Datasets:
Eval metadata Batch 4: Tweet Eval, Tweets Hate Speech Detection, VCTK, Weibo NER, Wisesight Sentiment, XSum, Yahoo Answers Topics, Yelp Polarity, Yelp Review Full (#4338)
Browse files* Eval metadata for Tweet Eval, Tweets Hate Speech Detection, VCTK, Weibo NER, Wisesight Sentiment, XSum, Yahoo Answers Topics, Yelp Polarity, Yelp Review Full
* Update README.md
oops, duplicate
* Update README.md
adding header
* Update README.md
adding header
* Update datasets/xsum/README.md
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* Update README.md
removing ROUGE args
* Update README.md
removing F1 params
* Update README.md
* Update README.md
updating to binary F1
* Update README.md
oops typo
* Update README.md
F1 binary
Co-authored-by: sashavor <sasha.luccioni@huggingface.co>
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
Commit from https://github.com/huggingface/datasets/commit/bce8e6541af98cb0e06b24f9c96a6431cf813a38
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- sentiment-classification
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paperswithcode_id: null
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pretty_name: WisesightSentiment
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---
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# Dataset Card for wisesight_sentiment
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### Data Fields
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- `texts`: texts
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- `category`: sentiment of texts ranging from `pos` (positive; 0), `neu` (neutral; 1), `neg` (negative; 2) and `q` (question; 3)
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### Data Splits
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### Dataset Curators
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Thanks [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) community, [Kitsuchart Pasupa](http://www.it.kmitl.ac.th/~kitsuchart/) (Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang), and [Ekapol Chuangsuwanich](https://www.cp.eng.chula.ac.th/en/about/faculty/ekapolc/) (Faculty of Engineering, Chulalongkorn University) for advice. The original Kaggle competition, using the first version of this corpus, can be found at https://www.kaggle.com/c/wisesight-sentiment/
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### Licensing Information
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- sentiment-classification
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paperswithcode_id: null
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pretty_name: WisesightSentiment
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train-eval-index:
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- config: wisesight_sentiment
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task: text-classification
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task_id: multi_class_classification
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splits:
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train_split: train
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eval_split: test
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col_mapping:
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texts: text
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category: target
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metrics:
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- type: accuracy
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name: Accuracy
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- type: f1
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name: F1 macro
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args:
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average: macro
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- type: f1
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name: F1 micro
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args:
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average: micro
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- type: f1
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name: F1 weighted
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args:
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average: weighted
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- type: precision
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name: Precision macro
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args:
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average: macro
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- type: precision
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name: Precision micro
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args:
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average: micro
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- type: precision
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name: Precision weighted
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args:
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average: weighted
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- type: recall
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name: Recall macro
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args:
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average: macro
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- type: recall
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name: Recall micro
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args:
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average: micro
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- type: recall
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name: Recall weighted
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args:
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average: weighted
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---
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# Dataset Card for wisesight_sentiment
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### Data Fields
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- `texts`: texts
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- `category`: sentiment of texts ranging from `pos` (positive; 0), `neu` (neutral; 1), `neg` (negative; 2) and `q` (question; 3)
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### Data Splits
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### Dataset Curators
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Thanks [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) community, [Kitsuchart Pasupa](http://www.it.kmitl.ac.th/~kitsuchart/) (Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang), and [Ekapol Chuangsuwanich](https://www.cp.eng.chula.ac.th/en/about/faculty/ekapolc/) (Faculty of Engineering, Chulalongkorn University) for advice. The original Kaggle competition, using the first version of this corpus, can be found at https://www.kaggle.com/c/wisesight-sentiment/
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### Licensing Information
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