# Sentinel-D DistilBERT Intent Classifier v1

## Model Details
- **Base Model**: distilbert-base-uncased
- **Task**: Sequence Classification (4 classes)
- **Training Date**: 2026-03-04T03:36:30.927788
- **Classes**: VERSION_PIN, API_MIGRATION, MONKEY_PATCH, FULL_REFACTOR

## Intent Classes
1. **VERSION_PIN**: Pinning or locking dependency versions
2. **API_MIGRATION**: Migrating between API versions or protocols
3. **MONKEY_PATCH**: Quick/temporary fixes via runtime patching
4. **FULL_REFACTOR**: Complete rewriting or structural redesign

## Usage

```python
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch
import json

tokenizer = DistilBertTokenizer.from_pretrained("./distilbert-intent-classifier-v1")
model = DistilBertForSequenceClassification.from_pretrained("./distilbert-intent-classifier-v1")

text = "I updated my package.json to lock the Express version to 4.18.0"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_id = logits.argmax(-1).item()

# Map ID back to label
label_config = json.load(open("label_config.json"))
predicted_label = label_config["id_to_label"][str(predicted_class_id)]
print(f"Predicted Intent: {predicted_label}")
```

## Files
- `pytorch_model.bin`: Fine-tuned model weights
- `config.json`: Model configuration
- `vocab.txt`: Tokenizer vocabulary
- `label_config.json`: Intent class mappings
- `README.md`: This file

## Training Configuration
- Epochs: 6
- Batch Size: 16
- Learning Rate: Dynamic tuning from 1e-05 to 5e-05
- Optimizer: AdamW with weighted cross-entropy loss
- Class Imbalance Handling: Random oversampling + weighted loss

## Performance Targets
- Accuracy: >= 0.80
- Macro F1: >= 0.80