Toxic Comment Detection Model
This model is trained to detect toxic comments in text.
Metrics
- Validation Accuracy: 0.7950
- Training Accuracy: 0.7950
- Overfitting: 0.00%
Base Model
Model Details
- Task: Binary Classification (Toxic/Non-toxic)
- Language: English
- Training Dataset: Toxic Comments Dataset
Model Details
- Base Architecture: BERT (bert-base-uncased)
- Type: Binary classification
- Maximum sequence length: 128 tokens
- Language: English
- Classes:
Training Hyperparameters
- Batch size: 16
- Initial learning rate: 2e-5
- Epochs: 8 (with early stopping)
- Dropout rate: 0.2
- Weight decay: 0.01
- Optimizer: AdamW
- Scheduler: Linear with warmup
Limitations
- Model is trained only for English text
- Maximum input length: 128 tokens
- Model may have inherent biases from the training dataset
- Not optimized for very short or very long texts
Intended Use
This model is designed for:
- Automatic comment moderation
- Toxic content detection in online platforms
- Toxicity analysis in digital conversations
