Improve language tag

README.md

@@ -1,115 +1,127 @@
----
-license: apache-2.0
-language:
-- zh
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: feature-extraction
-tags:
-- structuring
-- EHR
-- medical
-- IE
----
-# Model Card for GENIE
-
-
-## Model Details
-
-Model Size: 7B 
-
-Max Tokens: 8192
-
-Base model: Qwen 2.5 7B 
-
-### Model Description
-
-GENIE (Generative Note Information Extraction, 中文名：病历精灵) is an end-to-end model designed to structure free text from electronic health records (EHRs). It processes EHRs in a single pass, extracting biomedical named entities along with their assertion statuses, body locations, modifiers, values, units, and intended purposes, outputting this information in a structured JSON format. This streamlined approach simplifies traditional natural language processing workflows by replacing all the analysis components with a single model, making the system easier to maintain while leveraging the advanced analytical capabilities of large language models (LLMs). Comparing with general-purpose LLMs, GENIE does not require prompt engineering or few-shot examples. Additionally, it generates all relevant attributes in one pass, significantly reducing both runtime and operational costs.
-GENIE is co-developed by the groups of Sheng Yu (https://www.stat.tsinghua.edu.cn/teachers/shengyu/), Tianxi Cai (https://dbmi.hms.harvard.edu/people/tianxi-cai), and Isaac Kohane (https://dbmi.hms.harvard.edu/people/isaac-kohane).
-
-
-## Usage
-
-```python
-from vllm import LLM, SamplingParams
-
-PROMPT_TEMPLATE = "Human:\n{query}\n\n Assistant:\n"
-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)
-```
-
-# An example
-
-Input:
-```python
-EHR = ['慢性乙型肝炎病史10余年，曾有肝功能异常，中医治疗后好转；1年余前查HBsAg转阴，但肝脏病理提示病毒性肝炎伴肝纤维化（G1S3-4）']
-```
-
-Output:
-```python
-res = [
- { "术语": "慢性乙型肝炎",
- "语义类型": "疾病、综合征、病理功能",
- "叙述状态": "存在",
- "身体部位": "无",
- "数值": "NA",
- "单位": "NA",
- "修饰词": "无" },
- { "术语": "肝功能异常",
- "语义类型": "症状、体征、临床所见",
- "叙述状态": "存在",
- "身体部位": "无",
- "数值": "NA",
- "单位": "NA",
- "修饰词": "无" },
- { "术语": "HBsAg",
- "语义类型": "化学物质、药物",
- "叙述状态": "不存在",
- "身体部位": "NA",
- "数值": "无",
- "单位": "NA",
- "修饰词": "NA" },
-{ "术语": "肝脏病理",
- "语义类型": "诊断操作",
- "叙述状态": "存在",
- "身体部位": "无",
- "数值": "无",
- "单位": "NA",
- "修饰词": "NA" },
- { "术语": "病毒性肝炎",
- "语义类型": "疾病、综合征、病理功能",
- "叙述状态": "存在",
- "身体部位": "无",
- "数值": "NA",
- "单位": "NA",
- "修饰词": "无" },
- { "术语": "肝纤维化",
- "语义类型": "疾病、综合征、病理功能",
- "叙述状态": "存在",
- "身体部位": "无",
- "数值": "NA",
- "单位": "NA",
- "修饰词": "无" },
-]
-```
-
-
-
-## Citation
-
-If you find our paper or models helpful, please consider cite: 
-
-**BibTeX:**
-```
-@misc{ying2025geniegenerativenoteinformation,
-      title={GENIE: Generative Note Information Extraction model for structuring EHR data}, 
-      author={Huaiyuan Ying and Hongyi Yuan and Jinsen Lu and Zitian Qu and Yang Zhao and Zhengyun Zhao and Isaac Kohane and Tianxi Cai and Sheng Yu},
-      year={2025},
-      eprint={2501.18435},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2501.18435}, 
-}
+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: feature-extraction
+tags:
+- structuring
+- EHR
+- medical
+- IE
+---
+# Model Card for GENIE
+
+
+## Model Details
+
+Model Size: 7B 
+
+Max Tokens: 8192
+
+Base model: Qwen 2.5 7B 
+
+### Model Description
+
+GENIE (Generative Note Information Extraction, 中文名：病历精灵) is an end-to-end model designed to structure free text from electronic health records (EHRs). It processes EHRs in a single pass, extracting biomedical named entities along with their assertion statuses, body locations, modifiers, values, units, and intended purposes, outputting this information in a structured JSON format. This streamlined approach simplifies traditional natural language processing workflows by replacing all the analysis components with a single model, making the system easier to maintain while leveraging the advanced analytical capabilities of large language models (LLMs). Comparing with general-purpose LLMs, GENIE does not require prompt engineering or few-shot examples. Additionally, it generates all relevant attributes in one pass, significantly reducing both runtime and operational costs.
+GENIE is co-developed by the groups of Sheng Yu (https://www.stat.tsinghua.edu.cn/teachers/shengyu/), Tianxi Cai (https://dbmi.hms.harvard.edu/people/tianxi-cai), and Isaac Kohane (https://dbmi.hms.harvard.edu/people/isaac-kohane).
+
+
+## Usage
+
+```python
+from vllm import LLM, SamplingParams
+
+PROMPT_TEMPLATE = "Human:\n{query}\n\n Assistant:\n"
+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)
+```
+
+# An example
+
+Input:
+```python
+EHR = ['慢性乙型肝炎病史10余年，曾有肝功能异常，中医治疗后好转；1年余前查HBsAg转阴，但肝脏病理提示病毒性肝炎伴肝纤维化（G1S3-4）']
+```
+
+Output:
+```python
+res = [
+ { "术语": "慢性乙型肝炎",
+ "语义类型": "疾病、综合征、病理功能",
+ "叙述状态": "存在",
+ "身体部位": "无",
+ "数值": "NA",
+ "单位": "NA",
+ "修饰词": "无" },
+ { "术语": "肝功能异常",
+ "语义类型": "症状、体征、临床所见",
+ "叙述状态": "存在",
+ "身体部位": "无",
+ "数值": "NA",
+ "单位": "NA",
+ "修饰词": "无" },
+ { "术语": "HBsAg",
+ "语义类型": "化学物质、药物",
+ "叙述状态": "不存在",
+ "身体部位": "NA",
+ "数值": "无",
+ "单位": "NA",
+ "修饰词": "NA" },
+{ "术语": "肝脏病理",
+ "语义类型": "诊断操作",
+ "叙述状态": "存在",
+ "身体部位": "无",
+ "数值": "无",
+ "单位": "NA",
+ "修饰词": "NA" },
+ { "术语": "病毒性肝炎",
+ "语义类型": "疾病、综合征、病理功能",
+ "叙述状态": "存在",
+ "身体部位": "无",
+ "数值": "NA",
+ "单位": "NA",
+ "修饰词": "无" },
+ { "术语": "肝纤维化",
+ "语义类型": "疾病、综合征、病理功能",
+ "叙述状态": "存在",
+ "身体部位": "无",
+ "数值": "NA",
+ "单位": "NA",
+ "修饰词": "无" },
+]
+```
+
+
+
+## Citation
+
+If you find our paper or models helpful, please consider cite: 
+
+**BibTeX:**
+```
+@misc{ying2025geniegenerativenoteinformation,
+      title={GENIE: Generative Note Information Extraction model for structuring EHR data}, 
+      author={Huaiyuan Ying and Hongyi Yuan and Jinsen Lu and Zitian Qu and Yang Zhao and Zhengyun Zhao and Isaac Kohane and Tianxi Cai and Sheng Yu},
+      year={2025},
+      eprint={2501.18435},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.18435}, 
+}
 ```