Improve language tag

README.md

@@ -1,98 +1,109 @@
----
-license: apache-2.0
-language:
-- en
-- zh
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
-tags:
-- medical
-- cancer
-- Onco
----
-# OncoCareBrain-GPT
-
-## Model Description
-
-OncoCareBrain-GPT is a specialized large language model fine-tuned for oncology applications. Built upon the powerful Qwen2.5-3B foundation model, it has undergone supervised fine-tuning (SFT) with tens of thousands of multi-omics data samples, including genomic, pathological, and clinical data. This model is specifically designed to serve the cancer care domain with advanced reasoning capabilities.
-
-## Key Features
-
-- **Intelligent Medical Q&A**: Quickly answers complex questions about cancer, leveraging a deep understanding of oncology concepts
-- **Precision Decision Support**: Recommends optimal treatment plans based on multi-dimensional data analysis
-- **Transparent Reasoning Process**: Generates detailed chains of thought to ensure model explainability and trust in clinical settings
-
-## Intended Uses
-
-- **Clinical Decision Support**: Assists healthcare providers in evaluating treatment options
-- **Patient Education**: Helps patients better understand their condition and treatment plans
-- **Medical Research**: Supports researchers in analyzing cancer data and generating insights
-
-## Training Data
-
-OncoCareBrain-GPT was fine-tuned on a diverse dataset comprising:
-- Genomic data
-- Pathological samples
-- Clinical records and case studies
-
-The model was trained to generate detailed reasoning chains, provide personalized prognostic assessments, and suggest evidence-based treatment recommendations.
-
-## Technical Specifications
-
-- **Base Model**: Qwen2.5-3B
-- **Parameters**: 3 billion
-- **Training Method**: Supervised Fine-Tuning (SFT)
-- **Language Capabilities**: English, Chinese
-- **Input Format**: Natural language
-- **Output Format**: Detailed explanations with chain-of-thought reasoning
-
-## Limitations
-
-- The model should be used as a clinical decision support tool and not as a replacement for professional medical judgment
-- Recommendations should be verified by qualified healthcare professionals
-- Performance may vary depending on the complexity and rarity of cancer cases
-- While the model supports English and Chinese, performance might vary between languages
-
-## Ethical Considerations
-
-- **Privacy**: The model operates on input data and does not store patient information
-- **Bias**: While efforts have been made to minimize biases, users should be aware of potential biases in training data
-- **Transparency**: The model provides reasoning chains to ensure transparency in its decision-making process
-
-## How to Use
-
-```python
-# Example code for model inference
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("DXCLab/OncoCareBrain-GPT")
-model = AutoModelForCausalLM.from_pretrained("DXCLab/OncoCareBrain-GPT")
-
-input_text = "Could you analyze this genomic profile and suggest potential treatment options for breast cancer with BRCA1 mutation?"
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs, max_length=1000)
-response = tokenizer.decode(outputs[0])
-print(response)
-```
-
-## Citation
-
-If you use OncoCareBrain-GPT in your research, please cite:
-
-```
-@misc{OncoCareBrain-GPT,
-  author = {DXCLab},
-  title = {OncoCareBrain-GPT: A Specialized Language Model for Oncology},
-  year = {2025},
-  publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/DXCLab/OncoCareBrain-GPT}}
-}
-```
-
-## License
-
-This model is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.
-
-## Contact
-
+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+tags:
+- medical
+- cancer
+- Onco
+---
+# OncoCareBrain-GPT
+
+## Model Description
+
+OncoCareBrain-GPT is a specialized large language model fine-tuned for oncology applications. Built upon the powerful Qwen2.5-3B foundation model, it has undergone supervised fine-tuning (SFT) with tens of thousands of multi-omics data samples, including genomic, pathological, and clinical data. This model is specifically designed to serve the cancer care domain with advanced reasoning capabilities.
+
+## Key Features
+
+- **Intelligent Medical Q&A**: Quickly answers complex questions about cancer, leveraging a deep understanding of oncology concepts
+- **Precision Decision Support**: Recommends optimal treatment plans based on multi-dimensional data analysis
+- **Transparent Reasoning Process**: Generates detailed chains of thought to ensure model explainability and trust in clinical settings
+
+## Intended Uses
+
+- **Clinical Decision Support**: Assists healthcare providers in evaluating treatment options
+- **Patient Education**: Helps patients better understand their condition and treatment plans
+- **Medical Research**: Supports researchers in analyzing cancer data and generating insights
+
+## Training Data
+
+OncoCareBrain-GPT was fine-tuned on a diverse dataset comprising:
+- Genomic data
+- Pathological samples
+- Clinical records and case studies
+
+The model was trained to generate detailed reasoning chains, provide personalized prognostic assessments, and suggest evidence-based treatment recommendations.
+
+## Technical Specifications
+
+- **Base Model**: Qwen2.5-3B
+- **Parameters**: 3 billion
+- **Training Method**: Supervised Fine-Tuning (SFT)
+- **Language Capabilities**: English, Chinese
+- **Input Format**: Natural language
+- **Output Format**: Detailed explanations with chain-of-thought reasoning
+
+## Limitations
+
+- The model should be used as a clinical decision support tool and not as a replacement for professional medical judgment
+- Recommendations should be verified by qualified healthcare professionals
+- Performance may vary depending on the complexity and rarity of cancer cases
+- While the model supports English and Chinese, performance might vary between languages
+
+## Ethical Considerations
+
+- **Privacy**: The model operates on input data and does not store patient information
+- **Bias**: While efforts have been made to minimize biases, users should be aware of potential biases in training data
+- **Transparency**: The model provides reasoning chains to ensure transparency in its decision-making process
+
+## How to Use
+
+```python
+# Example code for model inference
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("DXCLab/OncoCareBrain-GPT")
+model = AutoModelForCausalLM.from_pretrained("DXCLab/OncoCareBrain-GPT")
+
+input_text = "Could you analyze this genomic profile and suggest potential treatment options for breast cancer with BRCA1 mutation?"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=1000)
+response = tokenizer.decode(outputs[0])
+print(response)
+```
+
+## Citation
+
+If you use OncoCareBrain-GPT in your research, please cite:
+
+```
+@misc{OncoCareBrain-GPT,
+  author = {DXCLab},
+  title = {OncoCareBrain-GPT: A Specialized Language Model for Oncology},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/DXCLab/OncoCareBrain-GPT}}
+}
+```
+
+## License
+
+This model is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.
+
+## Contact
+
 For questions or feedback about OncoCareBrain-GPT, please visit our Hugging Face page at https://huggingface.co/DXCLab or open an issue in the repository.