Update README.md

README.md

@@ -3,14 +3,131 @@ license: cc-by-nc-sa-4.0
 language:
 - zh
 pipeline_tag: sentence-similarity
+library_name: transformers
+tags:
+- chinese
+- medical
+- information-retrieval
+- dense-retrieval
+- text-embeddings
+- asymmetric-encoder
+- cmedteb
 ---
 
-@misc{jiang2026cmedtebcarebenchmarking,
-      title={CMedTEB & CARE: Benchmarking and Enabling Efficient Chinese Medical Retrieval via Asymmetric Encoders}, 
-      author={Angqing Jiang and Jianlyu Chen and Zhe Fang and Yongcan Wang and Xinpeng Li and Keyu Ding and Defu Lian},
-      year={2026},
-      eprint={2604.10937},
-      archivePrefix={arXiv},
-      primaryClass={cs.IR},
-      url={https://arxiv.org/abs/2604.10937}, 
-}
+# CARE-0.3B-4B
+
+CARE-0.3B-4B is a complete asymmetric dense retrieval system for Chinese medical text retrieval. It consists of a lightweight 0.3B query encoder and a 4B document encoder.
+
+The query encoder is intended for low-latency online query encoding, while the document encoder is intended for offline document encoding and indexing. The two encoders are trained as a pair and should be used together.
+
+## Model Components
+
+| Component | Size | Recommended usage |
+|---|---:|---|
+| Query encoder | 0.3B | Online query encoding |
+| Document encoder | 4B | Offline document encoding |
+
+This repository/model release represents the paired `0.3B + 4B` CARE system. The 4B document encoder should not be paired with an unrelated query encoder when reproducing the reported results.
+
+## Intended Use
+
+CARE-0.3B-4B is intended for:
+
+- Chinese medical passage retrieval
+- Medical knowledge-base search
+- Retrieval-augmented generation over Chinese medical documents
+- Offline document embedding and vector indexing
+- Research on asymmetric dense retrieval
+
+Typical deployment:
+
+1. Encode the document corpus offline with the 4B document encoder.
+2. Store document embeddings in a vector index.
+3. Encode incoming queries online with the 0.3B query encoder.
+4. Retrieve documents using dot-product or cosine similarity.
+
+## Out-of-Scope Use
+
+This model is not a standalone chatbot, reranker, or medical diagnosis system. Retrieved passages must not be treated as medical advice or as a substitute for clinical judgment.
+
+## Inference
+
+The inference wrapper is provided in [`inference/asymmetric.py`](https://github.com/PhilipGAQ/CARE/blob/main/inference/asymmetric.py).
+
+```python
+from inference.asymmetric import CARE
+import numpy as np
+
+model = CARE(
+    model_name_or_path_query="path/to/CARE-0.3B-query-encoder",
+    model_name_or_path_doc="PhilipGAQ/CARE-0.3B-4B",
+    trust_remote_code=True,
+    use_fp16=False,
+    normalize_embeddings=True,
+    query_batch_size=2,
+    passage_batch_size=2,
+)
+
+queries = ["什么是高血压？"]
+documents = [
+    "高血压是指动脉血压持续升高，通常指收缩压≥140mmHg和/或舒张压≥90mmHg。"
+]
+
+query_embeddings = model.encode_queries(queries, task_name="retrieval")
+document_embeddings = model.encode_corpus(documents, task_name="retrieval")
+
+scores = np.dot(query_embeddings, document_embeddings.T)
+print(scores)
+```
+
+When `normalize_embeddings=True`, embeddings are L2-normalized and dot product is equivalent to cosine similarity. Similarity scores are ranking signals, not calibrated probabilities.
+
+## Training
+
+CARE uses a two-stage asymmetric training strategy:
+
+1. Query-side alignment training with the document encoder fixed.
+2. Joint fine-tuning of the query and document encoders.
+
+This progressively aligns representations produced by the structurally different query-side and document-side encoders.
+
+## Evaluation
+
+The paired CARE system is evaluated on the Chinese Medical Text Embedding Benchmark (CMedTEB), which covers retrieval, reranking, and semantic textual similarity (STS).
+
+Results should be reported for the complete configuration:
+
+`CARE 0.3B query encoder + CARE 4B document encoder`
+
+See the paper for benchmark splits, metrics, baselines, and full results.
+
+## Limitations and Responsible Use
+
+- The model is primarily optimized for Chinese medical text retrieval.
+- Performance may degrade on non-Chinese, non-medical, or highly specialized domains.
+- Retrieval quality depends on chunking, preprocessing, document quality, and indexing strategy.
+- Retrieved information may be incomplete, outdated, duplicated, or clinically inappropriate.
+- The model does not verify clinical correctness and must not be used alone for diagnosis, treatment, medication, triage, or patient-specific risk decisions.
+- Medical applications require qualified human review, source attribution, freshness checks, and appropriate privacy and safety controls.
+
+## Resources
+
+- Code: https://github.com/PhilipGAQ/CARE
+- Benchmark: https://huggingface.co/datasets/PhilipGAQ/CMedTEB
+- Paper: https://arxiv.org/abs/2604.10937
+
+## Citation
+
+```bibtex
+@inproceedings{jiang2026benchmarking,
+  title={Benchmarking and Enabling Efficient Chinese Medical Retrieval via Asymmetric Encoders},
+  author={Jiang, Angqing and Chen, Jianlyu and Wang, Yongcan and Li, Xinpeng and Ding, Keyu and Lian, Defu and others},
+  booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
+  pages={20000--20020},
+  year={2026}
+}
+```
+
+## License
+
+CC-BY-NC-SA-4.0