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--- |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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language: |
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- en |
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size_categories: |
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- 1K<n<200K |
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--- |
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### Dataset info |
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#### Training Dataset: |
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You are provided with a large number of Wikipedia comments which have been labeled by human raters for toxic behavior. The types of toxicity are: |
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- toxic |
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- severe_toxic |
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- obscene |
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- threat |
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- insult |
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- identity_hate |
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The original dataset can be found here: [jigsaw_toxic_classification](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/data) |
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Our training dataset is a sampled version from the original dataset, <b>containing equal number of samples for both clean and toxic classes. </b><br> |
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#### Dataset creation: |
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<code><pre>data = pd.read_csv('train.csv') # train.csv from the original dataset |
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column_names = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'] |
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column_labels = data[column_names][2:-1] |
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train_toxic = data[data[column_names].sum(axis=1) > 0] |
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train_clean = data[data[column_names].sum(axis=1) == 0] |
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train_clean_sampled = train_clean.sample(n=16225, random_state=42) |
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dataframe = pd.concat([train_toxic, train_clean_sampled], axis=0) |
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dataframe = dataframe.sample(frac=1, random_state=42) |
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dataset = Dataset.from_pandas(dataframe) |
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train_dataset = dataset.train_test_split(test_size=0.2)['train'] |
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val_dataset = dataset.train_test_split(test_size=0.2)['test']</pre></code> |
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### Caution: |
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This dataset contains comments that are toxic in nature. Kindly use appropriately. |
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### Citation |
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<pre> |
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@misc{jigsaw-toxic-comment-classification-challenge, |
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author = {cjadams, Jeffrey Sorensen, Julia Elliott, Lucas Dixon, Mark McDonald, nithum, Will Cukierski}, |
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title = {Toxic Comment Classification Challenge}, |
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publisher = {Kaggle}, |
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year = {2017}, |
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url = {https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge} |
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}</pre> |
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