EmbeddingGemma-300M-LawVault (Chinese Legal RAG)

📖 Model Introduction

EmbeddingGemma-300M-LawVault is a high-performance embedding model fine-tuned specifically for Chinese Legal RAG (Retrieval-Augmented Generation) scenarios.

Fine-tuned on Google's embeddinggemma-300m, this model employs a rigorous contrastive learning approach using MultipleNegativesRankingLoss and MatryoshkaLoss. It was trained on a high-quality dataset of over 60,000 (Query, Positive, Hard Negative) triplets to significantly improve retrieval accuracy for legal statutes, colloquial legal inquiries, and noise resistance compared to the base model.

Note: This model is fine-tuned exclusively on Chinese laws and regulations. Its performance on other languages or non-legal domains has not been evaluated and is not guaranteed.

Key Highlights

• Domain Specialization: Specifically addresses the pain point where general models fail to distinguish between "National Laws" and "Local Regulations/Administrative Rules" with similar wording.
• Anti-Interference: Trained with "Source-aware Hard Negatives"—using the base model's incorrect top retrievals for the same query as hard negatives—enabling the model to precisely filter out confusingly similar but incorrect clauses.
• Colloquial Understanding: The training set includes queries generated by LLMs to simulate real-world user questions, bridging the semantic gap between formal legal terminology and everyday language.
• Matryoshka Embeddings: Supports flexible output vector dimensions (768, 512, 256, 128), allowing for significantly reduced storage costs without major performance loss.

📊 Evaluation Performance

The model was evaluated on a held-out test set constructed from real legal scenarios (containing 120 unseen colloquial legal queries generated by Deepseek V3.2). The End-to-End RAG retrieval results are as follows:

Note: The test environment used a LanceDB vector database covering a full slice of the Chinese laws and regulations database.

Case Study

🚀 Usage

Install Dependencies

pip install -U sentence-transformers

Load Model

from sentence_transformers import SentenceTransformer

# Load model
model_path = "ByronLeeee/EmbeddingGemma-300M-LawVault"
model = SentenceTransformer(model_path, trust_remote_code=True)

# 1. Define Query
query = "What is the penalty for robbery?" # (In Chinese: 抢劫罪一般判几年？)

# 2. Define Documents - Recommended Format: title: {Law Name} | text: {Content}
documents = [
    "title: 中华人民共和国刑法 第二百六十三条 | text: 以暴力、胁迫或者其他方法抢劫公私财物的，处三年以上十年以下有期徒刑，并处罚金...",
    "title: 中华人民共和国刑法 第二百六十七条 | text: 抢夺公私财物，数额较大的，或者多次抢夺的，处三年以下有期徒刑、拘役或者管制...",
    "title: 陕西省专利条例 第二十四条 | text: 负责专利执法的部门...可以查封或者扣押。"
]

# 3. Encode
query_vec = model.encode(query)
doc_vecs = model.encode(documents)

# 4. Compute Similarity
similarities = model.similarity(query_vec, doc_vecs)
print(similarities)

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Training Details (Generated by Trainer)

Dataset

• Size: 65,783 training triplets (Anchor, Positive, Hard Negative)
• Source: Chinese Laws & Regulations (Civil, Criminal, Administrative, etc.)

Training Hyperparameters

• Batch Size: 24 (Effective Batch Size = 144 with Gradient Accumulation)
• Learning Rate: 2e-05
• Epochs: 3
• Precision: bf16 (BFloat16)
• Gradient Accumulation: 6 steps
• Max Sequence Length: 1024 tokens

Loss Function

MatryoshkaLoss wrapping MultipleNegativesRankingLoss:

{
    "matryoshka_dims": [768, 512, 256, 128],
    "matryoshka_weights": [1, 1, 1, 1]
}

Training Logs

Click to expand detailed logs

Framework Versions

• Python: 3.13.1
• Sentence Transformers: 5.1.2
• Transformers: 4.57.1
• PyTorch: 2.9.1+cu130
• Accelerate: 1.12.0
• Datasets: 4.4.1
• Tokenizers: 0.22.1

Citation

If you use this model, please cite the following:

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}