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merve/qqp | merve | 2021-11-16T10:00:02Z | 14 | 0 | null | [
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mozilla-foundation/common_voice_5_0 | mozilla-foundation | 2023-07-29T16:00:03Z | 14 | 2 | common-voice | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | 2023-07-29T16:00:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- n<1K
ar:
- 10K<n<100K
as:
- n<1K
br:
- 10K<n<100K
ca:
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pt:
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zh-CN:
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zh-HK:
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source_datasets:
- extended|common_voice
paperswithcode_id: common-voice
pretty_name: Common Voice Corpus 5
language_bcp47:
- ab
- ar
- as
- br
- ca
- cnh
- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fr
- fy-NL
- ga-IE
- hsb
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- id
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- mn
- mt
- nl
- or
- pa-IN
- pl
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- rm-sursilv
- rm-vallader
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- ru
- rw
- sah
- sl
- sv-SE
- ta
- tr
- tt
- uk
- vi
- vot
- zh-CN
- zh-HK
- zh-TW
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not
attempt to determine the identity of speakers in the Common Voice dataset.
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Common Voice Corpus 5
## 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://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co)
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 7226 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 5591 validated hours in 54 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Abkhaz, Arabic, Assamese, Basque, Breton, Catalan, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, French, Frisian, Georgian, German, Greek, Hakha Chin, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Maltese, Mongolian, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Slovenian, Sorbian, Upper, Spanish, Swedish, Tamil, Tatar, Turkish, Ukrainian, Vietnamese, Votic, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_5_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### 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]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
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benchmark: raft
---
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multilinguality:
vi:
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source_datasets:
- extended|youtube
task_categories:
- automatic-speech-recognition
task_ids: []
Pretty_name: Youtube Casual Audio
Annotations_creators:
- crowdsourced
Language_creators:
- datlq
Languages:
- vi
Licenses:
- cc0-1.0
---
# Dataset Card for common_voice
## 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:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
[Needs More Information]
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Vietnamese
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called path and its sentence. Additional fields include accent, age, client_id, up_votes down_votes, gender, locale and segment.
`
{
'file_path': 'audio/_1OsFqkFI38_34.304_39.424.wav',
'script': 'Ik vind dat een dubieuze procedure.',
'audio': {'path': 'audio/_1OsFqkFI38_34.304_39.424.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000}
`
### Data Fields
file_path: The path to the audio file
audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
script: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train, test, validated.
The val, test, train are all data that has been reviewed, deemed of high quality and split into val, test and train.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
### Contributions
Thanks to [@datlq](https://github.com/datlq98) for adding this dataset.
| [
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pierreant-p/jcvd-or-linkedin | pierreant-p | 2021-07-14T18:26:09Z | 14 | 0 | null | [
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pierreguillou/test_datasetdict | pierreguillou | 2021-12-07T11:04:58Z | 14 | 0 | null | [
"region:us"
] | 2021-12-07T11:04:58Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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pierresi/cord | pierresi | 2021-10-13T16:47:07Z | 14 | 0 | null | [
"region:us"
] | 2021-10-13T16:47:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | CORD: A Consolidated Receipt Dataset for Post-OCR Parsing. | [
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projecte-aina/vilasum | projecte-aina | 2023-09-13T12:49:32Z | 14 | 1 | null | [
"task_categories:summarization",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-nc-4.0",
"arxiv:2202.06871",
"region:us"
] | 2023-09-13T12:49:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- ca
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets: []
task_categories:
- summarization
task_ids: []
pretty_name: casum
---
# Dataset Card for VilaSum
## 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
- **Paper:**[Sequence to Sequence Resources for Catalan](https://arxiv.org/pdf/2202.06871.pdf)
- **Point of Contact:** [Ona de Gibert Bonet](mailto:ona.degibert@bsc.es)
### Dataset Summary
VilaSum is a summarization dataset for evaluation. It is extracted from a newswire corpus crawled from the Catalan news portal [VilaWeb](https://www.vilaweb.cat/). The corpus consists of 13,843 instances that are composed by the headline and the body.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for abstractive summarization. Success on this task is typically measured by achieving a high Rouge score. The [mbart-base-ca-casum](https://huggingface.co/projecte-aina/bart-base-ca-casum) model currently achieves a 35.04.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
```
{
'summary': 'Un vídeo corrobora les agressions a dues animalistes en un correbou del Mas de Barberans',
'text': 'Noves imatges, a les quals ha tingut accés l'ACN, certifiquen les agressions i la destrucció del material d'enregistrament que han denunciat dues activistes d'AnimaNaturalis en la celebració d'un acte de bous a la plaça al Mas de Barberans (Montsià). En el vídeo es veu com unes quantes persones s'abalancen sobre les noies que reben estirades i cops mentre els intenten prendre les càmeres. Membres de la comissió taurina intervenen per aturar els presumptes agressors però es pot escoltar com part del públic victoreja la situació. Els Mossos d'Esquadra presentaran aquest dilluns al migdia l'atestat dels fets al Jutjat d'Amposta. Dissabte ja es van detenir quatre persones que van quedar en llibertat a l'espera de ser cridats pel jutge. Es tracta de tres homes i una dona de Sant Carles de la Ràpita, tots ells membres de la mateixa família.'
}
```
### Data Fields
- `summary` (str): Summary of the piece of news
- `text` (str): The text of the piece of news
### Data Splits
Due to the reduced size of the dataset, we use it only for evaluation as a test set.
- test: 13,843 examples
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.
### Source Data
#### Initial Data Collection and Normalization
We obtained each headline and its corresponding body of each news piece on [VilaWeb](https://www.vilaweb.cat/) and applied the following cleaning pipeline: deduplicating the documents, removing the documents with empty attributes, and deleting some boilerplate sentences.
#### Who are the source language producers?
The news portal [VilaWeb](https://www.vilaweb.cat/).
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Since all data comes from public websites, no anonymization process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that since the data comes from unreliable web pages, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by MT4All CEF project and the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
If you use any of these resources (datasets or models) in your work, please cite our latest preprint:
```bibtex
@misc{degibert2022sequencetosequence,
title={Sequence-to-Sequence Resources for Catalan},
author={Ona de Gibert and Ksenia Kharitonova and Blanca Calvo Figueras and Jordi Armengol-Estapé and Maite Melero},
year={2022},
eprint={2202.06871},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[N/A]
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rajeshradhakrishnan/malayalam_news | rajeshradhakrishnan | 2022-07-04T05:57:19Z | 14 | 1 | null | [
"region:us"
] | 2022-07-04T05:57:19Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ## IndicNLP News Article Classification Dataset
We used the IndicNLP text corpora to create classification datasets comprising news articles and their categories for 9 languages. The dataset is balanced across classes. The following table contains the statistics of our dataset:
| Language | Classes | Articles per Class |
| --------- | ------------------------------------------- | ------------------ |
| Bengali | entertainment, sports | 7K |
| Gujarati | business, entertainment, sports | 680 |
| Kannada | entertainment, lifestyle, sports | 10K |
| Malayalam | business, entertainment, sports, technology | 1.5K |
| Marathi | entertainment, lifestyle, sports | 1.5K |
| Oriya | business, crime, entertainment, sports | 7.5K |
| Punjabi | business, entertainment, sports, politics | 780 |
| Tamil | entertainment, politics, sport | 3.9K |
| Telugu | entertainment, business, sports | 8K |
## Citing
If you are using any of the resources, please cite the following article:
```
@article{kunchukuttan2020indicnlpcorpus,
title={AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages},
author={Anoop Kunchukuttan and Divyanshu Kakwani and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
journal={arXiv preprint arXiv:2005.00085},
}
```
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ronaldvanos/testdata | ronaldvanos | 2021-11-09T12:56:07Z | 14 | 0 | null | [
"region:us"
] | 2021-11-09T12:56:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | #this is a test dataset and should not be used by anyone
#i am not the owner of the data
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rubrix/imdb_spacy-ner | rubrix | 2022-02-22T11:10:59Z | 14 | 0 | null | [
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tommy19970714/common_voice | tommy19970714 | 2021-02-27T06:51:27Z | 14 | 0 | null | [
"region:us"
] | 2021-02-27T06:51:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | [Needs More Information]
# Dataset Card for common_voice
## 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://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://commonvoice.mozilla.org/en/datasets
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9,283 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help train the accuracy of speech recognition engines.
The dataset currently consists of 7,335 validated hours in 60 languages, but were always adding more voices and languages. Take a look at our Languages page to request a language or start contributing.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### 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]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
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valurank/PoliticalBias_Sources | valurank | 2022-10-21T13:34:55Z | 14 | 0 | null | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | 2022-10-21T13:34:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license:
- other
language:
- en
multilinguality:
- monolingual
task_categories:
- classification
task_ids:
- classification
---
# Dataset Card for PoliticalBias_Sources
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
908 rows of data containing source name of an article, the source bias and the type of source
## Languages
The text in the dataset is in English
## Dataset Structure
The dataset consists of three columns namely Source Name, Source Bias and Source Typ
## Source Data
The dataset is scrapped from https://www.allsides.com/media-bias
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yonesuke/Ising2D | yonesuke | 2022-01-18T11:50:23Z | 14 | 0 | null | [
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zloelias/lenta-ru | zloelias | 2021-11-30T21:43:38Z | 14 | 0 | null | [
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
drAbreu/bc4chemd_ner | drAbreu | 2022-10-25T10:02:51Z | 14 | 1 | bc4chemd | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:GitHub",
"language:en",
"license:unknown",
"region:us"
] | 2022-10-25T10:02:51Z | 2022-03-09T14:56:16.000Z | 2022-03-09T14:56:16 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- GitHub
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: bc4chemd
pretty_name: bc4chemd_ner
---
# Dataset Card for bc2gm_corpus
## 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:** [Github](https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/)
- **Repository:** [Github](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD)
- **Paper:** [NCBI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331692/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
* Token Classification
* Named Entity Recognition
### Languages
- English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens.
### Data Splits
```python
DatasetDict({
train: Dataset({
features: ['id', 'tokens', 'ner_tags'],
num_rows: 30683
})
validation: Dataset({
features: ['id', 'tokens', 'ner_tags'],
num_rows: 30640
})
test: Dataset({
features: ['id', 'tokens', 'ner_tags'],
num_rows: 26365
})
})
```
## Dataset Creation
### Curation Rationale
The automatic extraction of chemical information from text requires the recognition of chemical
entity mentions as one of its key steps. When developing supervised named entity recognition
(NER) systems, the availability of a large, manually annotated text corpus is desirable.
Furthermore, large corpora permit the robust evaluation and comparison of different
approaches that detect chemicals in documents.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
### Annotations
#### Annotation process
We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a
total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators,
following annotation guidelines specifically defined for this task.
#### Who are the annotators?
Expert chemistry literature curators
### Personal and Sensitive Information
It does not contain this kind of information
The abstracts of the CHEMDNER corpus were selected to be representative for all
major chemical disciplines. Each of the chemical entity mentions was manually
labeled according to its structure-associated chemical entity mention (SACEM)
class: abbreviation, family, formula, identifier, multiple, systematic and
trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study
between annotators, obtaining a percentage agreement of 91.
### Licensing Information
Unknown
### Citation Information
```latex
@article{Krallinger2015TheCC,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Martin Krallinger and Obdulia Rabal and Florian Leitner and Miguel Vazquez and David Salgado and Zhiyong Lu and Robert Leaman and Yanan Lu and Dong-Hong Ji and Daniel M. Lowe and Roger A. Sayle and Riza Theresa Batista-Navarro and Rafal Rak and Torsten Huber and Tim Rockt{\"a}schel and S{\'e}rgio Matos and David Campos and Buzhou Tang and Hua Xu and Tsendsuren Munkhdalai and Keun Ho Ryu and S. V. Ramanan and P. Senthil Nathan and Slavko Zitnik and Marko Bajec and Lutz Weber and Matthias Irmer and Saber Ahmad Akhondi and Jan A. Kors and Shuo Xu and Xin An and Utpal Kumar Sikdar and Asif Ekbal and Masaharu Yoshioka and Thaer M. Dieb and Miji Choi and Karin M. Verspoor and Madian Khabsa and C. Lee Giles and Hongfang Liu and K. E. Ravikumar and Andre Lamurias and Francisco M. Couto and Hong-Jie Dai and Richard Tzong-Han Tsai and C Ata and Tolga Can and Anabel Usie and Rui Alves and Isabel Segura-Bedmar and Paloma Mart{\'i}nez and Julen Oyarz{\'a}bal and Alfonso Valencia},
journal={Journal of Cheminformatics},
year={2015},
volume={7},
pages={S2 - S2}
}
```
### Contributions
Thanks to [@GamalC](https://github.com/GamalC) for uploading this dataset to GitHub.
| [
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0.477874815464019... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Biomedical-TeMU/CodiEsp_corpus | Biomedical-TeMU | 2022-03-11T02:24:53Z | 14 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-03-11T02:24:53Z | 2022-03-11T02:19:32.000Z | 2022-03-11T02:19:32 | ---
license: cc-by-4.0
---
## Introduction
These are the train, development, test and background sets of the CodiEsp corpus. Train and development have gold standard annotations. The unannotated background and test sets are distributed together. All documents are released in the context of the CodiEsp track for CLEF ehealth 2020 (http://temu.bsc.es/codiesp/).
The CodiEsp corpus contains manually coded clinical cases. All documents are in Spanish language and CIE10 is the coding terminology (it is the Spanish version of ICD10-CM and ICD10-PCS). The CodiEsp corpus has been randomly sampled into three subsets: the train, the development, and the test set. The train set contains 500 clinical cases, and the development and test set 250 clinical cases each. The test set contains 250 clinical cases and it is released together with the background set (with 2751 clinical cases). CodiEsp participants must submit predictions for the test and background set, but they will only be evaluated on the test set.
## Structure
Three folders: train, dev and test. Each one of them contains the files for the train, development and test corpora, respectively.
+ train and dev folders have:
+ 3 tab-separated files with the annotation information relevant for each of the 3 sub-tracks of CodiEsp.
+ A subfolder named text_files with the plain text files of the clinical cases.
+ A subfolder named text_files_en with the plain text files machine-translated to English. Due to the translation process, the text files are sentence-splitted.
+ The test folder has only text_files and text_files_en subfolders with the plain text files.
## Corpus format description
The CodiEsp corpus is distributed in plain text in UTF8 encoding, where each clinical case is stored as a single file whose name is the clinical case identifier. Annotations are released in a tab-separated file. Since the CodiEsp track has 3 sub-tracks, every set of documents (train and test) has 3 tab-separated files associated with it.
For the sub-tracks CodiEsp-D and CodiEsp-P, the file has the following fields:
articleID ICD10-code
Tab-separated files for the sub-track CodiEsp-X contain extra fields that provide the text-reference and its position:
articleID label ICD10-code text-reference reference-position
## Corpus summary statistics
The final collection of 1000 clinical cases that make up the corpus had a total of 16504 sentences, with an average of 16.5 sentences per clinical case. It contains a total of 396,988 words, with an average of 396.2 words per clinical case.
For more information, visit the track webpage: http://temu.bsc.es/codiesp/ | [
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0.33763849735260... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rocca/top-reddit-posts | rocca | 2022-03-23T05:16:33Z | 14 | 0 | null | [
"license:mit",
"region:us"
] | 2022-03-23T05:16:33Z | 2022-03-13T05:06:55.000Z | 2022-03-13T05:06:55 | ---
license: mit
---
The `post-data-by-subreddit.tar` file contains 5000 gzipped json files - one for each of the top 5000 subreddits (as roughly measured by subscriber count and comment activity). Each of those json files (e.g. `askreddit.json`) contains an array of the data for the top 1000 posts of all time.
Notes:
* I stopped crawling a subreddit's top-posts list if I reached a batch that had a post with a score less than 5, so some subreddits won't have the full 1000 posts.
* No posts comments are included. Only the posts themselves.
* See the example file `askreddit.json` in this repo if you want to see what you're getting before downloading all the data.
* The list of subreddits included are listed in `top-5k-subreddits.json`.
* NSFW subreddits have been included in the crawl, so you might have to filter them out depending on your use case.
* The Deno scraping/crawling script is included as `crawl.js`, and can be started with `deno run --allow-net --allow-read=. --allow-write=. crawl.js` once you've [installed Deno](https://deno.land/manual/getting_started/installation) and have downloaded `top-5k-subreddits.json` into the same folder as `crawl.js`. | [
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0.4328570365905761... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
chiarab/final-train | chiarab | 2022-03-13T07:29:07Z | 14 | 0 | null | [
"region:us"
] | 2022-03-13T07:29:07Z | 2022-03-13T07:28:49.000Z | 2022-03-13T07:28:49 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
chiarab/vaccine-keyword-all | chiarab | 2022-03-13T19:31:02Z | 14 | 0 | null | [
"region:us"
] | 2022-03-13T19:31:02Z | 2022-03-13T08:41:21.000Z | 2022-03-13T08:41:21 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
chiarab/sorted-with-10-lessneu | chiarab | 2022-03-13T09:10:24Z | 14 | 0 | null | [
"region:us"
] | 2022-03-13T09:10:24Z | 2022-03-13T09:06:25.000Z | 2022-03-13T09:06:25 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
chiarab/dct-keyword-us | chiarab | 2022-03-13T19:26:33Z | 14 | 0 | null | [
"region:us"
] | 2022-03-13T19:26:33Z | 2022-03-13T09:18:14.000Z | 2022-03-13T09:18:14 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
stjokerli/TextToText_record_seqio | stjokerli | 2022-03-21T13:57:22Z | 14 | 0 | null | [
"region:us"
] | 2022-03-21T13:57:22Z | 2022-03-13T09:31:33.000Z | 2022-03-13T09:31:33 | Entry not found | [
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-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
stjokerli/TextToText_wsc_seqio | stjokerli | 2022-03-18T04:57:02Z | 14 | 0 | null | [
"region:us"
] | 2022-03-18T04:57:02Z | 2022-03-13T09:57:25.000Z | 2022-03-13T09:57:25 | Entry not found | [
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-0.9104482531547546,
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
stjokerli/TextToText_axg_seqio | stjokerli | 2022-04-04T10:24:18Z | 14 | 0 | null | [
"region:us"
] | 2022-04-04T10:24:18Z | 2022-03-13T10:08:17.000Z | 2022-03-13T10:08:17 | # text-to-text format from superglue axg
# Note that RTE train and val set has been added
axg: DatasetDict({
test: Dataset({
features: ['idx', 'inputs', 'targets'],
num_rows: 356
})
train: Dataset({
features: ['idx', 'inputs', 'targets'],
num_rows: 2490
})
validation: Dataset({
features: ['idx', 'inputs', 'targets'],
num_rows: 277
})
}) | [
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0.244117915630340... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
stjokerli/TextToText_axb_seqio | stjokerli | 2022-04-04T10:25:39Z | 14 | 0 | null | [
"region:us"
] | 2022-04-04T10:25:39Z | 2022-03-13T10:08:23.000Z | 2022-03-13T10:08:23 | axb: DatasetDict({
test: Dataset({
features: ['idx', 'inputs', 'targets'],
num_rows: 1104
})
train: Dataset({
features: ['idx', 'inputs', 'targets'],
num_rows: 2490
})
validation: Dataset({
features: ['idx', 'inputs', 'targets'],
num_rows: 277
})
})
Text to text implemantion of T5
note that RTE train and validation set has been added | [
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0.38222426176071... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
joypersicanon/ph-en-text | joypersicanon | 2022-03-17T13:30:52Z | 14 | 0 | null | [
"region:us"
] | 2022-03-17T13:30:52Z | 2022-03-13T10:16:38.000Z | 2022-03-13T10:16:38 | [Needs More Information]
# Dataset Card for ph-en-text
## 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:** [Needs More Information]
- **Repository:** https://huggingface.co/datasets/joypersicanon/ph-en-text/tree/main
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Mary Joy P. Canon
### Dataset Summary
PhEnText is a large-scale and multi-domain lexical data written in Philippine English text.
It is composed of 20, 562, 265 lines from news articles, religious articles and court decisions.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
ph-en
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
id: "3128940",
text: "Why this happened should be the focus of inquiry."
### Data Splits
80:20 split for train and test data
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | [
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chiarab/uk | chiarab | 2022-03-13T19:28:01Z | 14 | 0 | null | [
"region:us"
] | 2022-03-13T19:28:01Z | 2022-03-13T19:27:43.000Z | 2022-03-13T19:27:43 | Entry not found | [
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chiarab/vax-keyword-canada | chiarab | 2022-03-13T19:32:48Z | 14 | 0 | null | [
"region:us"
] | 2022-03-13T19:32:48Z | 2022-03-13T19:32:30.000Z | 2022-03-13T19:32:30 | Entry not found | [
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chiarab/vax-keyword-uk | chiarab | 2022-03-14T05:53:58Z | 14 | 0 | null | [
"region:us"
] | 2022-03-14T05:53:58Z | 2022-03-13T19:33:44.000Z | 2022-03-13T19:33:44 | Entry not found | [
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0.5715669393539429... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
chiarab/vax-keyword-us | chiarab | 2022-03-14T05:55:21Z | 14 | 0 | null | [
"region:us"
] | 2022-03-14T05:55:21Z | 2022-03-13T19:34:59.000Z | 2022-03-13T19:34:59 | Entry not found | [
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0.5715669393539429... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
alkzzz/palui | alkzzz | 2022-03-14T07:32:35Z | 14 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-03-14T07:32:35Z | 2022-03-14T07:09:11.000Z | 2022-03-14T07:09:11 | ---
license: cc-by-4.0
---
| [
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GEM-submissions/lewtun__this-is-a-test__1647246406 | GEM-submissions | 2022-03-14T08:26:51Z | 14 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-03-14T08:26:51Z | 2022-03-14T08:26:50.000Z | 2022-03-14T08:26:50 | ---
benchmark: gem
type: prediction
submission_name: This is a test
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test
| [
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GEM-submissions/lewtun__mt5_xl__1647246454 | GEM-submissions | 2022-03-14T08:27:39Z | 14 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-03-14T08:27:39Z | 2022-03-14T08:27:38.000Z | 2022-03-14T08:27:38 | ---
benchmark: gem
type: prediction
submission_name: mT5_xl
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: mT5_xl
| [
-0.14999501407146454,
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0.4069474637508392... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Jiejie/asr_book_lm_v2.0 | Jiejie | 2022-03-14T10:45:10Z | 14 | 0 | null | [
"region:us"
] | 2022-03-14T10:45:10Z | 2022-03-14T10:45:06.000Z | 2022-03-14T10:45:06 | Entry not found | [
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Jiejie/asr_book_lm_v2.1 | Jiejie | 2022-03-14T17:40:48Z | 14 | 0 | null | [
"region:us"
] | 2022-03-14T17:40:48Z | 2022-03-14T17:02:31.000Z | 2022-03-14T17:02:31 | Entry not found | [
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cgarciae/cartoonset | cgarciae | 2022-03-23T19:12:10Z | 14 | 11 | null | [
"size_categories:10K<n<100K",
"license:cc-by-4.0",
"arxiv:1711.05139",
"region:us"
] | 2022-03-23T19:12:10Z | 2022-03-14T23:35:29.000Z | 2022-03-14T23:35:29 | ---
pretty_name: Cartoon Set
size_categories:
- 10K<n<100K
task_categories:
- image
- computer-vision
- generative-modelling
license: cc-by-4.0
---
# Dataset Card for Cartoon Set
## Table of Contents
- [Dataset Card for Cartoon Set](#dataset-card-for-cartoon-set)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Usage](#usage)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://google.github.io/cartoonset/
- **Repository:** https://github.com/google/cartoonset/
- **Paper:** XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[Cartoon Set](https://google.github.io/cartoonset/) is a collection of random, 2D cartoon avatar images. The cartoons vary in 10 artwork categories, 4 color categories, and 4 proportion categories, with a total of ~10^13 possible combinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes.
#### Usage
`cartoonset` provides the images as PNG byte strings, this gives you a bit more flexibility into how to load the data. Here we show 2 ways:
**Using PIL:**
```python
import datasets
from io import BytesIO
from PIL import Image
ds = datasets.load_dataset("cgarciae/cartoonset", "10k") # or "100k"
def process_fn(sample):
img = Image.open(BytesIO(sample["img_bytes"]))
...
return {"img": img}
ds = ds.map(process_fn, remove_columns=["img_bytes"])
```
**Using TensorFlow:**
```python
import datasets
import tensorflow as tf
hfds = datasets.load_dataset("cgarciae/cartoonset", "10k") # or "100k"
ds = tf.data.Dataset.from_generator(
lambda: hfds,
output_signature={
"img_bytes": tf.TensorSpec(shape=(), dtype=tf.string),
},
)
def process_fn(sample):
img = tf.image.decode_png(sample["img_bytes"], channels=3)
...
return {"img": img}
ds = ds.map(process_fn)
```
**Additional features:**
You can also access the features that generated each sample e.g:
```python
ds = datasets.load_dataset("cgarciae/cartoonset", "10k+features") # or "100k+features"
```
Apart from `img_bytes` these configurations add a total of 18 * 2 additional `int` features, these come in `{feature}`, `{feature}_num_categories` pairs where `num_categories` indicates the number of categories for that feature. See [Data Fields](#data-fields) for the complete list of features.
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```python
{
'img_bytes': b'0x...',
}
```
If `+features` is added to the dataset name, the following additional fields are provided:
```python
{
'img_bytes': b'0x...',
'eye_angle': 0,
'eye_angle_num_categories': 3,
'eye_lashes': 0,
'eye_lashes_num_categories': 2,
'eye_lid': 0,
'eye_lid_num_categories': 2,
'chin_length': 2,
'chin_length_num_categories': 3,
...
}
```
### Data Fields
- `img_bytes`: A byte string containing the raw data of a 500x500 PNG image.
If `+features` is appended to the dataset name, the following additional `int32` fields are provided:
- `eye_angle`
- `eye_angle_num_categories`
- `eye_lashes`
- `eye_lashes_num_categories`
- `eye_lid`
- `eye_lid_num_categories`
- `chin_length`
- `chin_length_num_categories`
- `eyebrow_weight`
- `eyebrow_weight_num_categories`
- `eyebrow_shape`
- `eyebrow_shape_num_categories`
- `eyebrow_thickness`
- `eyebrow_thickness_num_categories`
- `face_shape`
- `face_shape_num_categories`
- `facial_hair`
- `facial_hair_num_categories`
- `facial_hair_num_categories`
- `facial_hair_num_categories`
- `hair`
- `hair_num_categories`
- `hair_num_categories`
- `hair_num_categories`
- `eye_color`
- `eye_color_num_categories`
- `face_color`
- `face_color_num_categories`
- `hair_color`
- `hair_color_num_categories`
- `glasses`
- `glasses_num_categories`
- `glasses_color`
- `glasses_color_num_categories`
- `eyes_slant`
- `eye_slant_num_categories`
- `eyebrow_width`
- `eyebrow_width_num_categories`
- `eye_eyebrow_distance`
- `eye_eyebrow_distance_num_categories`
### Data Splits
Train
## Dataset Creation
### Licensing Information
This data is licensed by Google LLC under a Creative Commons Attribution 4.0 International License.
### Citation Information
```
@article{DBLP:journals/corr/abs-1711-05139,
author = {Amelie Royer and
Konstantinos Bousmalis and
Stephan Gouws and
Fred Bertsch and
Inbar Mosseri and
Forrester Cole and
Kevin Murphy},
title = {{XGAN:} Unsupervised Image-to-Image Translation for many-to-many Mappings},
journal = {CoRR},
volume = {abs/1711.05139},
year = {2017},
url = {http://arxiv.org/abs/1711.05139},
eprinttype = {arXiv},
eprint = {1711.05139},
timestamp = {Mon, 13 Aug 2018 16:47:38 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1711-05139.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
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0.1020001396536... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rubrix/go_emotions_training | rubrix | 2022-03-15T12:40:43Z | 14 | 0 | null | [
"region:us"
] | 2022-03-15T12:40:43Z | 2022-03-15T12:40:39.000Z | 2022-03-15T12:40:39 | Entry not found | [
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0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Jiejie/asr_book_lm_v2.3 | Jiejie | 2022-03-15T15:00:56Z | 14 | 0 | null | [
"region:us"
] | 2022-03-15T15:00:56Z | 2022-03-15T15:00:54.000Z | 2022-03-15T15:00:54 | Entry not found | [
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-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Hiruni99/eng-sin-laws-and-acts | Hiruni99 | 2022-03-15T20:20:48Z | 14 | 0 | null | [
"region:us"
] | 2022-03-15T20:20:48Z | 2022-03-15T20:19:37.000Z | 2022-03-15T20:19:37 | Entry not found | [
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-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rubrix/research_titles_multi-label | rubrix | 2022-03-15T22:49:45Z | 14 | 0 | null | [
"region:us"
] | 2022-03-15T22:49:45Z | 2022-03-15T22:49:41.000Z | 2022-03-15T22:49:41 | Entry not found | [
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-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rubrix/go_emotions_multi-label | rubrix | 2022-03-15T23:00:18Z | 14 | 0 | null | [
"region:us"
] | 2022-03-15T23:00:18Z | 2022-03-15T23:00:15.000Z | 2022-03-15T23:00:15 | Entry not found | [
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elricwan/roberta-data | elricwan | 2022-03-16T04:44:42Z | 14 | 0 | null | [
"region:us"
] | 2022-03-16T04:44:42Z | 2022-03-15T23:11:00.000Z | 2022-03-15T23:11:00 | Entry not found | [
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willcai/wav2vec2_common_voice_accents_3 | willcai | 2022-03-16T04:35:20Z | 14 | 0 | null | [
"region:us"
] | 2022-03-16T04:35:20Z | 2022-03-16T03:42:32.000Z | 2022-03-16T03:42:32 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jorge-henao/disco_poetry_spanish | jorge-henao | 2022-03-17T03:19:06Z | 14 | 2 | null | [
"region:us"
] | 2022-03-17T03:19:06Z | 2022-03-16T03:42:59.000Z | 2022-03-16T03:42:59 | # DISCO: Diachronic Spanish Sonnet Corpus
[](https://zenodo.org/badge/latestdoi/103841064)
The Diachronic Spanish Sonnet Corpus (DISCO) contains sonnets in Spanish in CSV, between the 15th and the 20th centuries (4303 sonnets by 1215 authors from 22 different countries). It includes well-known authors, but also less canonized ones.
This is a CSV compilation taken from the plain text corpus v4 published on git https://github.com/pruizf/disco/tree/v4. It includes the title, author, age and text metadata.
<br><br>
| [
-0.5798288583755493,
-0.12595118582248688,
0.16114045679569244,
0.49312469363212585,
-0.4865773916244507,
0.30353012681007385,
0.07260479032993317,
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0.7460135221481323,
0.7817480564117432,
-0.724733293056488,
-0.8375513553619385,
-0.36768224835395813,
0.4699554741382599... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
crabz/stsb-sk | crabz | 2022-10-23T05:13:41Z | 14 | 0 | null | [
"task_ids:semantic-similarity-scoring",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|stsb_multi_mt",
"language:sk",
"license:unknown",
"region:us"
] | 2022-10-23T05:13:41Z | 2022-03-16T10:20:28.000Z | 2022-03-16T10:20:28 | ---
annotations_creators:
- other
language_creators:
- other
language:
- sk
language_bcp47:
- sk-SK
license:
- unknown
multilinguality:
- monolingual
pretty_name: stsb-sk
size_categories:
- 1K<n<10K
source_datasets:
- extended|stsb_multi_mt
task_categories:
- text-scoring
task_ids:
- semantic-similarity-scoring
---
Retrieving the 50th example from the train set:
```
> print(dataset['train']['sentence1'][0][50])
Muž hrá na gitare.
> print(dataset['train']['sentence2'][0][50])
Chlapec hrá na gitare.
> print(dataset['train']['similarity_score'][0][50])
3.200000047683716
```
For score explanation see [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt).
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
davanstrien/newspaper_navigator_people | davanstrien | 2022-03-16T15:28:56Z | 14 | 0 | null | [
"region:us"
] | 2022-03-16T15:28:56Z | 2022-03-16T15:28:43.000Z | 2022-03-16T15:28:43 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ttxy/kaggle | ttxy | 2022-03-17T16:00:50Z | 14 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-03-17T16:00:50Z | 2022-03-17T15:02:27.000Z | 2022-03-17T15:02:27 | ---
license: apache-2.0
---
kaggle datasets | [
-0.05524672940373421,
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0.18057791888713837,
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ttxy/nlp | ttxy | 2022-07-24T05:58:39Z | 14 | 0 | null | [
"region:us"
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shivam/split | shivam | 2022-03-17T18:32:09Z | 14 | 0 | null | [
"region:us"
] | 2022-03-17T18:32:09Z | 2022-03-17T18:29:43.000Z | 2022-03-17T18:29:43 | Entry not found | [
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malteos/test2 | malteos | 2022-10-23T05:14:36Z | 14 | 0 | cnn-daily-mail-1 | [
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"region:us"
] | 2022-10-23T05:14:36Z | 2022-03-18T10:18:42.000Z | 2022-03-18T10:18:42 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- conditional-text-generation
task_ids:
- summarization
paperswithcode_id: cnn-daily-mail-1
pretty_name: CNN / Daily Mail
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [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:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### 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
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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indonesian-nlp/eli5_id | indonesian-nlp | 2022-03-18T20:04:52Z | 14 | 3 | null | [
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tomekkorbak/pile-curse-chunk-24 | tomekkorbak | 2022-03-18T22:05:43Z | 14 | 0 | null | [
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] | 2022-03-18T22:05:43Z | 2022-03-18T22:05:10.000Z | 2022-03-18T22:05:10 | Entry not found | [
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JennyGub/PrivTest | JennyGub | 2022-03-19T09:56:10Z | 14 | 0 | null | [
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scjnugacj/scjn_dataset_corpus_tesis | scjnugacj | 2022-10-23T05:16:49Z | 14 | 0 | null | [
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:es",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T05:16:49Z | 2022-03-19T18:10:59.000Z | 2022-03-19T18:10:59 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- es
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Corpus tesis de la SCJN
size_categories:
- unknown
source_datasets:
- original
task_categories: []
task_ids: []
---
# Corpus tesis de la SCJN
En su primera versión contiene textos correspondientes a las tesis de la décima y undécima época publicadas al mes de febrero del 2022 por la SCJN.
## Dataset Structure
### Data Instances
Un ejemplo de 'train' se ve de la siguiente forma:
```
{
'id': '3',
'text': 'a la luz de las disposiciones del sistema de derechos humanos, los principios tanto de buena fe como de protección de las apariencias constituyen un límite tendente a evitar el dolo para el disfuncional ejercicio de los actos procesales, al cumplir con la función de colmar las inevitables lagunas legales, en tanto que la norma sólo previene abusos comunes, prohibiéndolos en forma enunciativa, porque de considerarlos limitativamente, muchas conductas o declaraciones contrarias a otras precedentes y, por tanto, indebidas, escaparían de la regulación. ambos principios sirven para analizar el caso en el que, en una primera ejecutoria de amparo promovido contra el auto de vinculación a proceso, se declaró irregularmente llevada a cabo una diligencia de reconocimiento de una persona por una fotografía (imputado), al inobservarse las formas procesales, por lo que en cumplimiento con la sentencia, se dictó auto de no vinculación a proceso y, en atención al deber de investigar conforme a los parámetros convencionales, la autoridad practicó una posterior diligencia, esta vez conforme a las disposiciones adjetivas que la rigen; sin embargo, si el defensor se retiró sin firmarla, aduciendo que lo haría posteriormente, sin que así se hubiera logrado, no obstante las gestiones tendientes a ello por la autoridad investigadora, quien pormenorizadamente las detalló en una certificación. actuación que debe ser sometida en cada caso al escrutinio constitucional, considerando que no puede alegar la nulidad quien ha incurrido conscientemente a su producción, porque buscaría aprovecharse de su personal dolo, al provocar daños por medio del uso desviado de medios legales inicialmente legítimos, si se les considera aisladamente. ahora bien, ponderado el caso concreto, se advierte que no obstante alegar en favor de su defenso el propio dolo, se produjeron las consecuencias inherentes a la diligencia en los términos establecidos en la norma, pues incluso consta que intervino activamente en la diligencia; lo que conduce a estimar infundado el agravio expuesto en el sentido de que debe negársele validez, al tender a beneficiar al quejoso del dolo del defensor expresado en retirarse sin firmar, indicando que regresaría a hacerlo, sin que hubiera actuado conforme a esa manifestación precedente, pretendiendo que, de prosperar la falta de formalidad en la segunda diligencia, la cual ahora le es atribuible, afectaría la expectativa creada en otros sujetos de derecho, en la especie, las víctimas, incluso, el exceso en el ejercicio de la acción constitucional alentaría la práctica viciosa de actos cuyos frutos serían aprovechables por quienes los realizan y, por otra parte, tanto las autoridades investigadoras como los tribunales se harían en alguna forma partícipes de ese proceder irregular, si consideraran permitido ese comportamiento sólo porque la ley omitió prohibirlo, incumpliendo las primeras con el deber de investigar la verdad conforme a los parámetros convencionales y, los segundos, al otorgarles credibilidad.'
}
```
### Data Fields
Los campos son los mismos para todos los splits.
- `id`: a `string` feature.
- `text`: a `string` features.
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|scjn_corpus_tesis|27913|0|0|
## Dataset Creation
### Annotations
### Dataset Curators
Ana Gabriela Palomeque Ortiz, from SCJN - Unidad General de Administración del Conocimiento Jurídico.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Other Known Limitations
La información contenida en este dataset es para efectos demostrativos y no representa una fuente oficial de la Suprema Corte de Justicia de la Nación.
## License
<br/>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/deed.es">Attribution-ShareAlike 4.0 International License</a>.
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hackathon-pln-es/MESD | hackathon-pln-es | 2022-03-25T18:15:07Z | 14 | 6 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-03-25T18:15:07Z | 2022-03-19T18:39:32.000Z | 2022-03-19T18:39:32 | ---
license: cc-by-4.0
Duville, Mathilde Marie; Alonso-Valerdi, Luz Maria; Ibarra, David (2022), “Mexican Emotional Speech Database (MESD)”, Mendeley Data, V5, doi: 10.17632/cy34mh68j9.5
---
# Dataset Card for MESD
## 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://data.mendeley.com/datasets/cy34mh68j9/5
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Contiene los datos de la base MESD procesados para hacer 'finetuning' de un modelo 'Wav2Vec' en el Hackaton organizado por 'Somos NLP'.
Ejemplo de referencia:
https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/audio_classification.ipynb
Hemos accedido a la base MESD para obtener ejemplos.
Breve descripción de los autores de la base MESD:
"La Base de Datos del Discurso Emocional Mexicano (MESD en inglés) proporciona enunciados de una sola palabra para las prosodias afectivas de ira, asco, miedo, felicidad, neutro y tristeza con conformación cultural mexicana. El MESD ha sido pronunciado por actores adultos y niños no profesionales: Se dispone de 3 voces femeninas, 2 masculinas y 6 infantiles. Las palabras de los enunciados emocionales y neutros proceden de dos corpus: (corpus A) compuesto por sustantivos y adjetivos que se repiten a través de prosodias emocionales y tipos de voz (femenina, masculina, infantil), y (corpus B) que consiste en palabras controladas por edad de adquisición, frecuencia de uso, familiaridad, concreción, valencia, excitación y clasificaciones de dimensionalidad de emociones discretas.
Las grabaciones de audio se realizaron en un estudio profesional con los siguientes materiales (1) un micrófono Sennheiser e835 con una respuesta de frecuencia plana (100 Hz a 10 kHz), (2) una interfaz de audio Focusrite Scarlett 2i4 conectada al micrófono con un cable XLR y al ordenador, y (3) la estación de trabajo de audio digital REAPER (Rapid Environment for Audio Production, Engineering, and Recording). Los archivos de audio se almacenaron como una secuencia de 24 bits con una frecuencia de muestreo de 48000Hz. La amplitud de las formas de onda acústicas se reescaló entre -1 y 1.
Se crearon dos versiones con reducción de la naturalidad de los locutores a partir de expresiones emocionales humanas para voces femeninas del corpus B. En concreto, la naturalidad se redujo progresivamente de las voces humanas al nivel 1 al nivel 2. En particular, se editaron la duración y el tono medio en las sílabas acentuadas para reducir la diferencia entre las sílabas acentuadas y las no acentuadas. En los enunciados completos, se redujeron las relaciones F2/F1 y F3/F1 editando las frecuencias F2 y F3. También se redujo la intensidad de los armónicos 1 y 4. "
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Español
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
Origen: texto que indica si se trata del conjunto de datos MESD original o los casos 'Speaker-embedded naturalness-reduced female voices' donde los autores han generado de forma sintética nuevos datos transformando algunas de las instancias de los audios originales.
Palabra: texto de la palabra que se ha leído.
Emoción: texto de la emoción a la que representa: Valores: 'Enojo', 'Felicidad', 'Miedo', 'Neutral', 'Disgusto', 'Tristeza'.
InfoActor: texto que indica si la voz es de 'Niño', 'Hombre', 'Mujer'.
AudioArray: audio array, remuestreado a 16 Khz.
### Data Splits
Train: 891 ejemplos, mezcla de casos MESD y 'Speaker-embedded naturalness-reduced female voices'.
Validation: 130 ejemplos, todos casos MESD.
Test: 129 ejemplos, todos casos MESD.
## Dataset Creation
### Curation Rationale
Unir los tres subconjuntos de datos y procesarlos para la tarea de finetuning, acorde al input esperado por el modelo Wav2Vec.
### Source Data
#### Initial Data Collection and Normalization
Acceso a los datos en bruto:
https://data.mendeley.com/datasets/cy34mh68j9/5
Conversión a audio arra y remuestreo a 16 Khz.
#### Who are the source language producers?
Duville, Mathilde Marie; Alonso-Valerdi, Luz Maria; Ibarra, David (2022), “Mexican Emotional Speech Database (MESD)”, Mendeley Data, V5, doi: 10.17632/cy34mh68j9.5
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Creative Commons, [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
Duville, Mathilde Marie; Alonso-Valerdi, Luz Maria; Ibarra, David (2022), “Mexican Emotional Speech Database (MESD)”, Mendeley Data, V5, doi: 10.17632/cy34mh68j9.5
```
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Actor Description
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voidful/asr_glue_train | voidful | 2022-04-01T18:58:14Z | 14 | 0 | null | [
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dannyvas23/textosuicidios | dannyvas23 | 2022-03-21T00:03:08Z | 14 | 0 | null | [
"license:afl-3.0",
"region:us"
] | 2022-03-21T00:03:08Z | 2022-03-20T17:50:26.000Z | 2022-03-20T17:50:26 | ---
license: afl-3.0
---
| [
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dannyvas23/notas_suicidios | dannyvas23 | 2022-03-21T01:37:37Z | 14 | 1 | null | [
"license:afl-3.0",
"region:us"
] | 2022-03-21T01:37:37Z | 2022-03-21T01:18:47.000Z | 2022-03-21T01:18:47 | ---
license: afl-3.0
---
| [
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Splend1dchan/phone-mnli | Splend1dchan | 2022-03-21T03:32:35Z | 14 | 0 | null | [
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] | 2022-03-21T03:32:35Z | 2022-03-21T03:14:55.000Z | 2022-03-21T03:14:55 | Entry not found | [
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rubrix/frases_muchocine | rubrix | 2022-03-21T09:32:01Z | 14 | 0 | null | [
"region:us"
] | 2022-03-21T09:32:01Z | 2022-03-21T09:31:43.000Z | 2022-03-21T09:31:43 | Entry not found | [
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NoCaptain/MyTwitter | NoCaptain | 2022-03-21T14:51:28Z | 14 | 0 | null | [
"region:us"
] | 2022-03-21T14:51:28Z | 2022-03-21T14:14:44.000Z | 2022-03-21T14:14:44 | Twitter 3.21 | [
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blo05/cleaned_wiki_en | blo05 | 2022-03-30T10:12:38Z | 14 | 0 | null | [
"region:us"
] | 2022-03-30T10:12:38Z | 2022-03-21T15:55:39.000Z | 2022-03-21T15:55:39 | Cleaned wikipedia dataset | [
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rubrix/frases_muchocine_NER | rubrix | 2022-03-21T17:23:54Z | 14 | 0 | null | [
"region:us"
] | 2022-03-21T17:23:54Z | 2022-03-21T17:23:43.000Z | 2022-03-21T17:23:43 | Entry not found | [
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rubrix/muchocine_ner | rubrix | 2022-03-21T20:23:27Z | 14 | 0 | null | [
"region:us"
] | 2022-03-21T20:23:27Z | 2022-03-21T20:23:04.000Z | 2022-03-21T20:23:04 | Entry not found | [
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rubrix/pococine_textcat | rubrix | 2022-03-21T21:31:15Z | 14 | 0 | null | [
"region:us"
] | 2022-03-21T21:31:15Z | 2022-03-21T20:36:33.000Z | 2022-03-21T20:36:33 | Entry not found | [
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rubrix/muchocine_aspects | rubrix | 2022-03-21T21:36:58Z | 14 | 0 | null | [
"region:us"
] | 2022-03-21T21:36:58Z | 2022-03-21T21:36:36.000Z | 2022-03-21T21:36:36 | Entry not found | [
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Carlos89apc/TraductorES_Kichwa | Carlos89apc | 2022-03-22T14:04:09Z | 14 | 0 | null | [
"license:gpl",
"region:us"
] | 2022-03-22T14:04:09Z | 2022-03-22T14:03:19.000Z | 2022-03-22T14:03:19 | ---
license: gpl
---
| [
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sayalaruano/FakeNewsSpanish_Kaggle2 | sayalaruano | 2022-03-22T15:02:43Z | 14 | 0 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-03-22T15:02:43Z | 2022-03-22T15:01:36.000Z | 2022-03-22T15:01:36 | ---
license: cc-by-nc-sa-4.0
---
This dataset was obtained from: https://www.kaggle.com/datasets/zulanac/fake-and-real-news | [
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erikacardenas300/Zillow-Text-Listings | erikacardenas300 | 2022-03-23T01:47:24Z | 14 | 2 | null | [
"region:us"
] | 2022-03-23T01:47:24Z | 2022-03-22T19:24:10.000Z | 2022-03-22T19:24:10 | Please cite: E. Cardenas., et al. “A Comparison of House Price Classification with Structured and Unstructured Text Data.” Published in AAAI FLAIRS-35. 2022. | [
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Mnauel/MESD | Mnauel | 2022-03-22T22:44:36Z | 14 | 0 | null | [
"region:us"
] | 2022-03-22T22:44:36Z | 2022-03-22T22:44:31.000Z | 2022-03-22T22:44:31 | Entry not found | [
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0.5715669393539429... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sichenzhong/squad_v2_back_trans_aug | sichenzhong | 2022-03-30T00:59:32Z | 14 | 0 | null | [
"region:us"
] | 2022-03-30T00:59:32Z | 2022-03-23T00:53:30.000Z | 2022-03-23T00:53:30 | Entry not found | [
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0.5715669393539429... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
CohleM/Classification | CohleM | 2022-03-23T12:20:42Z | 14 | 0 | null | [
"region:us"
] | 2022-03-23T12:20:42Z | 2022-03-23T12:20:00.000Z | 2022-03-23T12:20:00 | Entry not found | [
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0.5715669393539429... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GEM-submissions/lewtun__this-is-a-test-name__1648048960 | GEM-submissions | 2022-03-23T15:22:42Z | 14 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-03-23T15:22:42Z | 2022-03-23T15:22:40.000Z | 2022-03-23T15:22:40 | ---
benchmark: gem
type: prediction
submission_name: This is a test name
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test name
| [
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Rakesharma21/transliterate-eng-hi | Rakesharma21 | 2022-03-23T16:07:26Z | 14 | 0 | null | [
"region:us"
] | 2022-03-23T16:07:26Z | 2022-03-23T16:00:46.000Z | 2022-03-23T16:00:46 | Entry not found | [
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polinaeterna/test_encode_example | polinaeterna | 2022-03-23T16:37:46Z | 14 | 0 | null | [
"region:us"
] | 2022-03-23T16:37:46Z | 2022-03-23T16:36:25.000Z | 2022-03-23T16:36:25 | Entry not found | [
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/ae_photos | huggan | 2022-04-12T13:56:12Z | 14 | 0 | null | [
"arxiv:1703.10593",
"region:us"
] | 2022-04-12T13:56:12Z | 2022-03-23T21:00:46.000Z | 2022-03-23T21:00:46 | This dataset is part of the CycleGAN datasets, originally hosted here: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
# Citation
```
@article{DBLP:journals/corr/ZhuPIE17,
author = {Jun{-}Yan Zhu and
Taesung Park and
Phillip Isola and
Alexei A. Efros},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks},
journal = {CoRR},
volume = {abs/1703.10593},
year = {2017},
url = {http://arxiv.org/abs/1703.10593},
eprinttype = {arXiv},
eprint = {1703.10593},
timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/ZhuPIE17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
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doctorlan/bert-amz-c | doctorlan | 2022-03-24T05:34:21Z | 14 | 0 | null | [
"region:us"
] | 2022-03-24T05:34:21Z | 2022-03-24T05:32:05.000Z | 2022-03-24T05:32:05 | Entry not found | [
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GEM-submissions/lewtun__this-is-a-test-name__1648111972 | GEM-submissions | 2022-03-24T08:52:55Z | 14 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-03-24T08:52:55Z | 2022-03-24T08:52:52.000Z | 2022-03-24T08:52:52 | ---
benchmark: gem
type: prediction
submission_name: This is a test name
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test name
| [
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M-Quan/sv_corpora_parliament_processed | M-Quan | 2022-03-24T09:05:17Z | 14 | 0 | null | [
"region:us"
] | 2022-03-24T09:05:17Z | 2022-03-24T09:04:57.000Z | 2022-03-24T09:04:57 | Entry not found | [
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0.5715669393539429... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Jira/mao | Jira | 2022-03-24T10:10:27Z | 14 | 0 | null | [
"license:gpl",
"region:us"
] | 2022-03-24T10:10:27Z | 2022-03-24T09:24:23.000Z | 2022-03-24T09:24:23 | ---
license: gpl
---
| [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GEM-submissions/lewtun__this-is-a-test-name__1648137608 | GEM-submissions | 2022-03-24T16:00:11Z | 14 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-03-24T16:00:11Z | 2022-03-24T16:00:09.000Z | 2022-03-24T16:00:09 | ---
benchmark: gem
type: prediction
submission_name: This is a test name
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test name
| [
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-0.01758323237299919,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
wesamhaddad14/spanishNLP | wesamhaddad14 | 2022-03-24T16:46:39Z | 14 | 0 | null | [
"region:us"
] | 2022-03-24T16:46:39Z | 2022-03-24T16:36:16.000Z | 2022-03-24T16:36:16 | # Dataset Card for SpanishNLP
## 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:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Spanish Poems and their Authors and titles
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### 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
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | [
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Openmindedness/mc_chat_scraped_from_toxigon_anarchy | Openmindedness | 2022-03-24T17:13:13Z | 14 | 0 | null | [
"license:cc",
"region:us"
] | 2022-03-24T17:13:13Z | 2022-03-24T17:03:45.000Z | 2022-03-24T17:03:45 | ---
license: cc
---
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huggan/AFHQ | huggan | 2022-03-24T19:28:07Z | 14 | 1 | null | [
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DrishtiSharma/MESD-Processed-Dataset-v2 | DrishtiSharma | 2022-03-24T19:48:54Z | 14 | 0 | null | [
"region:us"
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