README.md

---
tags:
- transformers
- text-classification
- reranking
- cross-encoder
- vietnamese
- phobert
- rag
- generated_from_trainer
base_model: vinai/phobert-base-v2
pipeline_tag: text-classification
library_name: transformers
metrics:
- accuracy
- f1
model-index:
- name: PhoBERT Cross-Encoder for Reranking
  results:
  - task:
      type: text-classification
      name: Relevance Classification
    dataset:
      name: cross_eval
      type: cross_eval
    metrics:
    - type: accuracy
      value: 0.995473
      name: Accuracy
    - type: f1
      value: 0.990951
      name: F1 Score
---

# PhoBERT Cross-Encoder for Vietnamese Reranking

This model is a cross-encoder fine-tuned from `vinai/phobert-base-v2` for binary relevance classification between a query and a document. 
Unlike bi-encoders, this model jointly encodes (query, context) pairs, enabling high-accuracy reranking in retrieval systems.

## Model Overview

*   **Architecture:** Cross-Encoder (Sequence Classification)
*   **Base Model:** `vinai/phobert-base-v2`
*   **Task:** Binary classification (relevant / not relevant)
*   **Input Format:** `[CLS] query [SEP] context [SEP]`
*   **Max Sequence Length:** 256 tokens

## Intended Use

This model is designed for:
*   Reranking top-k results from a bi-encoder
*   Improving semantic search precision
*   Vietnamese legal QA systems
*   Second-stage ranking in RAG pipelines

## Training Details

### Dataset
**Format:**
*   query
*   context
*   label (0 = irrelevant, 1 = relevant)

### Training Configuration
*   **Epochs:** 5
*   **Learning rate:** 2e-5
*   **Batch size:** 
    *   Train: 16
    *   Eval: 32
*   **Warmup:** 0.1
*   **Weight decay:** 0.01
*   **Mixed precision:** FP16 (if GPU available)

## Evaluation Results

| Epoch | Validation Loss | Accuracy | F1 Score |
| :---: | :---: | :---: | :---: |
| 1 | 0.0820 | 0.9934 | 0.9869 |
| 2 | 0.0675 | 0.9936 | 0.9871 |
| 3 | 0.0793 | 0.9934 | 0.9869 |
| 4 | 0.0572 | 0.9955 | 0.9910 |
| 5 | 0.0711 | 0.9955 | 0.9910 |

*Best model selected based on F1 score = 0.9909*

## Model Architecture

PhoBERT (RoBERTa-based encoder) -> Classification Head (dense + output layer)

## Usage

### Load model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "HiImHa/phobert-cross-encoder"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
```

### Inference Example
```python
query = "Tôi lái xe không giữ khoảng cách an toàn thì bị phạt như thế nào?"
context = "Phạt tiền từ 2.000.000 đến 3.000.000 đồng nếu không giữ khoảng cách an toàn."

inputs = tokenizer(
    query,
    context,
    return_tensors="pt",
    truncation="only_second",
    max_length=256
)

outputs = model(**inputs)
score = outputs.logits.softmax(dim=-1)[0][1].item()

print(score)  # relevance score
```

## How to Use in RAG

Typical pipeline:
1.  Use bi-encoder -> retrieve top-k documents
2.  Use this cross-encoder -> rerank candidates
3.  Select top results for downstream tasks

## Notes on Initialization

*   Classification head was randomly initialized and trained during fine-tuning
*   Some PhoBERT pretraining weights (e.g., `lm_head`) are unused -> expected behavior
*   LayerNorm naming differences (beta/gamma vs weight/bias) are automatically handled

## Limitations

*   Slower than bi-encoder (pairwise inference)
*   Limited to 256 tokens -> long contexts are truncated
*   Binary classification may not capture nuanced ranking differences

## Future Improvements

*   Pairwise / listwise ranking loss
*   Hard negative mining
*   Knowledge distillation from cross -> bi encoder
*   Larger and more diverse dataset

## Training Configuration (Summary)

*   **Epochs:** 5
*   **Learning rate:** 2e-5
*   **Loss:** Cross-entropy
*   **Metric:** F1 (primary)

## Acknowledgements

*   PhoBERT by VinAI
*   Hugging Face Transformers


## Citation
```bibtex
@inproceedings{reimers-2019-sentence-bert,
  title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
  author={Reimers, Nils and Gurevych, Iryna},
  year={2019}
}
```