Streamlit app for hate speech detection using a fine-tuned BERT-based model. The model is trained on the Jigsaw Toxic Comment Classification Challenge dataset for multi-label classification.
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Code and data is available at this notebook
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The app is deployed and can be tested here (also available at this link)
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The model is available at hugging face
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Clone the repository:
git clone https://github.com/berkaysahiin/SE464.git
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Change into the directory:
cd SE464 -
Virtual Environments:
virtualenv venv .\venv\Scripts\activate
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Requirements:
pip install -r requirements.txt # if fails try before: pip install pipreqs && pipreqs -
Run the Streamlit app:
streamlit run main.py
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Data preprocessing involves cleaning text data, tokenization, and formatting for multi-label classification.
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The model is trained with TrainingArguments and Trainer from the Transformers library.
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Metrics such as F1 score, ROC AUC, and accuracy are used to evaluate the model's performance on the test set.