| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | base_model: |
| | - Qwen/Qwen3-Embedding-8B |
| | --- |
| | ## LEXA-8B |
| |  |
| | 👉 **LEXA-8B**: LEXA: Legal Case Retrieval via Graph Contrastive Learning with Contextualised LLM Embeddings. More information is available in [**arXiv**](https://arxiv.org/abs/2405.11791) & [**GitHub**](https://github.com/yanran-tang/CaseGNN). |
| |
|
| | ## Example Usage |
| |
|
| | ```python |
| | from transformers import AutoModel, AutoTokenizer |
| | model = AutoModel.from_pretrained("AnnaStudy/LEXA-8B", torch_dtype="auto", device_map="auto") |
| | tokenizer = AutoTokenizer.from_pretrained("AnnaStudy/LEXA-8B") |
| | case_txt = "The following contains key components of a legal case. Legal facts..." |
| | tokenized = tokenizer(case_txt, return_tensors='pt', padding=True, truncation=True, max_length=2048) |
| | outputs = model(**tokenized) |
| | case_embedding = outputs.last_hidden_state[:, -1] |
| | ``` |
| | ## Base Model |
| |
|
| | ReaKase-8B is finetuned from **Qwen3-Embedding-8B**, which provides the underlying semantic representation capability. |
| |
|
| | Reference: [Qwen/Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) |
| |
|
| | ## Cite |
| | If you find this repo useful, please cite |
| | ``` |
| | @article |
| | {LEXA-8B, |
| | author = {Yanran Tang, Ruihong Qiu, Xue Li, Zi Huang}, |
| | title = {LEXA: Legal Case Retrieval via Graph Contrastive Learning with Contextualised LLM Embeddings}, |
| | journal = {CoRR}, |
| | volume = {abs/2405.11791}, |
| | year = {2025} |
| | } |
| | ``` |
