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8158cffcfc337e891fe287e8e0a3502752accad1
|
NBayer/test_6_rows
|
[
"license:openrail",
"region:us"
] |
2023-03-12T13:50:59+00:00
|
{"license": "openrail"}
|
2023-03-12T13:51:24+00:00
|
|
8eb9bb3d01402674cd1c36811634e3b33adb30c6
|
Den4ikAI/russian_code_qa
|
[
"license:mit",
"region:us"
] |
2023-03-12T13:52:14+00:00
|
{"license": "mit"}
|
2023-03-12T13:52:53+00:00
|
|
65a073f2c48401410b264213229a6c52417f367a
|
# Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese
This is the official repository for the UIT-ViCTSD dataset from the paper [Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese](https://arxiv.org/pdf/2103.10069.pdf), which was accepted at the [IEA/AIE 2021](https://ieaaie2021.wordpress.com/list-of-accepted-papers/).
# Citation Information
The provided dataset is only used for research purposes!
```
@InProceedings{nguyen2021victsd,
author="Nguyen, Luan Thanh and Van Nguyen, Kiet and Nguyen, Ngan Luu-Thuy",
title="Constructive and Toxic Speech Detection for Open-Domain Social Media Comments in Vietnamese",
booktitle="Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="572--583"
}
```
## Abstract
The rise of social media has led to the increasing of comments on online forums. However, there still exists invalid comments which are not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we obtain F1-scores of 78.59% and 59.40% for classifying constructive and toxic comments, respectively. Besides, we implement various baseline models as traditional Machine Learning and Deep Neural Network-Based models to evaluate the dataset. With the results, we can solve several tasks on the online discussions and develop the framework for identifying constructiveness and toxicity of Vietnamese social media comments automatically.
## Dataset
The ViCTSD dataset is consist of 10,000 human-annotated comments on 10 domains from Vietnamese users' comments on social media.
The dataset is divided into three parts as below:
1. Train set: 7,000 comments
2. Valid set: 2,000 comments
3. Test set: 1,000 comments
## Contact
Please feel free to contact us by email [email protected] if you have any further information!
|
tarudesu/ViCTSD
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:vi",
"arxiv:2103.10069",
"region:us"
] |
2023-03-12T14:16:24+00:00
|
{"language": ["vi"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "Vietnamese Constructive and Toxic Speech Detection Dataset"}
|
2023-11-28T07:20:27+00:00
|
35df84191d2b3b7cbc43d5d2d864d785e5754e00
|
# Dataset Card for "tehranen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
k0ntra/tehranen
|
[
"region:us"
] |
2023-03-12T14:17:34+00:00
|
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"float32"}, {"name": "731", "dtype": "float32"}, {"name": "732", "dtype": "float32"}, {"name": "733", "dtype": "float32"}, {"name": "734", "dtype": "float32"}, {"name": "735", "dtype": "float32"}, {"name": "736", "dtype": "float32"}, {"name": "737", "dtype": "float32"}, {"name": "738", "dtype": "float32"}, {"name": "739", "dtype": "float32"}, {"name": "740", "dtype": "float32"}, {"name": "741", "dtype": "float32"}, {"name": "742", "dtype": "float32"}, {"name": "743", "dtype": "float32"}, {"name": "744", "dtype": "float32"}, {"name": "745", "dtype": "float32"}, {"name": "746", "dtype": "float32"}, {"name": "747", "dtype": "float32"}, {"name": "748", "dtype": "float32"}, {"name": "749", "dtype": "float32"}, {"name": "750", "dtype": "float32"}, {"name": "751", "dtype": "float32"}, {"name": "752", "dtype": "float32"}, {"name": "753", "dtype": "float32"}, {"name": "754", "dtype": "float32"}, {"name": "755", "dtype": "float32"}, {"name": "756", "dtype": "float32"}, {"name": "757", "dtype": "float32"}, {"name": "758", "dtype": "float32"}, {"name": "759", "dtype": "float32"}, {"name": "760", "dtype": "float32"}, {"name": "761", "dtype": "float32"}, {"name": "762", "dtype": "float32"}, {"name": "763", "dtype": "float32"}, {"name": "764", "dtype": "float32"}, {"name": "765", "dtype": "float32"}, {"name": "766", "dtype": "float32"}, {"name": "767", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 1105920, "num_examples": 360}], "download_size": 1749452, "dataset_size": 1105920}}
|
2023-03-12T14:17:39+00:00
|
691330f8f8f74146fe41106ac58a037594419fe7
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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
[More Information Needed]
|
etahamad/pear
|
[
"region:us"
] |
2023-03-12T14:18:22+00:00
|
{}
|
2023-03-12T15:14:26+00:00
|
ef3a8a55ec4e9801996fded8e8a96a2174f8a71a
|
# Dataset Card for "tehranen2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
k0ntra/tehranen2
|
[
"region:us"
] |
2023-03-12T14:23:15+00:00
|
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"float32"}, {"name": "731", "dtype": "float32"}, {"name": "732", "dtype": "float32"}, {"name": "733", "dtype": "float32"}, {"name": "734", "dtype": "float32"}, {"name": "735", "dtype": "float32"}, {"name": "736", "dtype": "float32"}, {"name": "737", "dtype": "float32"}, {"name": "738", "dtype": "float32"}, {"name": "739", "dtype": "float32"}, {"name": "740", "dtype": "float32"}, {"name": "741", "dtype": "float32"}, {"name": "742", "dtype": "float32"}, {"name": "743", "dtype": "float32"}, {"name": "744", "dtype": "float32"}, {"name": "745", "dtype": "float32"}, {"name": "746", "dtype": "float32"}, {"name": "747", "dtype": "float32"}, {"name": "748", "dtype": "float32"}, {"name": "749", "dtype": "float32"}, {"name": "750", "dtype": "float32"}, {"name": "751", "dtype": "float32"}, {"name": "752", "dtype": "float32"}, {"name": "753", "dtype": "float32"}, {"name": "754", "dtype": "float32"}, {"name": "755", "dtype": "float32"}, {"name": "756", "dtype": "float32"}, {"name": "757", "dtype": "float32"}, {"name": "758", "dtype": "float32"}, {"name": "759", "dtype": "float32"}, {"name": "760", "dtype": "float32"}, {"name": "761", "dtype": "float32"}, {"name": "762", "dtype": "float32"}, {"name": "763", "dtype": "float32"}, {"name": "764", "dtype": "float32"}, {"name": "765", "dtype": "float32"}, {"name": "766", "dtype": "float32"}, {"name": "767", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 294912, "num_examples": 96}], "download_size": 673328, "dataset_size": 294912}}
|
2023-03-12T14:27:09+00:00
|
245440e0cf184af81ebe6a14ecfe6208cf9f004b
|
# Dataset Card for "tehranen3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
k0ntra/tehranen3
|
[
"region:us"
] |
2023-03-12T14:27:17+00:00
|
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"float32"}, {"name": "434", "dtype": "float32"}, {"name": "435", "dtype": "float32"}, {"name": "436", "dtype": "float32"}, {"name": "437", "dtype": "float32"}, {"name": "438", "dtype": "float32"}, {"name": "439", "dtype": "float32"}, {"name": "440", "dtype": "float32"}, {"name": "441", "dtype": "float32"}, {"name": "442", "dtype": "float32"}, {"name": "443", "dtype": "float32"}, {"name": "444", "dtype": "float32"}, {"name": "445", "dtype": "float32"}, {"name": "446", "dtype": "float32"}, {"name": "447", "dtype": "float32"}, {"name": "448", "dtype": "float32"}, {"name": "449", "dtype": "float32"}, {"name": "450", "dtype": "float32"}, {"name": "451", "dtype": "float32"}, {"name": "452", "dtype": "float32"}, {"name": "453", "dtype": "float32"}, {"name": "454", "dtype": "float32"}, {"name": "455", "dtype": "float32"}, {"name": "456", "dtype": "float32"}, {"name": "457", "dtype": "float32"}, {"name": "458", "dtype": "float32"}, {"name": "459", "dtype": "float32"}, {"name": "460", "dtype": "float32"}, {"name": "461", "dtype": "float32"}, {"name": "462", "dtype": "float32"}, {"name": "463", "dtype": "float32"}, {"name": "464", "dtype": "float32"}, {"name": "465", "dtype": "float32"}, {"name": "466", "dtype": "float32"}, {"name": "467", "dtype": "float32"}, {"name": "468", "dtype": "float32"}, {"name": "469", "dtype": "float32"}, {"name": "470", "dtype": "float32"}, {"name": "471", "dtype": "float32"}, {"name": "472", "dtype": "float32"}, {"name": "473", "dtype": "float32"}, {"name": "474", "dtype": "float32"}, {"name": "475", "dtype": "float32"}, {"name": "476", "dtype": "float32"}, {"name": "477", "dtype": "float32"}, {"name": "478", "dtype": "float32"}, {"name": "479", "dtype": "float32"}, {"name": "480", "dtype": "float32"}, {"name": "481", "dtype": "float32"}, {"name": "482", "dtype": "float32"}, {"name": "483", "dtype": "float32"}, {"name": "484", "dtype": "float32"}, {"name": "485", "dtype": "float32"}, {"name": "486", "dtype": "float32"}, {"name": "487", "dtype": "float32"}, {"name": "488", "dtype": "float32"}, {"name": "489", "dtype": "float32"}, {"name": "490", "dtype": "float32"}, {"name": "491", "dtype": "float32"}, {"name": "492", "dtype": "float32"}, {"name": "493", "dtype": "float32"}, {"name": "494", "dtype": "float32"}, {"name": "495", "dtype": "float32"}, {"name": "496", "dtype": "float32"}, {"name": "497", "dtype": "float32"}, {"name": "498", "dtype": "float32"}, {"name": "499", "dtype": "float32"}, {"name": "500", "dtype": "float32"}, {"name": "501", "dtype": "float32"}, {"name": "502", "dtype": "float32"}, {"name": "503", "dtype": "float32"}, {"name": "504", "dtype": "float32"}, {"name": "505", "dtype": "float32"}, {"name": "506", "dtype": "float32"}, {"name": "507", "dtype": "float32"}, {"name": "508", "dtype": "float32"}, {"name": "509", "dtype": "float32"}, {"name": "510", "dtype": "float32"}, {"name": "511", "dtype": "float32"}, {"name": "512", "dtype": "float32"}, {"name": "513", "dtype": "float32"}, {"name": "514", "dtype": "float32"}, {"name": "515", "dtype": "float32"}, {"name": "516", "dtype": "float32"}, {"name": "517", "dtype": "float32"}, {"name": "518", "dtype": "float32"}, {"name": "519", "dtype": "float32"}, {"name": "520", "dtype": "float32"}, {"name": "521", "dtype": "float32"}, {"name": "522", "dtype": "float32"}, {"name": "523", "dtype": "float32"}, {"name": "524", "dtype": "float32"}, {"name": "525", "dtype": "float32"}, {"name": "526", "dtype": "float32"}, {"name": "527", "dtype": "float32"}, {"name": "528", "dtype": "float32"}, {"name": "529", "dtype": "float32"}, {"name": "530", "dtype": "float32"}, {"name": "531", "dtype": "float32"}, {"name": "532", "dtype": "float32"}, {"name": "533", "dtype": "float32"}, {"name": "534", "dtype": "float32"}, {"name": "535", "dtype": "float32"}, {"name": "536", "dtype": "float32"}, {"name": "537", "dtype": "float32"}, {"name": "538", "dtype": "float32"}, {"name": "539", "dtype": "float32"}, {"name": "540", "dtype": "float32"}, {"name": "541", "dtype": "float32"}, {"name": "542", "dtype": "float32"}, {"name": "543", "dtype": "float32"}, {"name": "544", "dtype": "float32"}, {"name": "545", "dtype": "float32"}, {"name": "546", "dtype": "float32"}, {"name": "547", "dtype": "float32"}, {"name": "548", "dtype": "float32"}, {"name": "549", "dtype": "float32"}, {"name": "550", "dtype": "float32"}, {"name": "551", "dtype": "float32"}, {"name": "552", "dtype": "float32"}, {"name": "553", "dtype": "float32"}, {"name": "554", "dtype": "float32"}, {"name": "555", "dtype": "float32"}, {"name": "556", "dtype": "float32"}, {"name": "557", "dtype": "float32"}, {"name": "558", "dtype": "float32"}, {"name": "559", "dtype": "float32"}, {"name": "560", "dtype": "float32"}, {"name": "561", "dtype": "float32"}, {"name": "562", "dtype": "float32"}, {"name": "563", "dtype": "float32"}, {"name": "564", "dtype": "float32"}, {"name": "565", "dtype": "float32"}, {"name": "566", "dtype": "float32"}, {"name": "567", "dtype": "float32"}, {"name": "568", "dtype": "float32"}, {"name": "569", "dtype": "float32"}, {"name": "570", "dtype": "float32"}, {"name": "571", "dtype": "float32"}, {"name": "572", "dtype": "float32"}, {"name": "573", "dtype": "float32"}, {"name": "574", "dtype": "float32"}, {"name": "575", "dtype": "float32"}, {"name": "576", "dtype": "float32"}, {"name": "577", "dtype": "float32"}, {"name": "578", "dtype": "float32"}, {"name": "579", "dtype": "float32"}, {"name": "580", "dtype": "float32"}, {"name": "581", "dtype": "float32"}, {"name": "582", "dtype": "float32"}, {"name": "583", "dtype": "float32"}, {"name": "584", "dtype": "float32"}, {"name": "585", "dtype": "float32"}, {"name": "586", "dtype": "float32"}, {"name": "587", "dtype": "float32"}, {"name": "588", "dtype": "float32"}, {"name": "589", "dtype": "float32"}, {"name": "590", "dtype": "float32"}, {"name": "591", "dtype": "float32"}, {"name": "592", "dtype": "float32"}, {"name": "593", "dtype": "float32"}, {"name": "594", "dtype": "float32"}, {"name": "595", "dtype": "float32"}, {"name": "596", "dtype": "float32"}, {"name": "597", "dtype": "float32"}, {"name": "598", "dtype": "float32"}, {"name": "599", "dtype": "float32"}, {"name": "600", "dtype": "float32"}, {"name": "601", "dtype": "float32"}, {"name": "602", "dtype": "float32"}, {"name": "603", "dtype": "float32"}, {"name": "604", "dtype": "float32"}, {"name": "605", "dtype": "float32"}, {"name": "606", "dtype": "float32"}, {"name": "607", "dtype": "float32"}, {"name": "608", "dtype": "float32"}, {"name": "609", "dtype": "float32"}, {"name": "610", "dtype": "float32"}, {"name": "611", "dtype": "float32"}, {"name": "612", "dtype": "float32"}, {"name": "613", "dtype": "float32"}, {"name": "614", "dtype": "float32"}, {"name": "615", "dtype": "float32"}, {"name": "616", "dtype": "float32"}, {"name": "617", "dtype": "float32"}, {"name": "618", "dtype": "float32"}, {"name": "619", "dtype": "float32"}, {"name": "620", "dtype": "float32"}, {"name": "621", "dtype": "float32"}, {"name": "622", "dtype": "float32"}, {"name": "623", "dtype": "float32"}, {"name": "624", "dtype": "float32"}, {"name": "625", "dtype": "float32"}, {"name": "626", "dtype": "float32"}, {"name": "627", "dtype": "float32"}, {"name": "628", "dtype": "float32"}, {"name": "629", "dtype": "float32"}, {"name": "630", "dtype": "float32"}, {"name": "631", "dtype": "float32"}, {"name": "632", "dtype": "float32"}, {"name": "633", "dtype": "float32"}, {"name": "634", "dtype": "float32"}, {"name": "635", "dtype": "float32"}, {"name": "636", "dtype": "float32"}, {"name": "637", "dtype": "float32"}, {"name": "638", "dtype": "float32"}, {"name": "639", "dtype": "float32"}, {"name": "640", "dtype": "float32"}, {"name": "641", "dtype": "float32"}, {"name": "642", "dtype": "float32"}, {"name": "643", "dtype": "float32"}, {"name": "644", "dtype": "float32"}, {"name": "645", "dtype": "float32"}, {"name": "646", "dtype": "float32"}, {"name": "647", "dtype": "float32"}, {"name": "648", "dtype": "float32"}, {"name": "649", "dtype": "float32"}, {"name": "650", "dtype": "float32"}, {"name": "651", "dtype": "float32"}, {"name": "652", "dtype": "float32"}, {"name": "653", "dtype": "float32"}, {"name": "654", "dtype": "float32"}, {"name": "655", "dtype": "float32"}, {"name": "656", "dtype": "float32"}, {"name": "657", "dtype": "float32"}, {"name": "658", "dtype": "float32"}, {"name": "659", "dtype": "float32"}, {"name": "660", "dtype": "float32"}, {"name": "661", "dtype": "float32"}, {"name": "662", "dtype": "float32"}, {"name": "663", "dtype": "float32"}, {"name": "664", "dtype": "float32"}, {"name": "665", "dtype": "float32"}, {"name": "666", "dtype": "float32"}, {"name": "667", "dtype": "float32"}, {"name": "668", "dtype": "float32"}, {"name": "669", "dtype": "float32"}, {"name": "670", "dtype": "float32"}, {"name": "671", "dtype": "float32"}, {"name": "672", "dtype": "float32"}, {"name": "673", "dtype": "float32"}, {"name": "674", "dtype": "float32"}, {"name": "675", "dtype": "float32"}, {"name": "676", "dtype": "float32"}, {"name": "677", "dtype": "float32"}, {"name": "678", "dtype": "float32"}, {"name": "679", "dtype": "float32"}, {"name": "680", "dtype": "float32"}, {"name": "681", "dtype": "float32"}, {"name": "682", "dtype": "float32"}, {"name": "683", "dtype": "float32"}, {"name": "684", "dtype": "float32"}, {"name": "685", "dtype": "float32"}, {"name": "686", "dtype": "float32"}, {"name": "687", "dtype": "float32"}, {"name": "688", "dtype": "float32"}, {"name": "689", "dtype": "float32"}, {"name": "690", "dtype": "float32"}, {"name": "691", "dtype": "float32"}, {"name": "692", "dtype": "float32"}, {"name": "693", "dtype": "float32"}, {"name": "694", "dtype": "float32"}, {"name": "695", "dtype": "float32"}, {"name": "696", "dtype": "float32"}, {"name": "697", "dtype": "float32"}, {"name": "698", "dtype": "float32"}, {"name": "699", "dtype": "float32"}, {"name": "700", "dtype": "float32"}, {"name": "701", "dtype": "float32"}, {"name": "702", "dtype": "float32"}, {"name": "703", "dtype": "float32"}, {"name": "704", "dtype": "float32"}, {"name": "705", "dtype": "float32"}, {"name": "706", "dtype": "float32"}, {"name": "707", "dtype": "float32"}, {"name": "708", "dtype": "float32"}, {"name": "709", "dtype": "float32"}, {"name": "710", "dtype": "float32"}, {"name": "711", "dtype": "float32"}, {"name": "712", "dtype": "float32"}, {"name": "713", "dtype": "float32"}, {"name": "714", "dtype": "float32"}, {"name": "715", "dtype": "float32"}, {"name": "716", "dtype": "float32"}, {"name": "717", "dtype": "float32"}, {"name": "718", "dtype": "float32"}, {"name": "719", "dtype": "float32"}, {"name": "720", "dtype": "float32"}, {"name": "721", "dtype": "float32"}, {"name": "722", "dtype": "float32"}, {"name": "723", "dtype": "float32"}, {"name": "724", "dtype": "float32"}, {"name": "725", "dtype": "float32"}, {"name": "726", "dtype": "float32"}, {"name": "727", "dtype": "float32"}, {"name": "728", "dtype": "float32"}, {"name": "729", "dtype": "float32"}, {"name": "730", "dtype": "float32"}, {"name": "731", "dtype": "float32"}, {"name": "732", "dtype": "float32"}, {"name": "733", "dtype": "float32"}, {"name": "734", "dtype": "float32"}, {"name": "735", "dtype": "float32"}, {"name": "736", "dtype": "float32"}, {"name": "737", "dtype": "float32"}, {"name": "738", "dtype": "float32"}, {"name": "739", "dtype": "float32"}, {"name": "740", "dtype": "float32"}, {"name": "741", "dtype": "float32"}, {"name": "742", "dtype": "float32"}, {"name": "743", "dtype": "float32"}, {"name": "744", "dtype": "float32"}, {"name": "745", "dtype": "float32"}, {"name": "746", "dtype": "float32"}, {"name": "747", "dtype": "float32"}, {"name": "748", "dtype": "float32"}, {"name": "749", "dtype": "float32"}, {"name": "750", "dtype": "float32"}, {"name": "751", "dtype": "float32"}, {"name": "752", "dtype": "float32"}, {"name": "753", "dtype": "float32"}, {"name": "754", "dtype": "float32"}, {"name": "755", "dtype": "float32"}, {"name": "756", "dtype": "float32"}, {"name": "757", "dtype": "float32"}, {"name": "758", "dtype": "float32"}, {"name": "759", "dtype": "float32"}, {"name": "760", "dtype": "float32"}, {"name": "761", "dtype": "float32"}, {"name": "762", "dtype": "float32"}, {"name": "763", "dtype": "float32"}, {"name": "764", "dtype": "float32"}, {"name": "765", "dtype": "float32"}, {"name": "766", "dtype": "float32"}, {"name": "767", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 190464, "num_examples": 62}], "download_size": 549812, "dataset_size": 190464}}
|
2023-03-12T14:27:22+00:00
|
1d4a8be2c4f8c698755e8562e93c2d4320bb2458
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@wandb.init(project=PROJECT](https://huggingface.co/wandb.init(project=PROJECT) for evaluating this model.
|
autoevaluate/autoeval-eval-acronym_identification-default-641c5d-40516105295
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-03-12T14:41:27+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["acronym_identification"], "eval_info": {"task": "entity_extraction", "model": "lewtun/autotrain-acronym-identification-7324788", "metrics": ["angelina-wang/directional_bias_amplification"], "dataset_name": "acronym_identification", "dataset_config": "default", "dataset_split": "validation", "col_mapping": {"tokens": "tokens", "tags": "labels"}}}
|
2023-03-12T14:42:08+00:00
|
e5349a6101127ff68e1fc477e1c8c12e50bf8453
|
gjuggler/extra-birds
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-03-12T14:49:20+00:00
|
{"license": "creativeml-openrail-m"}
|
2023-03-12T15:06:37+00:00
|
|
8ba333bf0fc956f52455b297c0d891c27d1d63f3
|
foldl/MathSet
|
[
"region:us"
] |
2023-03-12T14:52:46+00:00
|
{}
|
2023-03-12T15:50:21+00:00
|
|
05c19c0c7aa6ba84b002909a580dcef2fa4cd1cf
|
NBayer/20_observations_test
|
[
"license:openrail",
"region:us"
] |
2023-03-12T14:53:32+00:00
|
{"license": "openrail"}
|
2023-03-12T14:54:09+00:00
|
|
e5268f56961967421ad78e7e0695fa07b30ffa99
|
# Dataset Card for "processed_bio_bert_tiny_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yashpatil/processed_bio_bert_tiny_dataset
|
[
"region:us"
] |
2023-03-12T14:55:45+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 21940534800.0, "num_examples": 6094593}], "download_size": 6445245849, "dataset_size": 21940534800.0}}
|
2023-03-12T15:08:18+00:00
|
88dcab56f33deb416344623b0628eca4d7d99095
|
- This Dataset has been downloaded from PubMed
- It has abstracts and titles that are related to Psychedelics
- the data has been cleaned before uploading
- it could be used for any NLP task, such as Domain Adaptation
|
Gaborandi/Psychedelics_pubmed_abstracts
|
[
"region:us"
] |
2023-03-12T15:02:47+00:00
|
{}
|
2023-03-12T15:05:31+00:00
|
ff5496f05d0c26b418f761b66f2414d62b945a4f
|
# Dataset Card for "face_synthetics_smol"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pcuenq/face_synthetics_smol
|
[
"region:us"
] |
2023-03-12T15:22:11+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_seg", "dtype": "image"}, {"name": "landmarks", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35303202.0, "num_examples": 100}], "download_size": 35283640, "dataset_size": 35303202.0}}
|
2023-03-12T15:24:35+00:00
|
75beba2e8b0fce1f42ddb132efceaf299c2531b3
|
kurianbenoy/malayalam_common_voice_benchmarking
|
[
"license:mit",
"region:us"
] |
2023-03-12T15:28:25+00:00
|
{"license": "mit"}
|
2023-09-24T17:09:39+00:00
|
|
15b9d3d33a38cfce358ff9831fa7d78cacfc5e3f
|
# Dataset Card for "product-test-01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tuperte69/product-test-01
|
[
"region:us"
] |
2023-03-12T15:33:12+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 44144249.0, "num_examples": 29}], "download_size": 41857792, "dataset_size": 44144249.0}}
|
2023-03-12T15:35:08+00:00
|
7468ccc61423c84df5714feb540fc81803ceaa6a
|
kurianbenoy/malayalam_msc_benchmarking
|
[
"license:mit",
"region:us"
] |
2023-03-12T15:39:53+00:00
|
{"license": "mit"}
|
2023-09-24T17:27:31+00:00
|
|
81c348b93ba0859468bfbaa0c56ad11b2e2da334
|
# Dataset Card for "shirazfa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
k0ntra/shirazfa
|
[
"region:us"
] |
2023-03-12T15:53:06+00:00
|
{"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": "26", "dtype": "float32"}, 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"float32"}, {"name": "326", "dtype": "float32"}, {"name": "327", "dtype": "float32"}, {"name": "328", "dtype": "float32"}, {"name": "329", "dtype": "float32"}, {"name": "330", "dtype": "float32"}, {"name": "331", "dtype": "float32"}, {"name": "332", "dtype": "float32"}, {"name": "333", "dtype": "float32"}, {"name": "334", "dtype": "float32"}, {"name": "335", "dtype": "float32"}, {"name": "336", "dtype": "float32"}, {"name": "337", "dtype": "float32"}, {"name": "338", "dtype": "float32"}, {"name": "339", "dtype": "float32"}, {"name": "340", "dtype": "float32"}, {"name": "341", "dtype": "float32"}, {"name": "342", "dtype": "float32"}, {"name": "343", "dtype": "float32"}, {"name": "344", "dtype": "float32"}, {"name": "345", "dtype": "float32"}, {"name": "346", "dtype": "float32"}, {"name": "347", "dtype": "float32"}, {"name": "348", "dtype": "float32"}, {"name": "349", "dtype": "float32"}, {"name": "350", "dtype": "float32"}, {"name": "351", "dtype": "float32"}, {"name": "352", "dtype": "float32"}, {"name": "353", "dtype": "float32"}, {"name": "354", "dtype": "float32"}, {"name": "355", "dtype": "float32"}, {"name": "356", "dtype": "float32"}, {"name": "357", "dtype": "float32"}, {"name": "358", "dtype": "float32"}, {"name": "359", "dtype": "float32"}, {"name": "360", "dtype": "float32"}, {"name": "361", "dtype": "float32"}, {"name": "362", "dtype": "float32"}, {"name": "363", "dtype": "float32"}, {"name": "364", "dtype": "float32"}, {"name": "365", "dtype": "float32"}, {"name": "366", "dtype": "float32"}, {"name": "367", "dtype": "float32"}, {"name": "368", "dtype": "float32"}, {"name": "369", "dtype": "float32"}, {"name": "370", "dtype": "float32"}, {"name": "371", "dtype": "float32"}, {"name": "372", "dtype": "float32"}, {"name": "373", "dtype": "float32"}, {"name": "374", "dtype": "float32"}, {"name": "375", "dtype": "float32"}, {"name": "376", "dtype": "float32"}, {"name": "377", "dtype": "float32"}, {"name": "378", "dtype": "float32"}, {"name": "379", "dtype": "float32"}, {"name": "380", "dtype": "float32"}, {"name": "381", "dtype": "float32"}, {"name": "382", "dtype": "float32"}, {"name": "383", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 433152, "num_examples": 282}], "download_size": 721412, "dataset_size": 433152}}
|
2023-03-12T15:53:16+00:00
|
59b47f7cbf463ac77d85870f1a7d4df66e526986
|
# Dataset Card for "adv-int"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
violetamaral/adv-int
|
[
"region:us"
] |
2023-03-12T16:30:25+00:00
|
{"dataset_info": {"features": [{"name": "ADV", "dtype": "string"}, {"name": "INT", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 514838.63927576604, "num_examples": 1723}, {"name": "test", "num_bytes": 128784.36072423398, "num_examples": 431}], "download_size": 456341, "dataset_size": 643623.0}}
|
2023-03-12T16:30:40+00:00
|
154ec7a4980782f8553a36c35cbc679fdfe4d67b
|
# Dataset Card for "shirazfa2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
k0ntra/shirazfa2
|
[
"region:us"
] |
2023-03-12T16:32:41+00:00
|
{"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": "26", "dtype": "float32"}, 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"float32"}, {"name": "55", "dtype": "float32"}, {"name": "56", "dtype": "float32"}, {"name": "57", "dtype": "float32"}, {"name": "58", "dtype": "float32"}, {"name": "59", "dtype": "float32"}, {"name": "60", "dtype": "float32"}, {"name": "61", "dtype": "float32"}, {"name": "62", "dtype": "float32"}, {"name": "63", "dtype": "float32"}, {"name": "64", "dtype": "float32"}, {"name": "65", "dtype": "float32"}, {"name": "66", "dtype": "float32"}, {"name": "67", "dtype": "float32"}, {"name": "68", "dtype": "float32"}, {"name": "69", "dtype": "float32"}, {"name": "70", "dtype": "float32"}, {"name": "71", "dtype": "float32"}, {"name": "72", "dtype": "float32"}, {"name": "73", "dtype": "float32"}, {"name": "74", "dtype": "float32"}, {"name": "75", "dtype": "float32"}, {"name": "76", "dtype": "float32"}, {"name": "77", "dtype": "float32"}, {"name": "78", "dtype": "float32"}, {"name": "79", "dtype": "float32"}, {"name": "80", "dtype": "float32"}, {"name": "81", "dtype": "float32"}, {"name": "82", 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"float32"}, {"name": "380", "dtype": "float32"}, {"name": "381", "dtype": "float32"}, {"name": "382", "dtype": "float32"}, {"name": "383", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 147456, "num_examples": 96}], "download_size": 324302, "dataset_size": 147456}}
|
2023-03-12T16:32:51+00:00
|
1bcd3173d8410c5dc5eb1fcf31378f8fe3a4bedd
|
# Dataset Card for "shirazfa3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
k0ntra/shirazfa3
|
[
"region:us"
] |
2023-03-12T17:00:32+00:00
|
{"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": "26", "dtype": "float32"}, 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"float32"}, {"name": "245", "dtype": "float32"}, {"name": "246", "dtype": "float32"}, {"name": "247", "dtype": "float32"}, {"name": "248", "dtype": "float32"}, {"name": "249", "dtype": "float32"}, {"name": "250", "dtype": "float32"}, {"name": "251", "dtype": "float32"}, {"name": "252", "dtype": "float32"}, {"name": "253", "dtype": "float32"}, {"name": "254", "dtype": "float32"}, {"name": "255", "dtype": "float32"}, {"name": "256", "dtype": "float32"}, {"name": "257", "dtype": "float32"}, {"name": "258", "dtype": "float32"}, {"name": "259", "dtype": "float32"}, {"name": "260", "dtype": "float32"}, {"name": "261", "dtype": "float32"}, {"name": "262", "dtype": "float32"}, {"name": "263", "dtype": "float32"}, {"name": "264", "dtype": "float32"}, {"name": "265", "dtype": "float32"}, {"name": "266", "dtype": "float32"}, {"name": "267", "dtype": "float32"}, {"name": "268", "dtype": "float32"}, {"name": "269", "dtype": "float32"}, {"name": "270", "dtype": "float32"}, {"name": "271", "dtype": 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"float32"}, {"name": "299", "dtype": "float32"}, {"name": "300", "dtype": "float32"}, {"name": "301", "dtype": "float32"}, {"name": "302", "dtype": "float32"}, {"name": "303", "dtype": "float32"}, {"name": "304", "dtype": "float32"}, {"name": "305", "dtype": "float32"}, {"name": "306", "dtype": "float32"}, {"name": "307", "dtype": "float32"}, {"name": "308", "dtype": "float32"}, {"name": "309", "dtype": "float32"}, {"name": "310", "dtype": "float32"}, {"name": "311", "dtype": "float32"}, {"name": "312", "dtype": "float32"}, {"name": "313", "dtype": "float32"}, {"name": "314", "dtype": "float32"}, {"name": "315", "dtype": "float32"}, {"name": "316", "dtype": "float32"}, {"name": "317", "dtype": "float32"}, {"name": "318", "dtype": "float32"}, {"name": "319", "dtype": "float32"}, {"name": "320", "dtype": "float32"}, {"name": "321", "dtype": "float32"}, {"name": "322", "dtype": "float32"}, {"name": "323", "dtype": "float32"}, {"name": "324", "dtype": "float32"}, {"name": "325", "dtype": "float32"}, {"name": "326", "dtype": "float32"}, {"name": "327", "dtype": "float32"}, {"name": "328", "dtype": "float32"}, {"name": "329", "dtype": "float32"}, {"name": "330", "dtype": "float32"}, {"name": "331", "dtype": "float32"}, {"name": "332", "dtype": "float32"}, {"name": "333", "dtype": "float32"}, {"name": "334", "dtype": "float32"}, {"name": "335", "dtype": "float32"}, {"name": "336", "dtype": "float32"}, {"name": "337", "dtype": "float32"}, {"name": "338", "dtype": "float32"}, {"name": "339", "dtype": "float32"}, {"name": "340", "dtype": "float32"}, {"name": "341", "dtype": "float32"}, {"name": "342", "dtype": "float32"}, {"name": "343", "dtype": "float32"}, {"name": "344", "dtype": "float32"}, {"name": "345", "dtype": "float32"}, {"name": "346", "dtype": "float32"}, {"name": "347", "dtype": "float32"}, {"name": "348", "dtype": "float32"}, {"name": "349", "dtype": "float32"}, {"name": "350", "dtype": "float32"}, {"name": "351", "dtype": "float32"}, {"name": "352", "dtype": "float32"}, {"name": "353", "dtype": "float32"}, {"name": "354", "dtype": "float32"}, {"name": "355", "dtype": "float32"}, {"name": "356", "dtype": "float32"}, {"name": "357", "dtype": "float32"}, {"name": "358", "dtype": "float32"}, {"name": "359", "dtype": "float32"}, {"name": "360", "dtype": "float32"}, {"name": "361", "dtype": "float32"}, {"name": "362", "dtype": "float32"}, {"name": "363", "dtype": "float32"}, {"name": "364", "dtype": "float32"}, {"name": "365", "dtype": "float32"}, {"name": "366", "dtype": "float32"}, {"name": "367", "dtype": "float32"}, {"name": "368", "dtype": "float32"}, {"name": "369", "dtype": "float32"}, {"name": "370", "dtype": "float32"}, {"name": "371", "dtype": "float32"}, {"name": "372", "dtype": "float32"}, {"name": "373", "dtype": "float32"}, {"name": "374", "dtype": "float32"}, {"name": "375", "dtype": "float32"}, {"name": "376", "dtype": "float32"}, {"name": "377", "dtype": "float32"}, {"name": "378", "dtype": "float32"}, {"name": "379", "dtype": "float32"}, {"name": "380", "dtype": "float32"}, {"name": "381", "dtype": "float32"}, {"name": "382", "dtype": "float32"}, {"name": "383", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 113664, "num_examples": 74}], "download_size": 305096, "dataset_size": 113664}}
|
2023-03-12T17:00:42+00:00
|
acb91c16f730ac6fecbc18bac705cb73d3efedab
|
<p align="center"><h1>🧠 Awesome Chaττensor Prompts [CSV dataset]</h1></p>
This is a Dataset Repository of **Awesome Chattensor Prompts**
**[View All Prompts on GitHub](https://github.com/neuralinternet/awesome-chattensor-prompts)**
# License
CC-0
|
NeuralInternet/awesome-chattensor-prompts
|
[
"license:cc0-1.0",
"ChatGPT",
"Chattensor",
"region:us"
] |
2023-03-12T17:16:33+00:00
|
{"license": "cc0-1.0", "tags": ["ChatGPT", "Chattensor"]}
|
2023-03-12T20:06:01+00:00
|
324fe81bfb5230a9f10ac5b549b4f52541c3b448
|
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForQuestionAnswering.from_pretrained('roberta-base')
import torch
from transformers import RobertaTokenizer, RobertaForQuestionAnswering
from sklearn.model_selection import train_test_split
import pandas as pd
# Load the pre-trained RoBERTa tokenizer and model
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForQuestionAnswering.from_pretrained('roberta-base')
# Define the preprocessed data as a list of tuples, where the first element is the question and the second element is the answer
data = [("What types of products do you sell?", "We offer a wide range of cannabis products, including dried flowers, pre-rolls, oils, capsules, and accessories."),
("How do I place an order?", "You can place an order on our website or by calling our customer service line."),
("How long will it take to receive my order?", "Orders are typically processed and shipped within 1-2 business days. Shipping times vary depending on your location and the shipping method you choose."),
("What payment methods do you accept?", "We accept Visa, Mastercard, and American Express credit cards, as well as Visa Debit and Mastercard Debit cards. You can also pay with an electronic funds transfer (EFT) from your bank account."),
("Do you offer free shipping?", "We offer free shipping on orders over $99 (before taxes and shipping fees) to most locations in Canada."),
("Can I track my order?", "Yes, you will receive a confirmation email with a tracking number once your order has shipped. You can use the tracking number to check the status of your order on the carrier's website."),
("What are your store hours?", "Our stores are open Monday to Sunday from 10:00am to 9:00pm."),
("Where are your store locations?", "We have several retail locations across New Brunswick. You can find a list of our stores and their addresses on our website."),
("What is your return policy?", "We do not accept returns on cannabis products due to health and safety regulations. However, if you receive a damaged or defective product, please contact our customer service team for assistance."),
("What are the legal regulations for buying and using cannabis in New Brunswick?", "In New Brunswick, you must be 19 years or older to purchase and use cannabis. It is illegal to drive under the influence of cannabis, and you can face fines and penalties for doing so. You can possess up to 30 grams of dried cannabis or the equivalent in public, and up to 150 grams in your home. It is also illegal to buy or sell cannabis from anyone other than an authorized retailer. For more information, please visit the Government of New Brunswick's website.")]
# Convert the data into a pandas dataframe
df = pd.DataFrame(data, columns=["Question", "Answer"])
# Split the data into training, validation, and testing datasets
train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)
train_data, val_data = train_test_split(train_data, test_size=0.2, random_state=42)
# Tokenize the input sequences and convert them to tensors
train_input_ids = tokenizer.batch_encode_plus(train_data.Question.tolist(), padding=True, truncation=True, max_length=512, return_tensors="pt")
val_input_ids = tokenizer.batch_encode_plus(val_data.Question.tolist(), padding=True, truncation=True, max_length=512, return_tensors="pt")
test_input_ids = tokenizer.batch_encode_plus(test_data.Question.tolist
|
KVN-AI/CannaNBot
|
[
"region:us"
] |
2023-03-12T17:25:11+00:00
|
{}
|
2023-03-12T18:53:32+00:00
|
eb58b6bb4457bd7b185edcd5a4286291b3f64e68
|
# Dataset Card for Common Voice Corpus 12.0
## 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 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:** [Vaibhav Srivastav](mailto:[email protected])
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 26119 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 17127 validated hours in 104 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
[🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=mozilla-foundation%2Fcommon_voice_11_0&only_verified=0&task=automatic-speech-recognition&config=ar&split=test&metric=wer)
### Languages
```
Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
```
## How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi):
```python
from datasets import load_dataset
cv_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
cv_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", split="train", streaming=True)
print(next(iter(cv_12)))
```
*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
### Local
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
cv_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_12), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_12, batch_sampler=batch_sampler)
```
### Streaming
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", split="train")
dataloader = DataLoader(cv_12, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 12 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
## 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_12_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
}
```
|
mozilla-foundation/common_voice_12_0
|
[
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:extended|common_voice",
"language:ab",
"language:ar",
"language:as",
"language:ast",
"language:az",
"language:ba",
"language:bas",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:ckb",
"language:cnh",
"language:cs",
"language:cv",
"language:cy",
"language:da",
"language:de",
"language:dv",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:gl",
"language:gn",
"language:ha",
"language:hi",
"language:hsb",
"language:hu",
"language:ia",
"language:id",
"language:ig",
"language:it",
"language:ja",
"language:ka",
"language:kab",
"language:kk",
"language:kmr",
"language:ko",
"language:ky",
"language:lg",
"language:lt",
"language:lv",
"language:mdf",
"language:mhr",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:mrj",
"language:mt",
"language:myv",
"language:nl",
"language:oc",
"language:or",
"language:pl",
"language:pt",
"language:quy",
"language:ro",
"language:ru",
"language:rw",
"language:sah",
"language:sat",
"language:sc",
"language:sk",
"language:skr",
"language:sl",
"language:sr",
"language:sw",
"language:ta",
"language:th",
"language:ti",
"language:tig",
"language:tok",
"language:tr",
"language:tt",
"language:tw",
"language:ug",
"language:uk",
"language:ur",
"language:uz",
"language:vi",
"language:vot",
"language:yo",
"language:yue",
"language:rm",
"language:zh",
"language:sv",
"language:pa",
"language:nn",
"language:ne",
"language:nan",
"language:hy",
"language:ga",
"language:fy",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] |
2023-03-12T17:28:02+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ab", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "gl", "gn", "ha", "hi", "hsb", "hu", "ia", "id", "ig", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lt", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nl", "oc", "or", "pl", "pt", "quy", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sr", "sw", "ta", "th", "ti", "tig", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yo", "yue", "rm", "zh", "sv", "pa", "nn", "ne", "nan", "hy", "ga", "fy"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|common_voice"], "task_categories": ["automatic-speech-recognition"], "paperswithcode_id": "common-voice", "pretty_name": "Common Voice Corpus 12.0", "language_bcp47": ["fy-NL", "ga-IE", "hy-AM", "nan-tw", "ne-NP", "nn-NO", "pa-IN", "rm-sursilv", "rm-vallader", "sv-SE", "zh-CN", "zh-HK", "zh-TW"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."}
|
2023-11-17T18:09:06+00:00
|
ff31fc02ed7f478419f520b830a0799a99e912c9
|
# Dataset Card for PorSimplesSent
## Dataset Description
- **Repository:** [sidleal/porsimplessent](https://github.com/sidleal/porsimplessent)
- **Paper:** [A Nontrivial Sentence Corpus for the Task of Sentence Readability Assessment in Portuguese](https://aclanthology.org/C18-1034/)
- **Point of Contact:** [Sidney Evaldo Leal]([email protected])
### Dataset Summary
PorSimplesSent is a Portuguese corpus of aligned sentence pairs and triplets created for the purpose of investigating sentence readability
assessment in Portuguese. The dataset consists of 4,968 pairs and 1,141 triplets of sentences, combining the three levels of the PorSimples
corpus: Original, Natural, and Strong. The dataset can be used for tasks such as sentence-pair classification, sentence retrieval, and readability assessment.
### Supported Tasks and Leaderboards
The dataset supports the following tasks:
- `sentence-pair-classification`: The dataset can be used to train a model for sentence-pair classification, which consists in determining whether one sentence is simpler than the other or if both sentences are equally simple. Success on this task is typically measured by achieving a high accuracy, f1, precision, and recall.
### Languages
The dataset consists of sentence pairs in Portuguese.
## Dataset Structure
### Data Instances
```json
{
'sentence1': '-- Parece que o assassinato de civis iraquianos transformou-se em um fenômeno cotidiano e banal -- disse o presidente da Associação Iraquiana dos Direitos Humanos, Muayed al-Anbaki.',
'sentence2': '-- Parece que o assassinato de civis iraquianos transformou-se em um fenômeno comum e banal -- disse o presidente da Associação Iraquiana dos Direitos Humanos, Muayed al-Anbaki.',
'label': 2,
'production_id': 3,
'level': 'ORI->NAT',
'changed': 'S',
'split': 'N',
'sentence_text_from': '-- Parece que o assassinato de civis iraquianos transformou-se em um fenômeno cotidiano e banal -- disse o presidente da Associação Iraquiana dos Direitos Humanos, Muayed al-Anbaki.',
'sentence_text_to': '-- Parece que o assassinato de civis iraquianos transformou-se em um fenômeno comum e banal -- disse o presidente da Associação Iraquiana dos Direitos Humanos, Muayed al-Anbaki.'
}
```
### Data Fields
The dataset has the following fields:
* `sentence1`: the first sentence in the sentence pair (string).
* `sentence2`: the second sentence in the sentence pair (string).
* `label`: an integer indicating the relationship between the two sentences in the pair. The possible values are 0, 1, and 2, where 0 means that sentence1 is more simple than sentence2, 1 means that both sentences have the same level of complexity, and 2 means that sentence2 is more simple than sentence1 (int).
* `production_id`: an integer identifier for each sentence pair (int).
* `level`: a string indicating the level of simplification between the two sentences. The possible values are:
* 'ORI->NAT' (original to natural)
* 'NAT->STR' (natural to strong)
* 'ORI->STR' (original to strong) (string).
* `changed`: a string indicating whether the sentence was changed during the simplification process. The possible values are:
* 'S' (changed)
* 'N' (not changed) (string).
* `split`: a string indicating whether the sentence suffered a split in this simplification level. The possible values are:
* 'S' (split)
* 'N' (not split) (string).
* `sentence_text_from`: the raw text of the source sentence (string).
* `sentence_text_to`: the raw text of the target sentence (string).
### Data Splits
The dataset is split into three subsets: train, validation, and test. The sizes of each split are as follows:
| | Train | Validation | Test |
|--------------------|--------|------------|-------|
| Number of examples | 4,976 | 1,446 | 1,697 |
The authors did not provide standard splits. We created the splits ourselves while ensuring that sentence pairs from the same document did not appear in multiple splits.
## Additional Information
### Dataset Curators
The PorSimplesSent dataset was created by Sidney Evaldo Leal, with guidance from his advisors Dra. Sandra Maria Aluísio and Dra. Magali Sanches Duran, during his master's degree at ICMC-USP. The Interinstitutional Center for Computational Linguistics - NILC (Núcleo Interinstitucional de Linguística Computacional) also contributed to the creation of the dataset.
### Licensing Information
The PorSimplesSent dataset is released under the CC BY 4.0 license. The license terms can be found at https://creativecommons.org/licenses/by/4.0/.
### Citation Information
If you use this dataset in your work, please cite the following publication:\
```bibtex
@inproceedings{leal2018pss,
author = {Sidney Evaldo Leal and Magali Sanches Duran and Sandra Maria Aluíso},
title = {A Nontrivial Sentence Corpus for the Task of Sentence Readability Assessment in Portuguese},
booktitle = {Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018)},
year = {2018},
pages = {401-413},
month = {August},
date = {20-26},
address = {Santa Fe, New Mexico, USA},
}
```
### Contributions
Thanks to [@ruanchaves](https://github.com/ruanchaves) for adding this dataset.
|
ruanchaves/porsimplessent
|
[
"size_categories:1K<n<10K",
"region:us"
] |
2023-03-12T17:45:24+00:00
|
{"size_categories": ["1K<n<10K"]}
|
2023-04-12T14:57:26+00:00
|
15ec5f1bf25d9cee1ae73f2a426882d0c0151f4e
|
# Dataset Card for "silverdata-iwslt-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pvisnrt/silverdata-iwslt-2023
|
[
"region:us"
] |
2023-03-12T18:22:37+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "styles", "dtype": "int64"}], "splits": [{"name": "vi", "num_bytes": 118306.0, "num_examples": 500}, {"name": "ko", "num_bytes": 121983.0, "num_examples": 500}], "download_size": 67669, "dataset_size": 240289.0}}
|
2023-03-12T21:59:47+00:00
|
e479f5b9b38e3dc885a9afe1c084092289615017
|
# Dataset Card for "iwslt-2023-en-vi-train-split-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
shreevigneshs/iwslt-2023-en-vi-train-split-v1
|
[
"region:us"
] |
2023-03-12T19:00:35+00:00
|
{"dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "vi", "dtype": "string"}, {"name": "vi_annotated", "dtype": "string"}, {"name": "styles", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 293279.0, "num_examples": 640}, {"name": "val", "num_bytes": 69940.0, "num_examples": 160}, {"name": "if_test", "num_bytes": 33427.0, "num_examples": 80}, {"name": "f_test", "num_bytes": 36513.0, "num_examples": 80}], "download_size": 210801, "dataset_size": 433159.0}}
|
2023-03-12T19:00:50+00:00
|
101f0025379e0c3fbf1cbfa8034c3e4eac7ae249
|
# Dataset Card for "construction_place"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yiming19/construction_place
|
[
"region:us"
] |
2023-03-12T19:24:19+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 268981588.0, "num_examples": 32}], "download_size": 16173440, "dataset_size": 268981588.0}}
|
2023-03-12T20:07:20+00:00
|
f950b507022ac6e6413a1ca12281de9c401f1a79
|
Kanashimi/DacelfoMix
|
[
"license:openrail",
"region:us"
] |
2023-03-12T19:25:50+00:00
|
{"license": "openrail"}
|
2023-03-12T20:41:33+00:00
|
|
b31d91acc23295190d0862fb0d1d1f6095d756de
|
# Dataset Card for "hugo_dresses_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yotam56/hugo_dresses_ds
|
[
"region:us"
] |
2023-03-12T20:00:40+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Subfolder_1", "1": "Subfolder_10", "2": "Subfolder_11", "3": "Subfolder_12", "4": "Subfolder_13", "5": "Subfolder_14", "6": "Subfolder_15", "7": "Subfolder_16", "8": "Subfolder_17", "9": "Subfolder_18", "10": "Subfolder_19", "11": "Subfolder_2", "12": "Subfolder_20", "13": "Subfolder_21", "14": "Subfolder_22", "15": "Subfolder_23", "16": "Subfolder_24", "17": "Subfolder_3", "18": "Subfolder_4", "19": "Subfolder_5", "20": "Subfolder_6", "21": "Subfolder_7", "22": "Subfolder_8", "23": "Subfolder_9"}}}}], "splits": [{"name": "train", "num_bytes": 1193381.0, "num_examples": 120}], "download_size": 0, "dataset_size": 1193381.0}}
|
2023-03-14T15:17:42+00:00
|
106cb02577e6a60ada890ec5bed85219d2398ce4
|
# Dataset Card for "hugo_jackets_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yotam56/hugo_jackets_ds
|
[
"region:us"
] |
2023-03-12T20:23:05+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Subfolder_1", "1": "Subfolder_10", "2": "Subfolder_11", "3": "Subfolder_12", "4": "Subfolder_13", "5": "Subfolder_14", "6": "Subfolder_15", "7": "Subfolder_16", "8": "Subfolder_17", "9": "Subfolder_18", "10": "Subfolder_19", "11": "Subfolder_2", "12": "Subfolder_20", "13": "Subfolder_21", "14": "Subfolder_22", "15": "Subfolder_23", "16": "Subfolder_24", "17": "Subfolder_25", "18": "Subfolder_26", "19": "Subfolder_27", "20": "Subfolder_28", "21": "Subfolder_3", "22": "Subfolder_4", "23": "Subfolder_5", "24": "Subfolder_6", "25": "Subfolder_7", "26": "Subfolder_8", "27": "Subfolder_9"}}}}], "splits": [{"name": "train", "num_bytes": 1400017.0, "num_examples": 142}], "download_size": 0, "dataset_size": 1400017.0}}
|
2023-03-14T18:44:08+00:00
|
604967dc59cf5590a69d4c157979e8324197185f
|
# multilingual_librispeech_fr_processed
## Dataset Description
### Dataset Summary
The data files can be found on the illuin gcloud instance at this adress: unknown_url
This dataset has been processed from Huggingface Hub dataset ``facebook/multilingual_librispeech`` and the config ``french``
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
#### Columns
``audio`` ``sentence`` ``path`` ``taxonomy`` ``taxonomy_large`` ``sentence_processed``
#### Sample
```
{ 'audio': { 'array': array([ 2.7465820e-04, 3.3569336e-04, 2.1362305e-04, ...,
-3.0517578e-05, -3.0517578e-05, 0.0000000e+00], dtype=float32),
'path': '/home/brunohays/.cache/huggingface/datasets/downloads/extracted/13f33c266be7d9e111d1bbccdadea34d95cb6456b778bf5e74ba71f49f3caa9a/8778_9061_000897.flac',
'sampling_rate': 16000},
'path': '8778_9061_000897',
'sentence': 'la carte fut place devant lady helena et chacun se plaa de faon suivre la dmonstration de paganel â ainsi que je vous '
"l'ai dj appris dit le gographe apr s avoir travers l'amrique du sud le trente septi me degr de latitude rencontre les les "
"tristan d'acunha",
'sentence_processed': 'la carte fut place devant lady helena et chacun se plaa de faon suivre la dmonstration de paganel â ainsi que '
"je vous l'ai dj appris dit le gographe apr s avoir travers l'amrique du sud le trente septi me degr de latitude "
"rencontre les les tristan d'acunha",
'taxonomy': 'librispeech',
'taxonomy_large': 'librispeech'}
```
### Data Splits
|split|number_of_rows|
|:---:|:---:
|train|251463|
|test|2393|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
### Annotations
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the dataset
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Property of Illuin Technology
### Contributions
This dataset has been pushed using the repo [illuin-hf-dataset-pusher](https://gitlab.illuin.tech/data-science/ml/libraries/illuin-hf-dataset-pusher)
|
BrunoHays/multilingual_librispeech_fr_processed
|
[
"region:us"
] |
2023-03-12T20:38:30+00:00
|
{}
|
2023-03-15T09:46:36+00:00
|
a0d940eaeab07f73464de5bc0b032db8417967a0
|
# Dataset Card for "mix-closeup-lcd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tuperte69/mix-closeup-lcd
|
[
"region:us"
] |
2023-03-12T20:40:25+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 177761269.0, "num_examples": 81}], "download_size": 177632765, "dataset_size": 177761269.0}}
|
2023-03-12T21:14:39+00:00
|
d85158aae54fbdea8e52cf9541f60e7f1bbb3fa7
|
# RuNews dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Description](#description)
- [Usage](#usage)
- [Data Instances](#data-instances)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Description
**Summary:** Dataset of news from several sources:
* [Lenta.ru by yutkin](https://github.com/yutkin/Lenta.Ru-News-Dataset)
* [Several sources by buriy](https://github.com/buriy/russian-nlp-datasets/releases)
* [ODS Newsviz Tass](https://github.com/newsviz/newsviz)
* [Taiga fontanka](https://tatianashavrina.github.io/taiga_site/)
* [News from Telegram contest](https://github.com/IlyaGusev/tgcontest)
**Script:** [create_ru_news.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/create_ru_news.py)
**Point of Contact:** [Ilya Gusev]([email protected])
**Languages:** Russian.
## Usage
Prerequisites:
```bash
pip install datasets zstandard jsonlines pysimdjson
```
Dataset iteration:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/ru_news', split="train", streaming=True)
for example in dataset:
print(example["text"])
```
## Data Instances
```
{
"title": "Заместитель главы района в Якутии пожаловался на пьянство начальника",
"text": "Заместитель главы Нерюнгринского района Якутии Геннадий Ленц пожаловался руководителю республики Егору Борисову на своего начальника. Как рассказал Ленц 'Интерфаксу', Андрей Фитисов пьет на рабочем месте и 'уходит в многодневные загулы'...",
"timestamp": 1346284800,
"url": "https://lenta.ru/news/2012/08/30/alco/",
"source": "lenta"
}
```
## Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.
|
IlyaGusev/ru_news
|
[
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:ru",
"region:us"
] |
2023-03-12T20:56:14+00:00
|
{"language": ["ru"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "timestamp", "dtype": "uint64"}], "splits": [{"name": "train", "num_bytes": 12858731888, "num_examples": 4137525}], "download_size": 3669747077, "dataset_size": 12858731888}}
|
2023-03-20T23:05:08+00:00
|
eaf6afd256e582230a458848340cbdf12cab96c1
|
# Dataset Card for "sentiment_analysis_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sadiksha/sentiment_analysis_data
|
[
"region:us"
] |
2023-03-12T21:04:56+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1741533, "num_examples": 16000}, {"name": "test", "num_bytes": 217173, "num_examples": 2000}, {"name": "valid", "num_bytes": 214695, "num_examples": 2000}], "download_size": 1286836, "dataset_size": 2173401}}
|
2023-03-12T21:05:08+00:00
|
73fde21d57c8d447564e651cd3dacfea3a719a79
|
# Dataset Card for `face_synthetics`
This is a copy of [Microsoft FaceSynthetics dataset](https://github.com/microsoft/FaceSynthetics), uploaded to Hugging Face Datasets for convenience.
Please, refer to the original [license](LICENSE.txt), which we replicate in this repo.
The dataset was uploaded using the following code, which assumes the original `zip` file was uncompressed to `/data/microsoft_face_synthetics`:
```Python
from datasets import Dataset
from pathlib import Path
from PIL import Image
face_synthetics = Path("/data/microsoft_face_synthetics")
def entry_for_id(entry_id):
if type(entry_id) == int:
entry_id = f"{entry_id:06}"
image = Image.open(face_synthetics/f"{entry_id}.png")
image_seg = Image.open(face_synthetics/f"{entry_id}_seg.png")
with open(face_synthetics/f"{entry_id}_ldmks.txt") as f:
landmarks = f.read()
return {
"image": image,
"image_seg": image_seg,
"landmarks": landmarks,
}
def generate_entries():
for x in range(100000):
yield entry_for_id(x)
ds = Dataset.from_generator(generate_entries)
ds.push_to_hub('pcuenq/face_synthetics')
```
Note that `image_seg`, the segmented images, appear to be black because each pixel contains a number between `0` to `18` corresponging to the different categories, see the [original README]() for details. We haven't created visualization code yet.
|
pcuenq/face_synthetics
|
[
"region:us"
] |
2023-03-12T21:37:41+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_seg", "dtype": "image"}, {"name": "landmarks", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 33730885609.0, "num_examples": 100000}], "download_size": 34096881533, "dataset_size": 33730885609.0}}
|
2023-03-13T09:37:52+00:00
|
020aa19e8b0420b9a16aa550a521a92edee47a4a
|
LangChainDatasets/question-answering-state-of-the-union
|
[
"license:mit",
"region:us"
] |
2023-03-12T21:37:56+00:00
|
{"license": "mit"}
|
2023-03-12T21:39:00+00:00
|
|
920f745ef48a22f6f7718886e89e346951164344
|
LangChainDatasets/sql-qa-chinook
|
[
"license:mit",
"region:us"
] |
2023-03-12T22:08:41+00:00
|
{"license": "mit"}
|
2023-03-12T22:09:12+00:00
|
|
41b0e26895731e47f51d95760359a49cfe28cf7d
|
NadiaHolmlund/Japanese_Speech_Examples
|
[
"license:openrail",
"region:us"
] |
2023-03-12T22:12:56+00:00
|
{"license": "openrail"}
|
2023-03-12T22:19:19+00:00
|
|
db58bf34804e45ae32ae53284041696a08486de1
|
LangChainDatasets/agent-vectordb-qa-sota-pg
|
[
"license:mit",
"region:us"
] |
2023-03-12T22:23:19+00:00
|
{"license": "mit"}
|
2023-03-12T22:23:46+00:00
|
|
0199898ab8b2f26ad2e7e6496ac8d4e90199646d
|
LangChainDatasets/agent-search-calculator
|
[
"license:mit",
"region:us"
] |
2023-03-12T22:38:58+00:00
|
{"license": "mit"}
|
2023-03-12T22:42:29+00:00
|
|
93086879a16b67f66c24bd4ff1f6ba0f575cb648
|
koliskos/fake_news
|
[
"task_categories:text-classification",
"language:en",
"license:unknown",
"region:us"
] |
2023-03-12T23:19:42+00:00
|
{"language": ["en"], "license": "unknown", "task_categories": ["text-classification"]}
|
2023-03-12T23:27:32+00:00
|
|
39228b8c615fc13acc6cbec5b4a2b92baf38bbb5
|
# Dataset Card for "apps_partial_300_310"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minimario/apps_partial_300_310
|
[
"region:us"
] |
2023-03-13T00:25:50+00:00
|
{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17369435, "num_examples": 15529}], "download_size": 438347, "dataset_size": 17369435}}
|
2023-03-13T01:01:17+00:00
|
d9912bd0e635ba142574849b66ef4a2e4882ab6c
|
# MagicPrompt_SD_Washed
It's a version of datasets of [Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion).
When I want to train a model using origin data, some bad prompts broke model and waste many time.
So I washed the origin datasets:
1. 😄 delete some meanless words like some artists name with misspelling
2. 😂 delete many spaces that make `100mm` to `10 0m`
3. 😭 some url in datasets
4. 😭 and many unknown words
And this version is doing well in my train test!😍
|
KonghaYao/MagicPrompt_SD_Washed
|
[
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc0-1.0",
"code",
"art",
"region:us"
] |
2023-03-13T00:30:02+00:00
|
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "text-classification"], "pretty_name": "mg_sd_washed", "tags": ["code", "art"]}
|
2023-03-13T00:40:42+00:00
|
56bfacf175ea00e02cea49a8fa588a32f8a4f5a5
|
# Dataset Card for "apps_partial_0_300"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minimario/apps_partial_0_300
|
[
"region:us"
] |
2023-03-13T00:31:27+00:00
|
{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 685566901, "num_examples": 533723}], "download_size": 0, "dataset_size": 685566901}}
|
2023-03-13T00:59:45+00:00
|
cabb714a3042c981b616f8589b3bb704ee01b941
|
# Clean podcast audio
Split using VAD -> detect at least 30% cleaned speech, ~352 hours, https://github.com/huseinzol05/malaya-speech/tree/master/data/podcast
|
malaysia-ai/clean-podcast
|
[
"region:us"
] |
2023-03-13T00:59:00+00:00
|
{}
|
2023-03-17T08:23:11+00:00
|
2b0b662786e52b1205f0a5a2f12d23858459910b
|
# Pseudo-labeled-python-data-pii-detection-filtered
This dataset was used for the training of a PII detection NER model. We annotated it using pseudo-labelelling to enhance model performance on some rare PII entities like keys.
It consists of 18,000 files annotates using an ensemble of two encoder models Deberta-v3-large and stanford-deidentifier-base which were fine-tuned on a labeled PII dataset for code with 400 files from this work. To select good-quality pseudo-labels,
we computed the average probability logits between the models and filtered based on a minimum score. After inspection, we observed a high rate of false positives for Keys and Passwords, hence we retained only the entities that had a trigger word like key, auth and pwd in the surrounding context.
|
bigcode/pseudo-labeled-python-data-pii-detection-filtered
|
[
"region:us"
] |
2023-03-13T01:23:25+00:00
|
{"dataset_info": {"features": [{"name": "lang", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "pii", "dtype": "string"}], "splits": [{"name": "filtered", "num_bytes": 221082330, "num_examples": 17678}], "download_size": 0, "dataset_size": 221082330}}
|
2023-04-23T18:07:42+00:00
|
7deaf83aa998b303c03158eaedcf9c7cf2866206
|
# Dataset Card for "WhisperSmallTest200001"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Nolan1206/WhisperSmallTest200001
|
[
"region:us"
] |
2023-03-13T01:40:56+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 113600198, "num_examples": 534}, {"name": "test", "num_bytes": 113600198, "num_examples": 534}], "download_size": 228223568, "dataset_size": 227200396}}
|
2023-03-13T01:41:08+00:00
|
1b14f36a33bad97d354fd198991c7bab099d52ea
|
# Dataset Card for "WhisperSmallTest200002"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Nolan1206/WhisperSmallTest200002
|
[
"region:us"
] |
2023-03-13T02:00:47+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82981.0, "num_examples": 3}, {"name": "test", "num_bytes": 82981.0, "num_examples": 3}], "download_size": 169572, "dataset_size": 165962.0}}
|
2023-03-13T02:00:53+00:00
|
460d34ccccfbd9d87f91eb6916ca58ac0be4bb8b
|
Test Only
|
Cyberelay/nebula_ghostbusters
|
[
"task_categories:text-to-image",
"language:en",
"license:openrail",
"region:us"
] |
2023-03-13T02:12:13+00:00
|
{"language": ["en"], "license": "openrail", "task_categories": ["text-to-image"]}
|
2023-03-13T09:48:02+00:00
|
02170593d85739ce4ff558d2a5814b6f14550e4d
|
# Dataset Card for "apps_partial_600_900"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minimario/apps_partial_600_900
|
[
"region:us"
] |
2023-03-13T04:41:08+00:00
|
{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 475424024, "num_examples": 428815}], "download_size": 12893044, "dataset_size": 475424024}}
|
2023-03-13T05:13:37+00:00
|
fc283437dd43118cd4fe407c25683482ff21e9ce
|
# Dataset Card for "apps_partial_400_420"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minimario/apps_partial_400_420
|
[
"region:us"
] |
2023-03-13T04:41:16+00:00
|
{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30220693, "num_examples": 29683}], "download_size": 846453, "dataset_size": 30220693}}
|
2023-03-13T05:27:56+00:00
|
44b54baed25501c845c0ee5c2c08adb95091daed
|
This dataset can be used to fine-tune Speech To Text models as Text To Speech.
## dataset information
* Speaker: Aldo
* Dataset size: 535 audio files
* audio duration of 4-15 seconds (1:33:15)
## Dataset structure
This dataset has been structured in the LJSpeech format:
* wavs/
* 1.wav
* 2.wav
* 3.wav
* ---
* 535.wav
* transcript.csv
|
rmcpantoja/Ald_Mexican_Spanish_speech_dataset
|
[
"license:unlicense",
"region:us"
] |
2023-03-13T05:35:07+00:00
|
{"license": "unlicense"}
|
2023-03-13T05:59:04+00:00
|
962943597c93e80bf52e94cf6959a442c53e7e27
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains more than 2.1 million negative user reviews (reviews with 1 or 2 ratings) from 9775 apps across 48 categories from Google Play. Moreover, the number of votes that each review received within a month is also recorded. Those reviews having more votes can be cosidered as improtant reviews.
### Supported Tasks and Leaderboards
Detecting app issues proactively by identifying prominent app reviews.
### Languages
English
## How to use the dataset?
```
from datasets import load_dataset
import pandas as pd
# Load the dataset
dataset = load_dataset("recmeapp/thumbs-up")
# Convert to Pandas
dfs = {split: dset.to_pandas() for split, dset in dataset.items()}
dataset_df = pd.concat([dfs["train"], dfs["validation"], dfs["test"]])
# How many rows are there in the thumbs-up dataset?
print(f'There are {len(dataset_df)} rows in the thumbs-up dataset.')
# How many unique apps are there in the thumbs-up dataset?
print(f'There are {len(dataset_df["app_name"].unique())} unique apps.')
# How many categoris are there in the thumbs-up dataset?
print(f'There are {len(dataset_df["category"].unique())} unique categories.')
# What is the highest vote a review received in the thumbs-up dataset?
print(f'The highest vote a review received is {max(dataset_df["votes"])}.')
```
## Usage
This dataset was used for training the PPrior, a novel framework proposed in [this paper](https://ieeexplore.ieee.org/abstract/document/10020586). You can find the implementation in this [GitHub repository](https://github.com/MultifacetedNLP/PPrior).
|
recmeapp/thumbs-up
|
[
"task_categories:text-classification",
"size_categories:1M<n<10M",
"language:en",
"code",
"region:us"
] |
2023-03-13T06:05:49+00:00
|
{"language": ["en"], "size_categories": ["1M<n<10M"], "task_categories": ["text-classification"], "tags": ["code"]}
|
2023-03-13T08:56:10+00:00
|
377a9559fbafcc8108e363a7bbd878f8ffa850e6
|
# Dataset Card for "shrutilipi_mr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bhatvineet/shrutilipi_mr
|
[
"region:us"
] |
2023-03-13T07:12:52+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 114253169328.11655, "num_examples": 474332}, {"name": "test", "num_bytes": 39048725811.21545, "num_examples": 158111}], "download_size": 147662822982, "dataset_size": 153301895139.332}}
|
2023-03-13T18:54:45+00:00
|
7945933190894e55f2b8287e93a0e49edbf10ff6
|
<div align="center">
<img width="640" alt="trpakov/chest-xray-classification" src="https://huggingface.co/datasets/trpakov/chest-xray-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['PNEUMONIA', 'NORMAL']
```
### Number of Images
```json
{'test': 582, 'valid': 1165, 'train': 12230}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("trpakov/chest-xray-classification", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/3](https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/3?ref=roboflow2huggingface)
### Citation
```
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.ai on December 8, 2021 at 12:45 AM GMT
It includes 13977 images.
Pneumonia are annotated in folder format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)
The following augmentation was applied to create 3 versions of each source image:
* Random shear of between -3° to +3° horizontally and -2° to +2° vertically
* Random brigthness adjustment of between -5 and +5 percent
* Random exposure adjustment of between -5 and +5 percent
|
trpakov/chest-xray-classification
|
[
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"Biology",
"region:us"
] |
2023-03-13T07:23:40+00:00
|
{"task_categories": ["image-classification"], "tags": ["roboflow", "roboflow2huggingface", "Biology"]}
|
2023-03-13T07:23:48+00:00
|
f120a20521ffe7925cd84d16efb90a01f59d55a1
|
# Dataset Card for "kather_19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Reasat/kather_19
|
[
"region:us"
] |
2023-03-13T07:42:01+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 15223214158.0, "num_examples": 100000}], "download_size": 13426375812, "dataset_size": 15223214158.0}}
|
2023-03-13T07:49:18+00:00
|
794c9a48d06577c2ca960c774659e40ff554174e
|
AndrewMetaBlock/andrew_test
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-13T07:53:59+00:00
|
{"license": "apache-2.0"}
|
2023-03-13T07:53:59+00:00
|
|
d5cedbbdb9f1e7591569ebaf7cf1dd238f0b624b
|
```
@inproceedings{thukral-etal-2021-probing,
title = "Probing Language Models for Understanding of Temporal Expressions",
author = "Thukral, Shivin and
Kukreja, Kunal and
Kavouras, Christian",
booktitle = "Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.blackboxnlp-1.31",
doi = "10.18653/v1/2021.blackboxnlp-1.31",
pages = "396--406",
abstract = "We present three Natural Language Inference (NLI) challenge sets that can evaluate NLI models on their understanding of temporal expressions. More specifically, we probe these models for three temporal properties: (a) the order between points in time, (b) the duration between two points in time, (c) the relation between the magnitude of times specified in different units. We find that although large language models fine-tuned on MNLI have some basic perception of the order between points in time, at large, these models do not have a thorough understanding of the relation between temporal expressions.",
}
```
|
tasksource/temporal-nli
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-13T08:28:33+00:00
|
{"license": "apache-2.0"}
|
2023-03-24T10:30:25+00:00
|
40be8138bd9161ca1a28c8c704fa565af229f529
|
Heerak/abstract_summary
|
[
"license:afl-3.0",
"region:us"
] |
2023-03-13T08:51:28+00:00
|
{"license": "afl-3.0"}
|
2023-03-13T09:08:32+00:00
|
|
6e7340e6be87124f319e25290778760c14df64d3
|
### Dataset Summary
This dataset is a DeepL -based machine translated version of the Jigsaw toxicity dataset for Finnish. The dataset is originally from a Kaggle competition https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data.
The dataset poses a multi-label text classification problem and includes the labels `identity_attack`, `insult`, `obscene`, `severe_toxicity`, `threat` and `toxicity`.
#### Example data
```
{
"label_identity_attack": 0,
"label_insult": 0,
"label_obscene": 0,
"label_severe_toxicity": 0,
"label_threat": 0,
"label_toxicity": 0,
"lang": "fi-deepl",
"text": "\" \n\n Hei Pieter Pietersen, ja tervetuloa Wikipediaan! \n\n Tervetuloa Wikipediaan! Toivottavasti viihdyt tietosanakirjassa ja haluat jäädä tänne. Ensimmäiseksi voit lukea johdannon. \n\n Jos sinulla on kysyttävää, voit kysyä minulta keskustelusivullani - autan mielelläni. Tai voit kysyä kysymyksesi Uusien avustajien ohjesivulla. \n\n - \n Seuraavassa on lisää resursseja, jotka auttavat sinua tutkimaan ja osallistumaan maailman suurinta tietosanakirjaa.... \n\n Löydät perille: \n\n \n * Sisällysluettelo \n\n * Osastohakemisto \n\n \n Tarvitsetko apua? \n\n \n * Kysymykset - opas siitä, mistä voi esittää kysymyksiä. \n * Huijausluettelo - pikaohje Wikipedian merkintäkoodeista. \n\n * Wikipedian 5 pilaria - yleiskatsaus Wikipedian perustaan. \n * The Simplified Ruleset - yhteenveto Wikipedian tärkeimmistä säännöistä. \n\n \n Miten voit auttaa: \n\n \n * Wikipedian avustaminen - opas siitä, miten voit auttaa. \n\n * Yhteisöportaali - Wikipedian toiminnan keskus. \n\n \n Lisää vinkkejä... \n\n \n * Allekirjoita viestisi keskustelusivuilla neljällä tildillä (~~~~). Tämä lisää automaattisesti \"\"allekirjoituksesi\"\" (käyttäjänimesi ja päivämääräleima). Myös Wikipedian tekstinmuokkausikkunan yläpuolella olevassa työkalupalkissa oleva painike tekee tämän. \n\n * Jos haluat leikkiä uusilla Wiki-taidoillasi, Hiekkalaatikko on sinua varten. \n\n \n Onnea ja hauskaa. \""
}
```
### Data Fields
Fields marked as `label_` have either `0` to convey *not* having that category of toxicity in the text and `1` to convey having that category of toxicity present in the text.
- `label_identity_attack`: a `int64` feature.
- `label_insult`: a `int64` feature.
- `label_obscene`: a `int64` feature.
- `label_severe_toxicity`: a `int64` feature.
- `label_threat`: a `int64` feature.
- `label_toxicity`: a `int64` feature.
- `lang`: a `string` feature.
- `text`: a `string` feature.
### Data Splits
The splits are the same as in the original English data.
| dataset | train | test |
| -------- | -----: | ---------: |
| TurkuNLP/jigsaw_toxicity_pred_fi| 159571 | 63978 |
### Evaluation Results
Results from fine-tuning [TurkuNLP/bert-large-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-large-finnish-cased-v1) for multi-label toxicity detection. The fine-tuned model can be found
| dataset | F1-micro | Precision | Recall |
| -------------------- | ----: | ---: | ----: |
| TurkuNLP/jigsaw_toxicity_pred_fi | 0.66 | 0.58 | 0.76 |
<!--- Base results from fine-tuning [bert-large-cased](https://huggingface.co/bert-large-cased) on the original English data for multi-label toxicity detection.
| dataset | F1-micro | Precision | Recall |
| -------------------- | ----: | ---: | ----: |
| jigsaw_toxicity_pred | 0.69 | 0.59 | 0.81 | --->
### Considerations for Using the Data
Due to DeepL terms and conditions, this dataset **must not be used for any machine translation work**, namely machine translation
system development and evaluation of any kind. In general, we wish you do not pair the original English data with the translations
except when working on research unrelated to machine translation, so as not to infringe on the terms and conditions.
### Licensing Information
Contents of this repository are distributed under the
[Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).
Copyright of the dataset contents belongs to the original copyright holders.
### Citing
To cite this dataset use the following bibtex.
```
@inproceedings{eskelinen-etal-2023-toxicity,
title = "Toxicity Detection in {F}innish Using Machine Translation",
author = "Eskelinen, Anni and
Silvala, Laura and
Ginter, Filip and
Pyysalo, Sampo and
Laippala, Veronika",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.68",
pages = "685--697",
abstract = "Due to the popularity of social media platforms and the sheer amount of user-generated content online, the automatic detection of toxic language has become crucial in the creation of a friendly and safe digital space. Previous work has been mostly focusing on English leaving many lower-resource languages behind. In this paper, we present novel resources for toxicity detection in Finnish by introducing two new datasets, a machine translated toxicity dataset for Finnish based on the widely used English Jigsaw dataset and a smaller test set of Suomi24 discussion forum comments originally written in Finnish and manually annotated following the definitions of the labels that were used to annotate the Jigsaw dataset. We show that machine translating the training data to Finnish provides better toxicity detection results than using the original English training data and zero-shot cross-lingual transfer with XLM-R, even with our newly annotated dataset from Suomi24.",
}
```
|
TurkuNLP/jigsaw_toxicity_pred_fi
|
[
"task_categories:text-classification",
"task_ids:multi-label-classification",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:extended|jigsaw_toxicity_pred",
"language:fi",
"license:cc-by-sa-4.0",
"toxicity, multi-label",
"region:us"
] |
2023-03-13T08:58:01+00:00
|
{"language": ["fi"], "license": "cc-by-sa-4.0", "multilinguality": ["translation"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|jigsaw_toxicity_pred"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"], "tags": ["toxicity, multi-label"]}
|
2023-09-25T08:56:33+00:00
|
839e54d74780fc7ae978951b887e91536d4281a7
|
# Dataset Card for "annotated_github_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joetey/annotated_github_dataset
|
[
"region:us"
] |
2023-03-13T08:59:12+00:00
|
{"dataset_info": {"features": [{"name": "function", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "features", "sequence": "float32"}, {"name": "purpose", "dtype": "string"}, {"name": "detailed_description", "dtype": "string"}, {"name": "code_trans", "dtype": "string"}, {"name": "runtime", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 42019, "num_examples": 35}], "download_size": 21835, "dataset_size": 42019}}
|
2023-03-13T08:59:15+00:00
|
d29ba456044649c3c2fe29c5ff429c3e6d80a97b
|
https://github.com/HLR/SpartQA-baselines
```
@inproceedings{mirzaee-etal-2021-spartqa,
title = "{SPARTQA}: A Textual Question Answering Benchmark for Spatial Reasoning",
author = "Mirzaee, Roshanak and
Rajaby Faghihi, Hossein and
Ning, Qiang and
Kordjamshidi, Parisa",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.364",
doi = "10.18653/v1/2021.naacl-main.364",
pages = "4582--4598",
}
```
|
metaeval/spartqa-mchoice
|
[
"license:mit",
"region:us"
] |
2023-03-13T09:06:42+00:00
|
{"license": "mit"}
|
2023-06-09T16:34:13+00:00
|
150c819e88bba1859b8a763fd40d3a15033adc58
|
```
@inproceedings{mirzaee-etal-2021-spartqa,
title = "{SPARTQA}: A Textual Question Answering Benchmark for Spatial Reasoning",
author = "Mirzaee, Roshanak and
Rajaby Faghihi, Hossein and
Ning, Qiang and
Kordjamshidi, Parisa",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.364",
doi = "10.18653/v1/2021.naacl-main.364",
pages = "4582--4598",
}
```
|
metaeval/spartqa-yn
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-13T09:09:03+00:00
|
{"license": "apache-2.0"}
|
2023-03-13T09:12:09+00:00
|
3ccf416b77a32b0056b21267820263bb3fb7a31c
|
# Dataset card for reper2
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset description](#dataset-description)
- [Dataset categories](#dataset-categories)
## Dataset description
- **Homepage:** https://segments.ai/veronika/reper2
This dataset was created using [Segments.ai](https://segments.ai). It can be found [here](https://segments.ai/veronika/reper2).
## Dataset categories
| Id | Name | Description |
| --- | ---- | ----------- |
| 1 | circle | - |
| 2 | cross | - |
|
veroniccccccha/reper2
|
[
"task_categories:image-segmentation",
"region:us"
] |
2023-03-13T09:30:33+00:00
|
{"task_categories": ["image-segmentation"]}
|
2023-03-16T04:49:21+00:00
|
0c0a1bc27a253683bdcdd061007b82bfc7bbddb1
|
# StackExchange Paired
This is a processed version of the [`HuggingFaceH4/stack-exchange-preferences`](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences). The following steps were applied:
- Parse HTML to Markdown with `markdownify`
- Create pairs `(response_j, response_k)` where j was rated better than k
- Sample at most 10 pairs per question
- Shuffle the dataset globally
This dataset is designed to be used for preference learning. The processing notebook is in [the repository](https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main) as well.
|
lvwerra/stack-exchange-paired
|
[
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:10M<n<100M",
"language:en",
"region:us"
] |
2023-03-13T09:32:41+00:00
|
{"language": ["en"], "size_categories": ["10M<n<100M"], "task_categories": ["text-generation", "question-answering"], "pretty_name": "StackExchange Paired"}
|
2023-03-13T11:30:17+00:00
|
cb51eaf72e5df7ca3864beb47e4074cba6a95312
|
HisAtri/AudioCut
|
[
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-03-13T09:58:22+00:00
|
{"license": "cc-by-nc-sa-4.0"}
|
2023-03-13T10:02:36+00:00
|
|
387f1a63c0d04d17e1592b6e5faa63006a59761c
|
## Anh multilingual chat dataset
This is about 24M multilingual synthetic instructions intended to perform continued pretraining and finetuning a chatbot.
- cross_lingual.jsonl (~800000)
This dataset contains both the multi-lingual and cross-lingual version of the Anh data in the form of `Human: instruction\nAssistant: response` described here:
https://github.com/LAION-AI/Anh/tree/main/data .
The data is translated from a portion of the OIG dataset, which includes synthic_qa, prosocial and anthropic data. Read more about the data in LAION's OIG hf repo.
Covers these langs: zh, vi, ru, ms, pt, ja, id, hi, fr, es, de.
- xp3_sample.jsonl (~650000)
This dataset also contains a portion of the xp3 dataset converted into the standard Human/Assistant format.
See https://huggingface.co/datasets/bigscience/xP3 for the 43 languages covered by xp3.
- sungai_ul2_instructions.jsonl (~23000000)
This dataset also contains a UL2 like instruction set based on 140 languages from a subset of cc100, OSCAR and mc4.
You can find the individual datasets from which this UL2 version was created here: https://github.com/ontocord/sungai
## Disclaimer
- Translations may be inaccurate. The web text found in the UL2 file may contain inappropriate content as it is based on web scrapped data.
- Translations were generated by M2M 12B and the output generations were limited at 512 tokens due to VRAM limit (40G).
## License
The Anh dataset that is authored by LAION volunteers is released under an Apache 2.0 license. However, the data also includes content licensed under other permissive licenses, or web-crawled data which is used under fair use principles.
## Acknowledgement
- Thanks to LAION's Anh multilingual chat team: @yp_yurilee, @cahya, @kevin ko, @lasse, @mattdf, @theblackcat102, @yongzx, @acul3, @logus2, @paulovn, and many others.
- Thanks to @rallio67 for the original English version of the cross_lingual dataset.
- Thanks to @theblackcat102 for his translations at https://huggingface.co/datasets/theblackcat102/instruction_translations, from which the cross-lingual data is based.
- Thanks to the authors of all the underlying datasets from which Anh is based, including the xp3 authors, OSCAR, cc100 and mc4 authors.
|
laion/Anh
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-13T10:13:54+00:00
|
{"license": "apache-2.0"}
|
2023-03-21T19:00:53+00:00
|
8542ffcb4aaefb0f33381857c216f5cc7c1d0597
|
# Dataset Card for "reklamation24_unternehmen-verbaende-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_unternehmen-verbaende-full
|
[
"region:us"
] |
2023-03-13T10:16:59+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 28216223, "num_examples": 5336}], "download_size": 0, "dataset_size": 28216223}}
|
2023-04-25T13:11:09+00:00
|
a4f6cc82910f6a595aee6bc69a1e0581ae25eb3a
|
# Dataset Card for "reklamation24_medizin-gesundheit-pflege-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_medizin-gesundheit-pflege-full
|
[
"region:us"
] |
2023-03-13T10:18:51+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 38167353, "num_examples": 6936}], "download_size": 0, "dataset_size": 38167353}}
|
2023-04-25T13:12:45+00:00
|
f41e7f53c28a251a9111370c010b3018c65b3108
|
# Dataset Card for "reklamation24_transport-logistik-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_transport-logistik-full
|
[
"region:us"
] |
2023-03-13T10:20:43+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 37132250, "num_examples": 6706}], "download_size": 0, "dataset_size": 37132250}}
|
2023-04-25T13:14:15+00:00
|
955df7804337038dfdda7acd1d28edb1b58d0719
|
# Dataset Card for "reklamation24_versicherungen-recht-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_versicherungen-recht-full
|
[
"region:us"
] |
2023-03-13T10:21:28+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 11137639, "num_examples": 1796}], "download_size": 0, "dataset_size": 11137639}}
|
2023-04-25T13:14:44+00:00
|
fe79c18b26a96842caa23dab5370ab95d12b44ab
|
# Dataset Card for "reklamation24_oeffentlichkeit-soziales-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_oeffentlichkeit-soziales-full
|
[
"region:us"
] |
2023-03-13T10:21:46+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 2596252, "num_examples": 413}], "download_size": 0, "dataset_size": 2596252}}
|
2023-04-25T13:14:51+00:00
|
1c41969bbf04a25e5f5e4249ed5895533d6d90b7
|
# Dataset Card for "reklamation24_oeffentlicher-verkehr-vermietung-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_oeffentlicher-verkehr-vermietung-full
|
[
"region:us"
] |
2023-03-13T10:23:18+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 32455069, "num_examples": 5477}], "download_size": 0, "dataset_size": 32455069}}
|
2023-04-25T13:15:56+00:00
|
5ccf911cd4bd9bf47e8cbd7ee398df7ae085288c
|
# Dataset Card for "azuki-datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ethers/azuki-waifu-datasets
|
[
"license:openrail",
"region:us"
] |
2023-03-13T10:31:21+00:00
|
{"license": "openrail", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 109775830, "num_examples": 825}], "download_size": 109635616, "dataset_size": 109775830}}
|
2023-03-13T10:32:57+00:00
|
301889732fe2fa94a12efd3e5b384cbcf6d20794
|
WilliamWen/nickel_based_catalyst_001
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-13T10:31:38+00:00
|
{"license": "apache-2.0"}
|
2023-03-13T10:31:38+00:00
|
|
d0b4aa2c6b142fda852d4f4f371c1134560b69fa
|
# Dataset Card for "nq"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions)
### Dataset Summary
This is a modified version of the original Natural Questions (nq) dataset for qa tasks. The original is availabe [here](https://ai.google.com/research/NaturalQuestions).
Each sample was preprocessed into a squadlike format. The context was shortened from an entire wikipedia article into the passage containing the answer.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```json
{
"context": "The 2017 Major League Baseball All - Star Game was the 88th edition of the Major League Baseball All Star Game. The game was",
"question": "where is the 2017 baseball all-star game being played",
"answers":
{
"text":["Marlins Park"],
"answer_start":[171]
}
}
```
### Data Fields
The data fields are the same among all splits.
- `question`: a `string` feature.
- `context`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
## Additional Information
### Licensing Information
This dataset is distributed under the cc-by-sa-3.0 license.
|
LLukas22/nq-simplified
|
[
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:feature-extraction",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] |
2023-03-13T10:46:05+00:00
|
{"language": ["en"], "license": "cc-by-sa-3.0", "task_categories": ["question-answering", "sentence-similarity", "feature-extraction"]}
|
2023-04-30T19:28:17+00:00
|
5b0f898020e7bd74548450adef969d6d42cda73e
|
# Dataset Card for "diffMe"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sushmit/diffMe
|
[
"region:us"
] |
2023-03-13T12:05:11+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2346045522.71, "num_examples": 89395}], "download_size": 2318135039, "dataset_size": 2346045522.71}}
|
2023-03-13T12:07:55+00:00
|
3cfe60bc274af5d0a0fc854c2973bfde71e5ebdd
|
# Dataset Card for "ptb-sss"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liyongsea/ptb-sss
|
[
"region:us"
] |
2023-03-13T12:57:28+00:00
|
{"dataset_info": {"features": [{"name": "ecg_id", "dtype": "int64"}, {"name": "age", "dtype": "int32"}, {"name": "sex", "dtype": "string"}, {"name": "ecg_array", "dtype": {"array2_d": {"shape": [5000, 12], "dtype": "float32"}}}, {"name": "idx", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2600290, "num_examples": 10}], "download_size": 914715, "dataset_size": 2600290}}
|
2023-03-13T12:57:29+00:00
|
0ff9f88687f9df878b8a1224d4042e249911717d
|
# Dataset Card for "random-text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liyongsea/random-text
|
[
"region:us"
] |
2023-03-13T13:04:35+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11200, "num_examples": 100}], "download_size": 12812, "dataset_size": 11200}}
|
2023-03-13T13:06:02+00:00
|
aea2d0b2330e72a6d53cec8e1eab9273d85820bf
|
Clone of the dataset from https://laurencemoroney.com/datasets.html
|
jerpint/rock-paper-scissors
|
[
"license:cc-by-2.0",
"region:us"
] |
2023-03-13T13:07:45+00:00
|
{"license": "cc-by-2.0"}
|
2023-03-13T13:10:08+00:00
|
7537e6fb5c6754b6bd2f6d54b044d4040c046793
|
# Dataset Card for FaQuAD-NLI
## Dataset Description
- **Homepage:** https://github.com/liafacom/faquad
- **Repository:** https://github.com/liafacom/faquad
- **Paper:** https://ieeexplore.ieee.org/document/8923668/
<!-- - **Leaderboard:** -->
- **Point of Contact:** Eraldo R. Fernandes <[email protected]>
### Dataset Summary
FaQuAD is a Portuguese reading comprehension dataset that follows the format of the Stanford Question Answering Dataset (SQuAD). It is a pioneer Portuguese reading comprehension dataset using the challenging format of SQuAD. The dataset aims to address the problem of abundant questions sent by academics whose answers are found in available institutional documents in the Brazilian higher education system. It consists of 900 questions about 249 reading passages taken from 18 official documents of a computer science college from a Brazilian federal university and 21 Wikipedia articles related to the Brazilian higher education system.
FaQuAD-NLI is a modified version of the [FaQuAD dataset](https://huggingface.co/datasets/eraldoluis/faquad) that repurposes the question answering task as a textual entailment task between a question and its possible answers.
### Supported Tasks and Leaderboards
- `question_answering`: The dataset can be used to train a model for question-answering tasks in the domain of Brazilian higher education institutions.
- `textual_entailment`: FaQuAD-NLI can be used to train a model for textual entailment tasks, where answers in Q&A pairs are classified as either suitable or unsuitable.
### Languages
This dataset is in Brazilian Portuguese.
## Dataset Structure
### Data Fields
- `document_index`: an integer representing the index of the document.
- `document_title`: a string containing the title of the document.
- `paragraph_index`: an integer representing the index of the paragraph within the document.
- `question`: a string containing the question related to the paragraph.
- `answer`: a string containing the answer related to the question.
- `label`: an integer (0 or 1) representing if the answer is suitable (1) or unsuitable (0) for the question.
### Data Splits
The dataset is split into three subsets: train, validation, and test.
The splits were made carefully to avoid question and answer pairs belonging to the same document appearing in more than one split.
| | Train | Validation | Test |
|------------|-------|------------|------|
| Instances | 3128 | 731 | 650 |
### Contributions
Thanks to [@ruanchaves](https://github.com/ruanchaves) for adding this dataset.
|
ruanchaves/faquad-nli
|
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|wikipedia",
"language:pt",
"license:cc-by-4.0",
"region:us"
] |
2023-03-13T14:08:59+00:00
|
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["pt"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["extended|wikipedia"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "FaQuAD-NLI", "train-eval-index": [{"config": "plain_text", "task": "question-answering", "task_id": "extractive_question_answering", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"question": "question", "context": "context", "answers": {"text": "text", "answer_start": "answer_start"}}, "metrics": [{"type": "squad", "name": "SQuAD"}]}]}
|
2023-04-13T17:26:38+00:00
|
2177a7b3e4a755e2597087ba2675a23d06f0816a
|
test
|
faye1225/wshare
|
[
"region:us"
] |
2023-03-13T14:56:12+00:00
|
{}
|
2023-03-20T12:38:49+00:00
|
45084949150d6c18fb4a7bcb412635a441341004
|
# Dataset Card for "MedQuAD_47441_Context_Question_Answer_Triples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AnonymousSub/MedQuAD_47441_Context_Question_Answer_Triples
|
[
"region:us"
] |
2023-03-13T14:58:24+00:00
|
{"dataset_info": {"features": [{"name": "Contexts", "dtype": "string"}, {"name": "Questions", "dtype": "string"}, {"name": "Answers", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 190797665, "num_examples": 47441}], "download_size": 21780319, "dataset_size": 190797665}}
|
2023-03-13T14:58:26+00:00
|
7572e15da057f331bea9c768ecdcd59161f0c627
|
# Dataset Card for "maps_parquet_rgb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
davanstrien/maps_parquet_rgb
|
[
"region:us"
] |
2023-03-13T15:53:13+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "no building or railspace", "1": "railspace", "2": "building", "3": "railspace and non railspace building"}}}}, {"name": "map_sheet", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 120095091.0, "num_examples": 5000}, {"name": "test", "num_bytes": 297444323.5, "num_examples": 12404}, {"name": "train", "num_bytes": 889130785.5, "num_examples": 37212}], "download_size": 119972000, "dataset_size": 1306670200.0}}
|
2023-03-13T16:30:18+00:00
|
05f11dd52c289971da005eb3b7e43e6a05490437
|
# Dataset Card for "credit-card"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jbrazzy/credit-card
|
[
"region:us"
] |
2023-03-13T16:04:22+00:00
|
{"dataset_info": {"features": [{"name": "ID", "dtype": "int64"}, {"name": "LIMIT_BAL", "dtype": "float64"}, {"name": "SEX", "dtype": "int64"}, {"name": "EDUCATION", "dtype": "int64"}, {"name": "MARRIAGE", "dtype": "int64"}, {"name": "AGE", "dtype": "int64"}, {"name": "PAY_0", "dtype": "int64"}, {"name": "PAY_2", "dtype": "int64"}, {"name": "PAY_3", "dtype": "int64"}, {"name": "PAY_4", "dtype": "int64"}, {"name": "PAY_5", "dtype": "int64"}, {"name": "PAY_6", "dtype": "int64"}, {"name": "BILL_AMT1", "dtype": "int64"}, {"name": "BILL_AMT2", "dtype": "float64"}, {"name": "BILL_AMT3", "dtype": "float64"}, {"name": "BILL_AMT4", "dtype": "int64"}, {"name": "BILL_AMT5", "dtype": "float64"}, {"name": "BILL_AMT6", "dtype": "float64"}, {"name": "PAY_AMT1", "dtype": "float64"}, {"name": "PAY_AMT2", "dtype": "float64"}, {"name": "PAY_AMT3", "dtype": "float64"}, {"name": "PAY_AMT4", "dtype": "float64"}, {"name": "PAY_AMT5", "dtype": "float64"}, {"name": "PAY_AMT6", "dtype": "float64"}, {"name": "default.payment.next.month", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6000000, "num_examples": 30000}], "download_size": 2190378, "dataset_size": 6000000}}
|
2023-03-13T16:04:25+00:00
|
eef8719ffad2fbbe04259f050934b3d3e22864a4
|
# Dataset Card for "99problems"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
foldl/99problems
|
[
"region:us"
] |
2023-03-13T16:19:24+00:00
|
{"dataset_info": {"features": [{"name": "Question", "dtype": "string"}, {"name": "Solution", "dtype": "string"}, {"name": "Answer", "dtype": "string"}, {"name": "Themes", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1273441, "num_examples": 1000}], "download_size": 563808, "dataset_size": 1273441}}
|
2023-03-13T19:13:16+00:00
|
13dbe911ad8d35ec0582b307bd6272e88bce75e1
|
# Dataset Card for "geo_large_corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ZurabDz/geo_large_corpus
|
[
"region:us"
] |
2023-03-13T16:47:26+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14397567044, "num_examples": 41886434}], "download_size": 5031762025, "dataset_size": 14397567044}}
|
2023-03-13T17:19:58+00:00
|
cf1e699f6ab128275ae8a1d6f1ecdc16f51f09f8
|
jlmarrugom/gin-img-datasets
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-13T17:02:25+00:00
|
{"license": "apache-2.0"}
|
2023-03-13T17:24:21+00:00
|
|
d331d671d414f45807d06a48de7c0f2012f5ef0d
|
# Dataset Card for "StackExchange_Mar2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HuggingFaceGECLM/StackExchange_Mar2023
|
[
"region:us"
] |
2023-03-13T17:04:30+00:00
|
{"dataset_info": {"features": [{"name": "question_id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "metadata", "sequence": "string"}, {"name": "date", "dtype": "string"}, {"name": "original_text", "sequence": "string"}], "splits": [{"name": "3dprinting.meta", "num_bytes": 969521, "num_examples": 172}, {"name": "3dprinting", "num_bytes": 24134685, "num_examples": 4285}, {"name": "academia.meta", "num_bytes": 6877175, "num_examples": 1139}, {"name": "academia", "num_bytes": 280484682, "num_examples": 37355}, {"name": "ai.meta", "num_bytes": 1415694, "num_examples": 258}, {"name": "ai", "num_bytes": 39679129, "num_examples": 6428}, {"name": "android.meta", "num_bytes": 3569499, "num_examples": 811}, {"name": "android", "num_bytes": 105562019, "num_examples": 29426}, {"name": "anime.meta", "num_bytes": 6297025, "num_examples": 757}, {"name": "anime", "num_bytes": 46082709, "num_examples": 10459}, {"name": "apple.meta", "num_bytes": 5615240, "num_examples": 1284}, 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|
2023-03-16T19:06:45+00:00
|
7f17d6e8954a6466b12248dd29582f67a330431f
|
# Dataset Card for "oscar-2301-hpc"
## IMPORTANT NOTE: This dataset is intended to be downloaded as a snapshot and used directly on an HPC where keeping the number of inodes low is important. The files here are specifically designed to be very large, if you're looking for more managable file sizes please use the standard distribution of OSCAR instead: https://huggingface.co/datasets/oscar-corpus/OSCAR-2301
## IMPORTANT NOTE: THIS DATASET CARD IS STILL BEING WRITTEN, PLEASE BE PATIENT WHILE WE COMPLETE ALL THE INFORMATION ABOUT THE 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:** [https://oscar-project.org](https://oscar-project.org)
- **Repository:** [https://github.com/oscar-project](https://github.com/oscar-project)
- **Papers:** [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://aclanthology.org/2022.lrec-1.463/), [Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data](https://arxiv.org/abs/2212.10440)
- **Point of Contact:** [Contact](https://oscar-project.org/#contact)
### Dataset Summary
The OSCAR project (**O**pen **S**uper-large **C**rawled **A**ggregated co**R**pus) is an Open Source project aiming to provide web-based multilingual resources and datasets for Machine Learning (ML) and Artificial Intelligence (AI) applications. The project focuses specifically in providing large quantities of unannotated raw data that is commonly used in the pre-training of large deep learning models. The OSCAR project has developed [high-performance data pipelines](https://github.com/oscar-corpus/ungoliant) specifically conceived to classify and filter large amounts of [web data](https://commoncrawl.org/). The project has also put special attention in improving the data quality of web-based corpora as well as providing data for low-resource languages, so that these new ML/AI technologies are accessible to as many communities as possible.
OSCAR 23.01 is the January 2023 version of the OSCAR Corpus based on the [November/December 2022 dump of Common Crawl](https://commoncrawl.org/2022/12/nov-dec-2022-crawl-archive-now-available/). While being quite similar to OSCAR 22.01, it contains several new features, including [KenLM](https://kheafield.com/code/kenlm/)-based adult content detection, precomputed [Locality-Sensitive Hashes](https://fr.wikipedia.org/wiki/Locality_sensitive_hashing) for near deduplication, and [blocklist](https://dsi.ut-capitole.fr/blacklists/index_en.php)-based categories. OSCAR 23.01 has also moved from gzip to [Zstandard compression](https://facebook.github.io/zstd/). You might already have `zstd` installed on your system, but if not, please check the [Zstandard website](https://facebook.github.io/zstd/) for installation instructions.
### Supported Tasks and Leaderboards
OSCAR is mainly intended to pretrain language models and word representations.
### Languages
All the data is distributed by language, both the original and the deduplicated versions of the data are available. 151 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.
### Issues
OSCAR 23.01 may have quality issues on low size subcorpora, as it has been the case before.
Note that since the documents are identified as a whole, it is expected to have lines in other languages in a given language subcorpus.
As an example, it is known and expected that the German subcorpus contains documents holding lines identified as Swiss German / Alemannic.
**If you encounter something that is unexpected, please file an issue here: https://github.com/oscar-corpus/corpus/issues.**
|Language code|Language|Issues|
|-------------|--------|------|
| | | |
## Dataset Structure
We show detailed information for all the configurations of the dataset.
### Data Instances
TODO
### Layout
```js
{
"content":"English sentence\nphrase en français\n????????????", // (1)
"warc_headers":{ // (2)
"warc-identified-content-language":"fra,eng",
"warc-target-uri":"https://fr.wikipedia.org/wiki/...",
"warc-record-id":"<urn:uuid:29eaa920-d299-4b1d-b687-c72bd8d68116>",
"warc-type":"conversion",
"content-length":"35298", // (3)
"warc-refers-to":"<urn:uuid:39e42055-0d94-4e45-9c6c-9e7056635d64>",
"warc-block-digest":"sha1:WFH2A5WHCS2H365GIAFYQPI7UOAMFGHB", // (3)
"warc-date":"2022-11-26T09:45:47Z",
"content-type":"text/plain"
},
"metadata":{
"identification":{ // (4)
"label":"fr",
"prob":0.8938327
},
"harmful_pp":4063.1814, // (5)
"tlsh":"tlsh:T125315FF2B6088901EEA097015DB39B4600B...", // (6)
"quality_warnings":[ // (7)
"short_sentences",
"header",
"footer"
],
"categories":[ // (8)
"examen_pix",
"liste_bu"
],
"sentence_identifications":[ // (9)
{
"label":"fr",
"prob":0.99837273
},
{
"label":"en",
"prob":0.9992377
},
null
]
}
}
```
### Data Splits
<details>
<summary>Click to expand the number of samples per configuration</summary>
</details>
## Table
| | Code | Language | # docs | # words | Content Length : |
|----:|:-------|:-------------------------|:--------------|:----------------|:-----------------|
| 0 | af | Afrikaans | 23,994 | 6,217,024 | 37.2 MB |
| 1 | sq | Albanian | 1,342,790 | 462,694,599 | 3.2 GB |
| 2 | am | Amharic | 119,434 | 40,262,809 | 512.9 MB |
| 3 | ar | Arabic | 25,012,116 | 10,081,452,882 | 110.7 GB |
| 4 | an | Aragonese | 34 | 264 | 11.0 kB |
| 5 | hy | Armenian | 1,056,974 | 336,045,041 | 4.9 GB |
| 6 | as | Assamese | 89,542 | 24,395,215 | 412.1 MB |
| 7 | ast | Asturian | 440 | 10,917 | 74.1 kB |
| 8 | av | Avaric | 44 | 1,073 | 18.6 kB |
| 9 | az | Azerbaijani | 1,159,994 | 316,850,330 | 3.0 GB |
| 10 | bn | Bangla | 3,474,086 | 1,092,983,765 | 19.1 GB |
| 11 | ba | Bashkir | 128,248 | 26,036,637 | 363.7 MB |
| 12 | eu | Basque | 678,474 | 136,672,615 | 1.2 GB |
| 13 | be | Belarusian | 445,612 | 164,729,607 | 2.3 GB |
| 14 | bh | Bihari languages | 48 | 507 | 6.8 kB |
| 15 | bpy | Bishnupriya | 2,346 | 346,947 | 5.4 MB |
| 16 | bs | Bosnian | 20 | 395 | 3.0 kB |
| 17 | br | Breton | 36,338 | 4,759,407 | 31.4 MB |
| 18 | bg | Bulgarian | 8,933,998 | 3,635,273,738 | 44.1 GB |
| 19 | my | Burmese | 430,276 | 82,433,836 | 3.0 GB |
| 20 | ca | Catalan | 6,953,898 | 2,240,460,836 | 15.3 GB |
| 21 | ceb | Cebuano | 16,174 | 6,263,404 | 41.1 MB |
| 22 | ckb | Central Kurdish | 182,508 | 61,334,746 | 772.9 MB |
| 23 | ce | Chechen | 11,686 | 1,051,752 | 13.9 MB |
| 24 | zh | Chinese | 138,478,270 | 44,378,380,161 | 1.4 TB |
| 25 | cv | Chuvash | 16,652 | 3,039,925 | 42.3 MB |
| 26 | kw | Cornish | 8 | 80 | 432 Bytes |
| 27 | hr | Croatian | 31,808 | 3,542,961 | 26.5 MB |
| 28 | cs | Czech | 34,859,632 | 9,717,378,559 | 77.0 GB |
| 29 | da | Danish | 7,214,338 | 2,217,634,340 | 14.8 GB |
| 30 | dv | Divehi | 77,060 | 10,655,359 | 200.1 MB |
| 31 | nl | Dutch | 72,552,688 | 19,564,553,306 | 135.0 GB |
| 32 | mhr | Eastern Mari | 9,502 | 1,615,215 | 22.9 MB |
| 33 | arz | Egyptian Arabic | 3,958 | 385,511 | 3.7 MB |
| 34 | en | English | 1,235,510,986 | 523,869,288,690 | 3.4 TB |
| 35 | eo | Esperanto | 226,924 | 67,774,923 | 474.8 MB |
| 36 | et | Estonian | 3,601,904 | 938,296,892 | 8.0 GB |
| 37 | tl | Filipino | 250,558 | 110,560,444 | 719.2 MB |
| 38 | fi | Finnish | 14,471,710 | 4,198,143,883 | 41.1 GB |
| 39 | fr | French | 158,334,998 | 62,127,088,294 | 430.5 GB |
| 40 | gl | Galician | 248,762 | 38,345,625 | 255.7 MB |
| 41 | ka | Georgian | 1,343,036 | 373,935,158 | 8.4 GB |
| 42 | de | German | 206,598,430 | 73,848,586,648 | 594.7 GB |
| 43 | gom | Goan Konkani | 398 | 121,035 | 2.3 MB |
| 44 | el | Greek | 20,282,864 | 7,691,622,692 | 95.7 GB |
| 45 | gn | Guarani | 14 | 260 | 2.2 kB |
| 46 | gu | Gujarati | 425,552 | 417,001,705 | 5.6 GB |
| 47 | ht | Haitian Creole | 2 | 20,671 | 93.1 kB |
| 48 | he | Hebrew | 3,997,888 | 1,697,158,891 | 18.0 GB |
| 49 | hi | Hindi | 5,514,454 | 2,475,605,444 | 32.6 GB |
| 50 | hu | Hungarian | 21,349,372 | 16,013,364,289 | 150.1 GB |
| 51 | is | Icelandic | 1,210,232 | 294,471,539 | 2.2 GB |
| 52 | io | Ido | 224 | 2,598 | 16.1 kB |
| 53 | ilo | Iloko | 144 | 4,411 | 28.0 kB |
| 54 | id | Indonesian | 7,109,778 | 3,228,020,221 | 23.4 GB |
| 55 | ia | Interlingua | 34 | 9,384 | 33.5 kB |
| 56 | ie | Interlingue | 2 | 0 | 881 Bytes |
| 57 | ga | Irish | 29,894 | 9,054,923 | 63.2 MB |
| 58 | it | Italian | 89,021,606 | 36,327,274,203 | 259.4 GB |
| 59 | ja | Japanese | 94,236,404 | 4,401,059,165 | 181.2 GB |
| 60 | jv | Javanese | 172 | 3,286 | 25.7 kB |
| 61 | xal | Kalmyk | 2 | 27 | 315 Bytes |
| 62 | kn | Kannada | 448,500 | 124,924,350 | 2.6 GB |
| 63 | krc | Karachay-Balkar | 496 | 8,385 | 122.4 kB |
| 64 | kk | Kazakh | 677,622 | 214,679,857 | 3.3 GB |
| 65 | km | Khmer | 450,660 | 59,880,231 | 3.2 GB |
| 66 | kv | Komi | 460 | 5,909 | 70.3 kB |
| 67 | ko | Korean | 15,147,698 | 3,435,866,935 | 38.1 GB |
| 68 | ku | Kurdish | 80,338 | 25,921,607 | 174.1 MB |
| 69 | ky | Kyrgyz | 144,288 | 32,062,783 | 489.3 MB |
| 70 | lo | Lao | 118,374 | 10,659,203 | 472.1 MB |
| 71 | la | Latin | 14,384 | 307,865 | 2.0 MB |
| 72 | lv | Latvian | 2,435,882 | 845,459,899 | 7.4 GB |
| 73 | lez | Lezghian | 676 | 60,634 | 856.6 kB |
| 74 | li | Limburgish | 6 | 169 | 1.4 kB |
| 75 | lt | Lithuanian | 5,182,028 | 1,674,362,574 | 14.5 GB |
| 76 | jbo | Lojban | 572 | 312,315 | 1.5 MB |
| 77 | lmo | Lombard | 112 | 3,269 | 21.0 kB |
| 78 | nds | Low German | 5,248 | 1,612,175 | 10.7 MB |
| 79 | dsb | Lower Sorbian | 8 | 84 | 664 Bytes |
| 80 | lb | Luxembourgish | 18,090 | 2,514,838 | 18.4 MB |
| 81 | mk | Macedonian | 1,063,298 | 389,344,425 | 4.7 GB |
| 82 | mai | Maithili | 46 | 467 | 6.8 kB |
| 83 | mg | Malagasy | 10,830 | 1,416,430 | 11.2 MB |
| 84 | ms | Malay | 11,500 | 238,477 | 2.6 MB |
| 85 | ml | Malayalam | 800,936 | 236,597,838 | 5.8 GB |
| 86 | mt | Maltese | 5,180 | 149,886 | 1.3 MB |
| 87 | mr | Marathi | 729,578 | 252,706,331 | 4.5 GB |
| 88 | mzn | Mazanderani | 384 | 16,115 | 169.2 kB |
| 89 | min | Minangkabau | 2,436 | 305,589 | 3.8 MB |
| 90 | xmf | Mingrelian | 7,318 | 283,316 | 6.1 MB |
| 91 | mwl | Mirandese | 4 | 54 | 423 Bytes |
| 92 | mn | Mongolian | 1,061,710 | 454,350,415 | 5.8 GB |
| 93 | multi | **Multilingual** | 2,948,202 | 1,251,676,406 | 11.9 GB |
| 94 | nah | Nahuatl languages | 38 | 279 | 2.4 kB |
| 95 | ne | Nepali | 1,152,156 | 278,901,036 | 4.9 GB |
| 96 | new | Newari | 1,996 | 229,703 | 4.0 MB |
| 97 | no | Norwegian | 2,797,378 | 373,160,033 | 2.6 GB |
| 98 | nn | Norwegian Nynorsk | 19,470 | 575,518 | 3.7 MB |
| 99 | oc | Occitan | 920 | 34,701 | 405.0 kB |
| 100 | or | Odia | 158,426 | 31,963,340 | 543.1 MB |
| 101 | os | Ossetic | 8,628 | 3,935,964 | 50.7 MB |
| 102 | ps | Pashto | 87,408 | 30,196,179 | 261.6 MB |
| 103 | fa | Persian | 23,813,882 | 9,609,206,698 | 93.2 GB |
| 104 | pms | Piedmontese | 2,524 | 510,087 | 3.1 MB |
| 105 | pl | Polish | 57,184,826 | 18,073,705,588 | 147.1 GB |
| 106 | pt | Portuguese | 36,062,800 | 15,172,557,311 | 105.0 GB |
| 107 | pa | Punjabi | 222,058 | 104,235,418 | 1.4 GB |
| 108 | qu | Quechua | 2 | 13 | 143 Bytes |
| 109 | ro | Romanian | 11,985,668 | 6,302,600,833 | 45.6 GB |
| 110 | bxr | Russia Buriat | 72 | 698 | 8.2 kB |
| 111 | ru | Russian | 194,143,422 | 78,032,029,344 | 1.1 TB |
| 112 | sah | Sakha | 17,566 | 4,288,051 | 68.8 MB |
| 113 | sa | Sanskrit | 16,802 | 2,479,345 | 56.3 MB |
| 114 | gd | Scottish Gaelic | 776 | 18,458 | 146.1 kB |
| 115 | sr | Serbian | 1,677,896 | 632,781,822 | 7.7 GB |
| 116 | sh | Serbian (Latin) | 3,214 | 166,517 | 816.4 kB |
| 117 | sd | Sindhi | 48,566 | 14,667,207 | 131.6 MB |
| 118 | si | Sinhala | 301,066 | 172,755,385 | 2.6 GB |
| 119 | sk | Slovak | 8,931,784 | 2,704,716,280 | 21.5 GB |
| 120 | sl | Slovenian | 1,112,560 | 192,816,743 | 1.4 GB |
| 121 | so | Somali | 6 | 51 | 503 Bytes |
| 122 | azb | South Azerbaijani | 26,364 | 2,029,729 | 28.4 MB |
| 123 | es | Spanish | 153,574,556 | 63,388,237,965 | 429.9 GB |
| 124 | su | Sundanese | 18 | 258 | 2.0 kB |
| 125 | sw | Swahili | 1,664 | 164,459 | 1.0 MB |
| 126 | sv | Swedish | 21,891,348 | 6,993,719,601 | 50.0 GB |
| 127 | gsw | Swiss German | 342 | 34,328 | 232.7 kB |
| 128 | tg | Tajik | 144,932 | 76,987,285 | 1.0 GB |
| 129 | ta | Tamil | 1,638,238 | 738,824,392 | 15.8 GB |
| 130 | tt | Tatar | 262,654 | 59,253,765 | 833.8 MB |
| 131 | te | Telugu | 644,712 | 201,575,815 | 3.9 GB |
| 132 | th | Thai | 14,845,900 | 2,224,483,018 | 92.0 GB |
| 133 | bo | Tibetan | 62,352 | 6,062,558 | 531.6 MB |
| 134 | tr | Turkish | 26,654,330 | 8,290,890,087 | 73.7 GB |
| 135 | tk | Turkmen | 4,576 | 325,786 | 3.3 MB |
| 136 | uk | Ukrainian | 10,059,992 | 3,183,842,018 | 44.7 GB |
| 137 | x-eml | Emiliano-Romagnol | 4 | 329 | 1.8 kB |
| 138 | hsb | Upper Sorbian | 402 | 15,827 | 123.2 kB |
| 139 | ur | Urdu | 887,004 | 434,023,273 | 3.8 GB |
| 140 | ug | Uyghur | 51,304 | 14,659,554 | 219.8 MB |
| 141 | uz | Uzbek | 15,806 | 1,665,960 | 15.3 MB |
| 142 | vi | Vietnamese | 33,933,994 | 22,424,984,210 | 140.8 GB |
| 143 | vo | Volapük | 896 | 49,968 | 371.9 kB |
| 144 | wa | Walloon | 390 | 6,347 | 34.3 kB |
| 145 | war | Waray | 1,494 | 19,665 | 126.8 kB |
| 146 | cy | Welsh | 151,512 | 52,250,043 | 333.0 MB |
| 147 | fy | Western Frisian | 45,458 | 9,885,788 | 70.4 MB |
| 148 | mrj | Western Mari | 496 | 60,180 | 765.8 kB |
| 149 | pnb | Western Panjabi | 12,904 | 11,844,695 | 105.8 MB |
| 150 | wuu | Wu Chinese | 136 | 1,199 | 26.8 kB |
| 151 | yi | Yiddish | 47,438 | 14,287,370 | 171.7 MB |
| 152 | yo | Yoruba | 128 | 2,396 | 16.6 kB |
## Dataset Creation
### Curation Rationale
OSCAR was constructed using [`Ungoliant`](https://github.com/oscar-corpus/ungoliant), a new pipeline derived from [goclassy](https://github.com/oscar-corpus/goclassy), itself being derived from [fastText's one](https://github.com/facebookresearch/fastText).
The pipeline works on documents rather than lines.
`Ungoliant` is implemented in the [Rust programming language](https://rust-lang.org), and uses [rayon](https://github.com/rayon-rs/rayon) as its data parallelism strategy.
Threading is done at shard, record and sentence level, making the whole generation process much more efficient.
Filtering will be explained in a future blog post at our [website](https://oscar-corpus.com)
### Source Data
#### Initial Data Collection and Normalization
[Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies.
Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.
To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR 22.01, the **November/December 2021** snapshot was used. It is composed by 64 000 compressed text files containing documents and their headers.
#### Who are the source language producers?
The data comes from multiple web pages in a large variety of languages.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.
### Discussion of Biases
OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models.
### Other Known Limitations
The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571).
## Additional Information
### Dataset Curators
This release of OSCAR was made possible by [Julien Abadji](https://ujj.space), [Pedro Ortiz Suarez](https://portizs.eu/), [Rua Ismail](https://oscar-project.org/authors/rua/), [Sotaro Takeshita](https://sotaro.io/about), [Sebastian Nagel](https://www.polver.uni-konstanz.de/cnc/people/nagel/) and [Benoit Sagot](http://pauillac.inria.fr/~sagot/).
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging, the metadata and the annotations of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, the OSCAR project, Inria, the Univertity of Mannheim and DFKI GmbH have waived all copyright and related or neighboring rights to OSCAR
This work is published from: France and Germany.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@ARTICLE{2022arXiv221210440J,
author = {{Jansen}, Tim and {Tong}, Yangling and {Zevallos}, Victoria and {Ortiz Suarez}, Pedro},
title = "{Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
month = dec,
eid = {arXiv:2212.10440},
pages = {arXiv:2212.10440},
doi = {10.48550/arXiv.2212.10440},
archivePrefix = {arXiv},
eprint = {2212.10440},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv221210440J},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{abadji-etal-2022-towards,
title = "Towards a Cleaner Document-Oriented Multilingual Crawled Corpus",
author = "Abadji, Julien and
Ortiz Suarez, Pedro and
Romary, Laurent and
Sagot, Beno{\^\i}t",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.463",
pages = "4344--4355",
abstract = "The need for large corpora raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities.",
}
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
language = {en}
}
@article{kreutzer-etal-2022-quality,
title = "Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets",
author = {Kreutzer, Julia and
Caswell, Isaac and
Wang, Lisa and
Wahab, Ahsan and
van Esch, Daan and
Ulzii-Orshikh, Nasanbayar and
Tapo, Allahsera and
Subramani, Nishant and
Sokolov, Artem and
Sikasote, Claytone and
Setyawan, Monang and
Sarin, Supheakmungkol and
Samb, Sokhar and
Sagot, Beno{\^\i}t and
Rivera, Clara and
Rios, Annette and
Papadimitriou, Isabel and
Osei, Salomey and
Suarez, Pedro Ortiz and
Orife, Iroro and
Ogueji, Kelechi and
Rubungo, Andre Niyongabo and
Nguyen, Toan Q. and
M{\"u}ller, Mathias and
M{\"u}ller, Andr{\'e} and
Muhammad, Shamsuddeen Hassan and
Muhammad, Nanda and
Mnyakeni, Ayanda and
Mirzakhalov, Jamshidbek and
Matangira, Tapiwanashe and
Leong, Colin and
Lawson, Nze and
Kudugunta, Sneha and
Jernite, Yacine and
Jenny, Mathias and
Firat, Orhan and
Dossou, Bonaventure F. P. and
Dlamini, Sakhile and
de Silva, Nisansa and
{\c{C}}abuk Ball{\i}, Sakine and
Biderman, Stella and
Battisti, Alessia and
Baruwa, Ahmed and
Bapna, Ankur and
Baljekar, Pallavi and
Azime, Israel Abebe and
Awokoya, Ayodele and
Ataman, Duygu and
Ahia, Orevaoghene and
Ahia, Oghenefego and
Agrawal, Sweta and
Adeyemi, Mofetoluwa},
journal = "Transactions of the Association for Computational Linguistics",
volume = "10",
year = "2022",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2022.tacl-1.4",
doi = "10.1162/tacl_a_00447",
pages = "50--72",
abstract = "With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource corpora have systematic issues: At least 15 corpora have no usable text, and a significant fraction contains less than 50{\%} sentences of acceptable quality. In addition, many are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-proficient speakers, and supplement the human audit with automatic analyses. Finally, we recommend techniques to evaluate and improve multilingual corpora and discuss potential risks that come with low-quality data releases.",
}
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
```
|
oscar-corpus/oscar-2301-hpc
|
[
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:multilingual",
"size_categories:n>1T",
"source_datasets:original",
"license:cc0-1.0",
"arxiv:2212.10440",
"arxiv:2010.14571",
"region:us"
] |
2023-03-13T17:06:34+00:00
|
{"license": "cc0-1.0", "multilinguality": ["multilingual"], "size_categories": ["n>1T"], "source_datasets": ["original"], "task_categories": ["fill-mask", "text-generation"], "task_ids": ["language-modeling"], "paperswithcode_id": "oscar", "extra_gated_prompt": "By filling the form below, you understand that only the metadata and the annotations of OSCAR 23.01 have a cc0-1.0 license, and that the rest of the content is crawled data derived from the November/December 2022 snapshot of Common Crawl, for which the authors of OSCAR **do not** hold any copyright whatsoever.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Country": "text", "Usecase": "text", "I have explicitly check with my jurisdiction and I confirm that downloading OSCAR 2301 is legal in the country/region where I am located right now, and for the use case that I have described above": "checkbox"}}
|
2023-04-18T08:10:18+00:00
|
dce01c9b08f87459cf36a430d809084718273017
|
# Dataset Card for Alpaca
## Dataset Description
- **Homepage:** https://crfm.stanford.edu/2023/03/13/alpaca.html
- **Repository:** https://github.com/tatsu-lab/stanford_alpaca
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** Rohan Taori
### Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).
### Supported Tasks and Leaderboards
The Alpaca dataset designed for instruction training pretrained language models.
### Languages
The data in Alpaca are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"instruction": "Create a classification task by clustering the given list of items.",
"input": "Apples, oranges, bananas, strawberries, pineapples",
"output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
"text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
}
```
### Data Fields
The data fields are as follows:
* `instruction`: describes the task the model should perform. Each of the 52K instructions is unique.
* `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input.
* `output`: the answer to the instruction as generated by `text-davinci-003`.
* `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models.
### Data Splits
| | train |
|---------------|------:|
| alpaca | 52002 |
## 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
Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset:
> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```
### Contributions
[More Information Needed]
|
tatsu-lab/alpaca
|
[
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"region:us"
] |
2023-03-13T17:19:43+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "pretty_name": "Alpaca", "tags": ["instruction-finetuning"]}
|
2023-05-22T19:33:36+00:00
|
400e470a4ec2e0f77bf46f928c312107979af34a
|
The next_frame_dataset is the raw images, with the first_frame having the starting frame, and the next_frame having the frame right after that one
The next_frame_dataset_resized is the same, but with its height and width being rounded to closest 64. Also its in png now
The next_frame_dataset_resized2 is the above but any images above 512x512 pixel count has been resized to maintain the aspect ratio but lower the pixel count, and then has its height and width rounded down to closest 64.
|
SwayStar123/anime_next_frame
|
[
"region:us"
] |
2023-03-13T17:45:26+00:00
|
{}
|
2023-03-19T21:27:05+00:00
|
5beff9d599fb9511de697b53a19e7787983c7ad7
|
应该没外国人用，直接用中文吧
`dingzhen.zip` 是声源压缩文件，声源来自两部分
- 某次录播(qh_0_*.wav)：[220725丁真直播录屏完整版_哔哩哔哩_bilibili](https://www.bilibili.com/video/BV15S4y1t789)
- 粘合国演讲(qh_1_*.wav)：[丁真 出席联合国演讲(完整版)毫不怯场 从容自若 好有魅力_哔哩哔哩_bilibili](https://www.bilibili.com/video/BV1NN411f7mN)
`cuts.txt` 是对应文件字幕。
|
acdzh/dingzhen-voice
|
[
"license:mit",
"region:us"
] |
2023-03-13T17:49:22+00:00
|
{"license": "mit"}
|
2023-03-13T18:06:54+00:00
|
5f9ffc298d40b9c92ea681a43c7889280dda00ef
|
acdzh/jiaran-voice
|
[
"license:mit",
"region:us"
] |
2023-03-13T17:58:56+00:00
|
{"license": "mit"}
|
2023-03-13T17:58:56+00:00
|
|
ab0b67e7c56c529e7b3202dfe4ae74262e89fe74
|
# Dataset Card for "bookcorpus_stage2_coverage_100000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MartinKu/bookcorpus_stage2_coverage_100000
|
[
"region:us"
] |
2023-03-13T18:30:55+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "S_V_position", "sequence": "int64"}, {"name": "O_C_position", "sequence": "int64"}, {"name": "start_point_list", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 60883646, "num_examples": 99955}], "download_size": 7551557, "dataset_size": 60883646}}
|
2023-03-14T23:51:31+00:00
|
4dcd0b0800ee19b75e29a2699dfc73bcb7724ee3
|
野兽先辈音声素材
来源：https://www.nicovideo.jp/watch/sm31721928
|
acdzh/tadokoro-voice
|
[
"license:mit",
"region:us"
] |
2023-03-13T18:36:02+00:00
|
{"license": "mit"}
|
2023-03-13T18:37:27+00:00
|
d4d48371e76b0c46717592c32c183c04b19ad16b
|
# Dataset Card for "Genomic_Benchmarks_dummy_mouse_enhancers_ensembl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
katarinagresova/Genomic_Benchmarks_dummy_mouse_enhancers_ensembl
|
[
"region:us"
] |
2023-03-13T19:04:16+00:00
|
{"dataset_info": {"features": [{"name": "seq", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2273646, "num_examples": 968}, {"name": "test", "num_bytes": 608062, "num_examples": 242}], "download_size": 294310, "dataset_size": 2881708}}
|
2023-03-13T19:33:25+00:00
|
e0003669df0a180c8570ebe091ed91f20fa080ec
|
# Dataset Card for "Genomic_Benchmarks_human_nontata_promoters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
katarinagresova/Genomic_Benchmarks_human_nontata_promoters
|
[
"region:us"
] |
2023-03-13T19:06:27+00:00
|
{"dataset_info": {"features": [{"name": "seq", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 7126511, "num_examples": 27097}, {"name": "test", "num_bytes": 2375942, "num_examples": 9034}], "download_size": 0, "dataset_size": 9502453}}
|
2023-03-13T19:33:47+00:00
|
85458172cab6cffad46cb172e64ca94780506ed6
|
# Dataset Card for "Genomic_Benchmarks_demo_coding_vs_intergenomic_seqs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
katarinagresova/Genomic_Benchmarks_demo_coding_vs_intergenomic_seqs
|
[
"region:us"
] |
2023-03-13T19:33:48+00:00
|
{"dataset_info": {"features": [{"name": "seq", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 15900000, "num_examples": 75000}, {"name": "test", "num_bytes": 5300000, "num_examples": 25000}], "download_size": 2456511, "dataset_size": 21200000}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-10-04T12:10:11+00:00
|
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