This project demonstrates how to use the BERT (Bidirectional Encoder Representations from Transformers) model for classifying comments. It is designed to identify and categorize user comments based on their semantic content—useful for detecting spam, abuse, sentiment, or topic relevance in forums, customer feedback, or online platforms.
Fine-tuning of a pretrained BERT model (bert-base-uncased) for text classification tasks
Data preprocessing and tokenization using Hugging Face's transformers and datasets
Training with GPU support and performance tracking via accuracy and loss metrics
Evaluation with classification report and confusion matrix for detailed insight
git clone https://github.com/yourusername/comment-classification-bert.git cd comment-classification-bert
pip install -r requirements.txt
pip install torch transformers datasets scikit-learn matplotlib seaborn
Launch the notebook in Jupyter or any compatible IDE:
jupyter notebook comment_classification_task.ipynb
The dataset used consists of labeled user comments. Each comment is associated with a category label. The supported labels in this project are:
You may customize the notebook to load your own dataset in CSV or JSON format with the following structure:
comment,text,label 1,"This is a great product!",non-toxic 2,"You are terrible!",toxic
Model: bert-base-uncased (Hugging Face Transformers)
Fine-tuned for text classification
Optimizer: AdamW
Loss Function: CrossEntropyLoss
Evaluation: Accuracy, Confusion Matrix, Precision/Recall/F1
After training, the model is evaluated on a test set, and results include:
Accuracy score
Confusion matrix (visualized using seaborn)
Classification report with precision, recall, and F1-score
The notebook includes plots of training and validation loss, and confusion matrix for better interpretability.
Replace dataset with your own comment data
Adjust number of epochs, batch size, and learning rate
Add early stopping or additional metrics