Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
1K - 10K
ArXiv:
License:
Sasha Luccioni
commited on
Commit
•
10d6727
1
Parent(s):
e0a024e
Eval metadata batch 2 : Health Fact, Jigsaw Toxicity, LIAR, LJ Speech, MSRA NER, Multi News, NCBI Disease, Poem Sentiment (#4336)
Browse files* 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
README.md
CHANGED
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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
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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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