimmy},
year={2019}
}
@article{emdt5,
author = "Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin",
title = "The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models",
journal = "arXiv:2101.05667",
year = 2021,
}
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cestwc/adapted-synonym | cestwc | 2021-12-29T16:59:46Z | 297 | 0 | null | [
"region:us"
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cestwc/adapted-wikismall | cestwc | 2021-12-15T17:35:28Z | 297 | 0 | null | [
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chenghao/mc4_sw_dedup | chenghao | 2021-12-09T02:25:03Z | 297 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
chitra/contradictionNLI | chitra | 2021-12-29T10:45:19Z | 297 | 0 | null | [
"region:us"
] | 2021-12-29T10:45:19Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This data can help in solving contradiction detection problem. this data is picked from kaggle.
reference - Contradictory, My DWatson | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
classla/janes_tag | classla | 2022-10-25T07:31:04Z | 297 | 0 | null | [
"task_categories:other",
"task_ids:lemmatization",
"task_ids:part-of-speech",
"language:si",
"license:cc-by-sa-4.0",
"structure-prediction",
"normalization",
"tokenization",
"region:us"
] | 2022-10-25T07:31:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- si
license:
- cc-by-sa-4.0
task_categories:
- other
task_ids:
- lemmatization
- part-of-speech
tags:
- structure-prediction
- normalization
- tokenization
---
The dataset contains 6273 training samples, 762 validation samples and 749 test samples.
Each sample represents a sentence and includes the following features: sentence ID ('sent\_id'),
list of tokens ('tokens'), list of normalised word forms ('norms'), list of lemmas ('lemmas'),
list of Multext-East tags ('xpos\_tags), list of morphological features ('feats'),
and list of UPOS tags ('upos\_tags'), which are encoded as class labels. | [
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classla/reldi_hr | classla | 2022-10-25T07:30:56Z | 297 | 0 | null | [
"task_categories:other",
"task_ids:lemmatization",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"language:hr",
"license:cc-by-sa-4.0",
"structure-prediction",
"normalization",
"tokenization",
"region:us"
] | 2022-10-25T07:30:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- hr
license:
- cc-by-sa-4.0
task_categories:
- other
task_ids:
- lemmatization
- named-entity-recognition
- part-of-speech
tags:
- structure-prediction
- normalization
- tokenization
---
This dataset is based on 3,871 Croatian tweets that were segmented into sentences, tokens, and annotated with normalized forms, lemmas, MULTEXT-East tags (XPOS), UPOS tags and morphological features, and named entities.
The dataset contains 6339 training samples (sentences), 815 validation samples and 785 test samples.
Each sample represents a sentence and includes the following features: sentence ID ('sent\_id'),
list of tokens ('tokens'), list of normalised tokens ('norms'), list of lemmas ('lemmas'), list of UPOS tags ('upos\_tags'),
list of MULTEXT-East tags ('xpos\_tags), list of morphological features ('feats'),
and list of named entity IOB tags ('iob\_tags'), which are encoded as class labels.
If you are using this dataset in your research, please cite the following paper:
```
@article{Miličević_Ljubešić_2016,
title={Tviterasi, tviteraši or twitteraši? Producing and analysing a normalised dataset of Croatian and Serbian tweets},
volume={4},
url={https://revije.ff.uni-lj.si/slovenscina2/article/view/7007},
DOI={10.4312/slo2.0.2016.2.156-188},
number={2},
journal={Slovenščina 2.0: empirical, applied and interdisciplinary research},
author={Miličević, Maja and Ljubešić, Nikola},
year={2016},
month={Sep.},
pages={156–188} }
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
classla/reldi_sr | classla | 2022-10-25T07:30:33Z | 297 | 0 | null | [
"task_categories:other",
"task_ids:lemmatization",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"language:sr",
"license:cc-by-sa-4.0",
"structure-prediction",
"normalization",
"tokenization",
"region:us"
] | 2022-10-25T07:30:33Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- sr
license:
- cc-by-sa-4.0
task_categories:
- other
task_ids:
- lemmatization
- named-entity-recognition
- part-of-speech
tags:
- structure-prediction
- normalization
- tokenization
---
This dataset is based on 3,748 Serbian tweets that were segmented into sentences, tokens, and annotated with normalized forms, lemmas, MULTEXT-East tags (XPOS), UPOS tags and morphological features, and named entities.
The dataset contains 5462 training samples (sentences), 711 validation samples and 725 test samples.
Each sample represents a sentence and includes the following features: sentence ID ('sent\_id'),
list of tokens ('tokens'), list of normalised tokens ('norms'), list of lemmas ('lemmas'), list of UPOS tags ('upos\_tags'),
list of MULTEXT-East tags ('xpos\_tags), list of morphological features ('feats'),
and list of named entity IOB tags ('iob\_tags'), which are encoded as class labels.
If you are using this dataset in your research, please cite the following paper:
```
@article{Miličević_Ljubešić_2016,
title={Tviterasi, tviteraši or twitteraši? Producing and analysing a normalised dataset of Croatian and Serbian tweets},
volume={4},
url={https://revije.ff.uni-lj.si/slovenscina2/article/view/7007},
DOI={10.4312/slo2.0.2016.2.156-188},
number={2},
journal={Slovenščina 2.0: empirical, applied and interdisciplinary research},
author={Miličević, Maja and Ljubešić, Nikola},
year={2016},
month={Sep.},
pages={156–188} }
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ctu-aic/ctkfacts_nli | ctu-aic | 2022-11-01T06:35:47Z | 297 | 2 | null | [
"arxiv:2201.11115",
"region:us"
] | 2022-11-01T06:35:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # CTKFacts dataset for Natural Language Inference
Czech Natural Language Inference dataset of ~3K *evidence*-*claim* pairs labelled with SUPPORTS, REFUTES or NOT ENOUGH INFO veracity labels. Extracted from a round of fact-checking experiments concluded and described within the CsFEVER and [CTKFacts: Czech Datasets for Fact Verification](https://arxiv.org/abs/2201.11115) paper currently being revised for publication in LREV journal.
## Document retrieval version
Can be found at https://huggingface.co/datasets/ctu-aic/ctkfacts | [
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cylee/github-issues | cylee | 2021-12-19T19:12:55Z | 297 | 0 | null | [
"arxiv:2005.00614",
"region:us"
] | 2021-12-19T19:12:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Dataset Card for GitHub Issues
## Dataset Description
This dataset is created for the Hugging Face Datasets library course
### Dataset Summary
GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets [repository](https://github.com/huggingface/datasets). It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.
### Supported Tasks and Leaderboards
For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the `task-category-tag` with an appropriate `other:other-task-name`).
- `task-category-tag`: The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a *high/low* [metric name](https://huggingface.co/metrics/metric_name). The ([model name](https://huggingface.co/model_name) or [model class](https://huggingface.co/transformers/model_doc/model_class.html)) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at [leaderboard url]() and ranks models based on [metric name](https://huggingface.co/metrics/metric_name) while also reporting [other metric name](https://huggingface.co/metrics/other_metric_name).
### Languages
Provide a brief overview of the languages represented in the dataset. Describe relevant details about specifics of the language such as whether it is social media text, African American English,...
When relevant, please provide [BCP-47 codes](https://tools.ietf.org/html/bcp47), which consist of a [primary language subtag](https://tools.ietf.org/html/bcp47#section-2.2.1), with a [script subtag](https://tools.ietf.org/html/bcp47#section-2.2.3) and/or [region subtag](https://tools.ietf.org/html/bcp47#section-2.2.4) if available.
## Dataset Structure
### Data Instances
Provide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.
```
{
'example_field': ...,
...
}
```
Provide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.
### Data Fields
List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.
- `example_field`: description of `example_field`
Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [tagging app](https://github.com/huggingface/datasets-tagging), you will then only need to refine the generated descriptions.
### Data Splits
Describe and name the splits in the dataset if there are more than one.
Describe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.
Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:
| | Tain | Valid | Test |
| ----- | ------ | ----- | ---- |
| Input Sentences | | | |
| Average Sentence Length | | | |
## Dataset Creation
### Curation Rationale
What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?
### Source Data
This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)
#### Initial Data Collection and Normalization
Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.
If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).
If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.
#### Who are the source language producers?
State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.
If available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
Describe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
Describe other people represented or mentioned in the data. Where possible, link to references for the information.
### Annotations
If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.
#### Annotation process
If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
#### Who are the annotators?
If annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.
Describe the people or systems who originally created the annotations and their selection criteria if applicable.
If available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
Describe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
### Personal and Sensitive Information
State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).
State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).
If efforts were made to anonymize the data, describe the anonymization process.
## Considerations for Using the Data
### Social Impact of Dataset
Please discuss some of the ways you believe the use of this dataset will impact society.
The statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.
Also describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.
### Discussion of Biases
Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.
For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.
If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.
### Other Known Limitations
If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.
## Additional Information
### Dataset Curators
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
### Licensing Information
Provide the license and link to the license webpage if available.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{article_id,
author = {Author List},
title = {Dataset Paper Title},
journal = {Publication Venue},
year = {2525}
}
```
If the dataset has a [DOI](https://www.doi.org/), please provide it here.
### Contributions
[@cylee] added this dataset as part of the Hugging Face Dataset library tutorial (https://huggingface.co/course/chapter5/5?fw=tf). | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dk-crazydiv/huggingface-modelhub | dk-crazydiv | 2021-06-20T14:09:58Z | 297 | 3 | null | [
"region:us"
] | 2021-06-20T14:09:58Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ## Summary
Metadata information of all the models uploaded on [HuggingFace modelhub](https://huggingface.co/models)
Dataset was last updated on 15th June 2021. Contains information on 10,354 models (v1).
Only `train` dataset is provided
#### Update: v1.0.2: Added downloads_last_month and library data
Same dataset is available in [kaggle](https://www.kaggle.com/crazydiv/huggingface-modelhub)
## Loading data
```python
from datasets import load_dataset
modelhub_dataset = load_dataset("dk-crazydiv/huggingface-modelhub")
```
### Useful commands:
```python
modelhub_dataset["train"] # Access train subset (the only subset available)
modelhub_dataset["train"][0] # Access the dataset elements by index
modelhub_dataset["train"].features # Get the columns present in the dataset.
```
### Sample dataset:
```json
{
"downloads_last_month": 7474,
"files": [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"with-prefix-tf_model.h5"
],
"lastModified": "2021-01-13T15:08:24.000Z",
"library": "transformers",
"modelId": "albert-base-v1",
"pipeline_tag": "fill-mask",
"publishedBy": "huggingface",
"tags": [
"pytorch",
"tf",
"albert",
"masked-lm",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"fill-mask"
],
"modelCard": "Readme sample data..."
}
```
## Bugs:
Please report any bugs/improvements to me on [twitter](https://twitter.com/kartik_godawat) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dvilasuero/ag_news_error_analysis | dvilasuero | 2021-12-29T17:23:31Z | 297 | 0 | null | [
"region:us"
] | 2021-12-29T17:23:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This is a dataset created for testing purposes in the context of this tutorial: https://rubrix.readthedocs.io/en/master/tutorials/08-error_analysis_using_loss.html
You can find more details on section 5. of the tutorial and the corresponding dataset with corrected labels at https://huggingface.co/datasets/Recognai/ag_news_corrected_labels
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dvilasuero/test-dataset | dvilasuero | 2021-12-29T14:53:03Z | 297 | 0 | null | [
"region:us"
] | 2021-12-29T14:53:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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flax-sentence-embeddings/Gender_Bias_Evaluation_Set | flax-sentence-embeddings | 2021-07-26T04:14:18Z | 297 | 2 | null | [
"arxiv:1906.00591",
"region:us"
] | 2021-07-26T04:14:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | **This dataset has been created as part of the Flax/JAX community week for testing the [flax-sentence-embeddings](https://huggingface.co/flax-sentence-embeddings) Sentence Similarity models for Gender Bias but can be used for other use-cases as well related to evaluating Gender Bias.**
The Following Dataset has been created for Evaluating Gender Bias for different models, based on various stereotypical occupations.
* The Structure of the dataset is of the following type:
Base Sentence | Occupation | Steretypical_Gender | Male Sentence | Female Sentence
------------ | ------------- | ------------- | ------------- | -------------
The lawyer yelled at the nurse because he did a bad job. | nurse | female | The lawyer yelled at him because he did a bad job. | The lawyer yelled at her because she did a bad job.
* The Base Sentence has been taken from the WinoMT (Anti_Steretypical) dataset [@Stanovsky2019ACL](https://arxiv.org/abs/1906.00591).
**Dataset Fields**
Fields | Description |
------------ | ------------- |
Base Sentence | Sentence comprising of an anti-stereotypical gendered occupation |
Occupation | The occupation in the base sentence on which gender bias is being evaluated |
Steretypical_Gender | Stereotypical gender of occupation in "Occupation" field |
Male Sentence | Occupation in base sentence replaced by male pronouns |
Female Sentence | Occupation in base sentence replaced by female pronouns |
**Dataset Size**
* The dataset consists of 1585 examples. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
flax-sentence-embeddings/paws-jsonl | flax-sentence-embeddings | 2021-07-02T10:19:03Z | 297 | 0 | null | [
"region:us"
] | 2021-07-02T10:19:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Introduction
This dataset is a jsonl format for PAWS dataset from: https://github.com/google-research-datasets/paws. It only contains the `PAWS-Wiki Labeled (Final)` and
`PAWS-Wiki Labeled (Swap-only)` training sections of the original PAWS dataset. Duplicates data are removed.
Each line contains a dict in the following format:
`{"guid": <id>, "texts": [anchor, positive]}` or
`{"guid": <id>, "texts": [anchor, positive, negative]}`
positives_negatives.jsonl.gz: 24,723
positives_only.jsonl.gz: 13,487
**Total**: 38,210
## Dataset summary
[**PAWS: Paraphrase Adversaries from Word Scrambling**](https://github.com/google-research-datasets/paws)
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset. | [
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gcaillaut/pubmed | gcaillaut | 2021-10-21T15:39:47Z | 297 | 0 | null | [
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holylovenia/recam | holylovenia | 2021-10-18T03:28:53Z | 297 | 0 | null | [
"region:us"
] | 2021-10-18T03:28:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# ReCAM: Reading Comprehension of Abstract Meaning
## 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-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
This dataset is from SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning. [Original repository for the dataset and baseline code can be accessed here.](https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning)
- **Paper:** [SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning in ACL](https://aclanthology.org/2021.semeval-1.4.pdf)
- **Leaderboard:** [CodaLab](https://competitions.codalab.org/competitions/26153#learn_the_details)
### Dataset Summary
Refer to [this page](https://competitions.codalab.org/competitions/26153#learn_the_details).
## Dataset Structure
Refer to [the GitHub](https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning).
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{zheng-etal-2021-semeval,
title = "{S}em{E}val-2021 Task 4: Reading Comprehension of Abstract Meaning",
author = "Zheng, Boyuan and
Yang, Xiaoyu and
Ruan, Yu-Ping and
Ling, Zhenhua and
Liu, Quan and
Wei, Si and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.4",
doi = "10.18653/v1/2021.semeval-1.4",
pages = "37--50",
}
```
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | [
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hong/autonlp-data-zoo_test | hong | 2021-10-01T00:02:18Z | 297 | 0 | null | [
"region:us"
] | 2021-10-01T00:02:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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huggingartists/galenskaparna-and-after-shave | huggingartists | 2022-10-25T09:30:13Z | 297 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:30:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/galenskaparna-and-after-shave"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.252487 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://assets.genius.com/images/default_avatar_300.png?1629820244')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/galenskaparna-and-after-shave">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Galenskaparna & After Shave</div>
<a href="https://genius.com/artists/galenskaparna-and-after-shave">
<div style="text-align: center; font-size: 14px;">@galenskaparna-and-after-shave</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/galenskaparna-and-after-shave).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/galenskaparna-and-after-shave")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|157| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/galenskaparna-and-after-shave")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/ghost | huggingartists | 2022-10-25T09:30:19Z | 297 | 1 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:30:19Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ghost"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.184957 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/3192bff259bbe651686374ba3b8553bd.828x828x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ghost">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ghost</div>
<a href="https://genius.com/artists/ghost">
<div style="text-align: center; font-size: 14px;">@ghost</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/ghost).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/ghost")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|142| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/ghost")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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huggingartists/ghostmane | huggingartists | 2022-10-25T09:30:32Z | 297 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:30:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ghostmane"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.027776 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://assets.genius.com/images/default_avatar_300.png?1631290285')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ghostmane">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ghostmane</div>
<a href="https://genius.com/artists/ghostmane">
<div style="text-align: center; font-size: 14px;">@ghostmane</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/ghostmane).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/ghostmane")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|2| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/ghostmane")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/jah-khalib | huggingartists | 2022-10-25T09:32:00Z | 297 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:32:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/jah-khalib"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.269094 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/0fed863398263b7dc223768818883d19.300x300x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/jah-khalib">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jah Khalib</div>
<a href="https://genius.com/artists/jah-khalib">
<div style="text-align: center; font-size: 14px;">@jah-khalib</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/jah-khalib).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/jah-khalib")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|84| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/jah-khalib")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/jim-morrison | huggingartists | 2022-10-25T09:32:07Z | 297 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:32:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/jim-morrison"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.279131 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/ca9d975b4af890b1a7dedd5171157994.570x570x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/jim-morrison">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jim Morrison</div>
<a href="https://genius.com/artists/jim-morrison">
<div style="text-align: center; font-size: 14px;">@jim-morrison</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/jim-morrison).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/jim-morrison")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|252| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/jim-morrison")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/john-k-samson | huggingartists | 2022-10-25T09:32:13Z | 297 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:32:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/john-k-samson"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.128555 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/0af64278d82733c4487d404fd3703ef7.894x894x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/john-k-samson">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">John K. Samson</div>
<a href="https://genius.com/artists/john-k-samson">
<div style="text-align: center; font-size: 14px;">@john-k-samson</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/john-k-samson).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/john-k-samson")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|116| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/john-k-samson")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/john-lennon | huggingartists | 2022-10-25T09:32:20Z | 297 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:32:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/john-lennon"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 2.247193 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/de14b272004b51dea8071e7cba21cbac.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/john-lennon">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">John Lennon</div>
<a href="https://genius.com/artists/john-lennon">
<div style="text-align: center; font-size: 14px;">@john-lennon</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/john-lennon).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/john-lennon")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|2337| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/john-lennon")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/joni-mitchell | huggingartists | 2022-10-25T09:32:32Z | 297 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:32:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/joni-mitchell"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.703544 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/ed9a330b2539058076e0c48398599b09.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/joni-mitchell">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Joni Mitchell</div>
<a href="https://genius.com/artists/joni-mitchell">
<div style="text-align: center; font-size: 14px;">@joni-mitchell</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/joni-mitchell).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/joni-mitchell")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|559| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/joni-mitchell")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/sugar-ray | huggingartists | 2022-10-25T09:45:22Z | 297 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:45:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sugar-ray"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.164888 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/8b5c8fe74f6176047b2b5681e0e0e2d4.273x273x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sugar-ray">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sugar Ray</div>
<a href="https://genius.com/artists/sugar-ray">
<div style="text-align: center; font-size: 14px;">@sugar-ray</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/sugar-ray).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/sugar-ray")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|117| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/sugar-ray")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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huggingartists/sum-41 | huggingartists | 2022-10-25T09:45:34Z | 297 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:45:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sum-41"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.196472 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/7cf5f61ac4ffe9a0fd1f6a4b235b95eb.320x320x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sum-41">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sum 41</div>
<a href="https://genius.com/artists/sum-41">
<div style="text-align: center; font-size: 14px;">@sum-41</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/sum-41).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/sum-41")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|134| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/sum-41")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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huggingartists/sundara-karma | huggingartists | 2022-10-25T09:45:40Z | 297 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:45:40Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sundara-karma"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.081864 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/afbb51b0dc0e4618f79565e67991a9fd.360x360x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sundara-karma">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sundara Karma</div>
<a href="https://genius.com/artists/sundara-karma">
<div style="text-align: center; font-size: 14px;">@sundara-karma</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/sundara-karma).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/sundara-karma")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|46| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/sundara-karma")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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hyeonduck/whiteboard_abuse_dataset | hyeonduck | 2021-12-17T07:35:22Z | 297 | 0 | null | [
"region:us"
] | 2021-12-17T07:35:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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