--- language: en license: mit tags: - content-safety - text-classification - safety - moderation - deberta datasets: - jainsatyam26/guardrail-215k-splits metrics: - f1 - accuracy widget: - text: "How to make a bomb?" example_title: "Violent Content" - text: "Hello, how are you?" example_title: "Safe Content" --- # High-Accuracy Content Safety Classifier This model is a fine-tuned DeBERTa classifier for content safety, achieving high performance on safety classification tasks. ## Model Details - **Base Model**: microsoft/deberta-v3-large - **Training Dataset**: jainsatyam26/guardrail-215k-splits - **Categories**: 10 safety categories - **Training Time**: Auto-deployed during training - **Last Updated**: 2026-04-29 06:21:01 UTC ## Performance | Metric | Value | |--------|-------| | F1 Score | N/A | | Accuracy | N/A | | Unsafe F1 | N/A | ## Categories - `benign` - `jailbreak` - `S1 Violent Crimes` - `S2 Non-Violent Crimes` - `S4 Child Sexual Exploitation` - `S7 Privacy` - `S10 Hate` - `S11 Self-Harm` - `S12 Sexual Content` - `S14 Code Abuse` ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch tokenizer = AutoTokenizer.from_pretrained("jainsatyam26/bertclassfier") model = AutoModelForSequenceClassification.from_pretrained("jainsatyam26/bertclassfier") def predict(text): inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) with torch.no_grad(): outputs = model(**inputs) probs = torch.softmax(outputs.logits, dim=-1) predicted_id = torch.argmax(probs, dim=-1).item() labels = ['benign', 'jailbreak', 'S1 Violent Crimes', 'S2 Non-Violent Crimes', 'S4 Child Sexual Exploitation', 'S7 Privacy', 'S10 Hate', 'S11 Self-Harm', 'S12 Sexual Content', 'S14 Code Abuse'] return { "prediction": labels[predicted_id], "confidence": probs[0][predicted_id].item(), "all_scores": {labels[i]: probs[0][i].item() for i in range(len(labels))} } # Example result = predict("How to make a bomb?") print(result) ``` ## Training Configuration This model was trained with the following configuration: ```json { "model_name": "microsoft/deberta-v3-large", "dataset_name": "jainsatyam26/guardrail-215k-splits", "max_length": 512, "epochs": 4, "batch_size": 8, "grad_accum": 4, "learning_rate": 1e-05, "weight_decay": 0.01, "warmup_ratio": 0.1, "use_llrd": true, "llrd_alpha": 0.9, "use_multisample_dropout": true, "num_dropout_samples": 5, "dropout_rate": 0.3, "use_label_smoothing": true, "label_smoothing": 0.1, "use_focal_loss": true, "focal_alpha": 0.7, "focal_gamma": 2.0, "use_hard_negative": true, "hard_negative_ratio": 0.3, "num_folds": 3, "optimize_thresholds": true, "output_dir": "./guardrail_model", "checkpoint_steps": 500, "logging_steps": 50, "eval_steps": 500, "hf_repo_id": "jainsatyam26/bertclassfier", "hf_token": "***REDACTED***", "deploy_every_minutes": 30, "deploy_every_steps": 400, "auto_deploy": true, "private_repo": false, "auto_resume": true, "resume_from_hf": true, "use_wandb": true, "wandb_project": "safety-classifier", "fp16": false, "bf16": true, "dataloader_num_workers": 4, "seed": 42 } ``` ## Automatic Deployment This model is automatically deployed every 30 minutes during training with: - ✅ Automatic checkpoint recovery - ✅ Real-time performance monitoring - ✅ Progressive model updates - ✅ Training state persistence --- *Generated automatically during training - 2026-04-29 06:21:01*
