---
language: en
license: apache-2.0
tags:
- text-classification
- toxic-comments
- distilbert
- multi-label-classification
datasets:
- jigsaw-toxic-comment-classification
metrics:
- f1
- roc_auc
---

# Toxic Comment Classifier

Fine-tuned DistilBERT for multi-label toxic comment classification,
wrapped in a live FastAPI endpoint with Docker deployment.

## Model Details
- **Base model:** distilbert-base-uncased
- **Task:** Multi-label text classification (6 labels)
- **Labels:** toxic, severe_toxic, obscene, threat, insult, identity_hate
- **Training data:** Jigsaw Toxic Comment Classification (159,571 comments)
- **Framework:** HuggingFace Transformers + Trainer API
- **Training time:** ~28 minutes on T4 GPU

## Performance
| Metric | Score |
|--------|-------|
| ROC-AUC (macro) | 0.990 |
| F1 (macro) | 0.652 |

> F1 is lower due to severe class imbalance — threat label has only 478
> positive examples out of 159k. ROC-AUC of 0.990 reflects true model quality.

## Latency Benchmark (GPU, single request)
| Percentile | Latency |
|------------|---------|
| p50  | 7.3ms  |
| p95  | 9.9ms  |
| p99  | 19.4ms |
| min  | 4.1ms  |
| max  | 26.2ms |
| mean | 7.3ms  |

## Live API
- Swagger UI: https://chandkr123-toxic-classifier-api.hf.space/docs
- Predict endpoint: https://chandkr123-toxic-classifier-api.hf.space/predict

## Example Predictions
| Comment | Flagged |
|---------|---------|
| I love this community! | clean |
| You are the most stupid idiot | toxic, insult, obscene |
| I will find you and hurt you | toxic, threat |
| I will beat you in the hotel | toxic, threat |

## Limitations
- Trained on Wikipedia comments, may not generalise to all domains
- Severe class imbalance: threat label has only 478 training examples
- Predictions above 0.5 threshold are flagged (adjustable)
- Not suitable for production content moderation without human review