Auto-deploy: Step 400 | F1: N/A

README.md

@@ -0,0 +1,145 @@
+---
+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**: 2026-04-28 12:47:44 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 - 2026-04-28 12:47:44*

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:facfd34b08de2425f8194dc6057f8cf9d11294a2b74802a97ae5485e46387534
+size 870381672

tokenizer_config.json

@@ -0,0 +1,24 @@
+{
+  "add_prefix_space": true,
+  "backend": "tokenizers",
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": [
+    "[PAD]",
+    "[CLS]",
+    "[SEP]"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_id": 3,
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6cc005448bf0c866f37e672ed9ad1c82fc815ae0f114088ffaa1fe7114813fd6
+size 5265

training_config.json

@@ -0,0 +1,43 @@
+{
+  "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
+}

training_state.json

@@ -0,0 +1,6 @@
+{
+  "step": 400,
+  "epoch": 0.0844550013196094,
+  "best_metric": null,
+  "total_flos": 0.0
+}