README.md

---
base_model: Qwen/Qwen3-Embedding-8B
library_name: peft
tags:
- legal
- vietnamese
- sentence-transformers
- sentence-similarity
- feature-extraction
- peft
- lo-ra
language:
- vi
pipeline_tag: feature-extraction
---

# Legal-embedding-v1 (Vietnamese Legal Domain)

This model is a parameter-efficient fine-tuned (PEFT) version of **Qwen/Qwen3-Embedding-8B** specifically adapted for the **Vietnamese Legal Domain**. It uses LoRA (Low-Rank Adaptation) to capture the nuances of legal terminology and semantics in Vietnamese statutory documents.

## Model Details

### Model Description

- **Model type:** Large Language Model based Embedding (PEFT/LoRA)
- **Language(s) (NLP):** Vietnamese (vi)
- **Finetuned from model:** Qwen/Qwen3-Embedding-8B
- **Domain:** Law / Legal Systems of Vietnam

### Model Sources

- **Repository:** https://huggingface.co/ngovanphuoc2006/Legal-embedding
- **Base Model Architecture:** Qwen 3 (8B)

## Uses

### Direct Use

- **Semantic Search:** Searching for relevant legal articles based on natural language queries.
- **RAG (Retrieval-Augmented Generation):** Serving as the retrieval component for legal chatbots or AI assistants.
- **Legal Document Clustering:** Grouping similar court cases or regulatory documents.

### Out-of-Scope Use

- General-purpose English text embedding (not optimized).
- Direct text generation (this is an embedding model, not a chat model).

## How to Get Started with the Model

Bạn có thể sử dụng model này với thư viện `transformers` và `peft` theo cấu trúc sau:

```python
from transformers import AutoModel, AutoTokenizer
from peft import PeftModel, PeftConfig
import torch

# Đường dẫn repo
model_id = "ngovanphuoc2006/Legal-embedding"

# Load cấu hình và model
config = PeftConfig.from_pretrained(model_id)
base_model = AutoModel.from_pretrained(config.base_model_name_or_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Merge Adapter
model = PeftModel.from_pretrained(base_model, model_id)

# Ví dụ sử dụng
sentences = ["Quy định về tội giết người", "Các hình phạt đối với hành vi cố ý gây thương tích"]
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    # Lấy embedding từ Last Hidden State (thường là CLS token hoặc mean pooling)
    embeddings = outputs.last_hidden_state[:, 0, :]