README.md

---
license: apache-2.0
tags:
- cross-encoder
- reranker
- code
- retrieval
- typescript
- python
pipeline_tag: text-classification
---

# Code Reranker MiniLM v1

A fine-tuned cross-encoder reranker for code relevance ranking, trained on TypeScript/Python codebases.

## Model Details

- **Base model**: [cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2)
- **Training data**: 19,810 (query, code snippet) pairs from a real-world TypeScript/Python codebase
- **Training**: 2 epochs, batch size 8, lr 2e-5
- **Hardware**: GTX 1660 SUPER (6GB VRAM), CPU training

## Benchmarks

| Metric | Original | Fine-tuned |
|--------|----------|------------|
| Accuracy | 88.4% | **98.0%** |
| Best Accuracy | 96.2% | **99.0%** |
| AUC | 98.64% | **99.95%** |

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("trd92/code-reranker-minilm-v1")
model = AutoModelForSequenceClassification.from_pretrained("trd92/code-reranker-minilm-v1")

query = "How to define configuration?"
code = "def define_config(): ..."

inputs = tokenizer(query, code, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
    score = outputs.logits.item()
print(f"Relevance score: {score:.4f}")
```

## Training

Trained using contrastive learning on code triplets (query, positive_code, negative_code) extracted via AST parsing.