Update README.md

README.md

@@ -1,3 +1,54 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- zh
+- en
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+- meta-llama/Llama-3.1-8B-Instruct
+pipeline_tag: feature-extraction
+tags:
+- structuring
+- EHR
+- medical
+- IE
+---
+# Model Card for GENIE
+
+
+## Model Details
+
+Model Size: 8B (English) / 7B (Chinese)
+
+Max Tokens: 8192
+
+Base model: Llama 3.1 8B (English) / Qwen 2.5 7B (Chinese)
+
+### Model Description
+
+GENIE (Generative Note Information Extraction) is an end-to-end model for structuring EHR data.
+GENIE can process an entire paragraph of clinical notes in a single pass, outputting structured information on named entities, assertion statuses, locations, other relevant modifiers, clinical values, and intended purposes.
+This end-to-end approach simplifies the structuring process, reduces errors, and enables healthcare providers to derive structured data from EHRs more efficiently, without the need for extensive manual adjustments.
+And experiments have shown that GENIE achieves high accuracy in each of the task.
+
+
+## Usage
+
+```python
+from vllm import LLM, SamplingParams
+
+PROMPT_TEMPLATE = "Human:\n{query}\n\n Assistant:"
+sampling_params = SamplingParams(temperature=temperature, max_tokens=max_new_token)
+EHR = ['xxxxx1','xxxxx2']
+texts = [PROMPT_TEMPLATE.format(query=k) for k in EHR]
+output = model.generate(texts, sampling_params)
+```
+
+
+## Citation [optional]
+
+If you find our paper or models helpful, please consider cite: (to be released)
+
+**BibTeX:**
+
+[More Information Needed]