Sasha Luccioni commited on
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Eval metadata batch 2 : Health Fact, Jigsaw Toxicity, LIAR, LJ Speech, MSRA NER, Multi News, NCBI Disease, Poem Sentiment (#4336)

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* Eval metadata batch 2 : Health Fact, Jigsaw Toxicity, LIAR, LJ Speech, MSRA NER, Multi News, NCBI Disease, PiQA, Poem Sentiment, QAsper

* Update README.md

fixing header

* Update datasets/piqa/README.md

Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>

* Update README.md

changing MSRA NER metric to `seqeval`

* Update README.md

removing ROUGE args

* Update README.md

removing duplicate information

* Update README.md

removing eval for now

* Update README.md

removing eval for now

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/095d12ff7414df118f60e00cd6494299a881743a

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  1. README.md +51 -2
README.md CHANGED
@@ -19,6 +19,55 @@ task_ids:
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  - sentiment-classification
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  paperswithcode_id: gutenberg-poem-dataset
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  pretty_name: Gutenberg Poem Dataset
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for Gutenberg Poem Dataset
@@ -79,12 +128,12 @@ Example of one instance in the dataset.
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  - `id`: index of the example
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  - `verse_text`: The text of the poem verse
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- - `label`: The sentiment label. Here
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  - 0 = negative
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  - 1 = positive
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  - 2 = no impact
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  - 3 = mixed (both negative and positive)
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- > Note: The original dataset uses different label indices (negative = -1, no impact = 0, positive = 1)
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  ### Data Splits
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  - sentiment-classification
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  paperswithcode_id: gutenberg-poem-dataset
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  pretty_name: Gutenberg Poem Dataset
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+ train-eval-index:
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+ - config: default
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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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+ verse_text: text
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+ label: 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 Gutenberg Poem Dataset
 
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  - `id`: index of the example
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  - `verse_text`: The text of the poem verse
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+ - `label`: The sentiment label. Here
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  - 0 = negative
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  - 1 = positive
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  - 2 = no impact
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  - 3 = mixed (both negative and positive)
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+ > Note: The original dataset uses different label indices (negative = -1, no impact = 0, positive = 1)
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  ### Data Splits
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