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--- |
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language: |
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- de |
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- en |
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license: apache-2.0 |
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tags: |
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- medical |
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- loinc |
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- terminology-mapping |
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- llama-3 |
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- unsloth |
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base_model: unsloth/Llama-3.2-3B-Instruct |
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datasets: |
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- custom-loinc-dataset |
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metrics: |
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- accuracy |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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# LOINC Medical Terminology Mapper |
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Fine-tuned Llama-3.2-3B model for mapping German medical terms to LOINC codes using Chain-of-Thought reasoning. |
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## Model Details |
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- **Base Model:** unsloth/Llama-3.2-3B-Instruct |
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- **Fine-tuning Method:** LoRA (Low-Rank Adaptation) |
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- **Training Framework:** Unsloth + Hugging Face Transformers |
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- **Language:** German (primary), English (secondary) |
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- **Task:** Medical terminology to LOINC code mapping |
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## Performance |
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- **Accuracy:** 0.00% |
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- **Total Samples:** 0 |
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- **Correct Predictions:** 0 |
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## Training Configuration |
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- **LoRA Rank:** 64 |
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- **LoRA Alpha:** 128 |
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- **Learning Rate:** 0.0002 |
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- **Batch Size:** 64 |
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- **Epochs:** 1 |
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- **Precision:** BF16 |
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## Usage |
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```python |
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from unsloth import FastLanguageModel |
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# Load model |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name="Franc105/loinc-mapper", |
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max_seq_length=2048, |
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dtype=None, |
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load_in_4bit=True, |
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) |
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# Enable inference mode |
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FastLanguageModel.for_inference(model) |
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# Format input |
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messages = [ |
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{"role": "system", "content": "Du bist ein Experte für medizinische Terminologie und LOINC-Mapping."}, |
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{"role": "user", "content": "Begriff: Glukose\nEinheit: mg/dL\nBeschreibung: Blutzucker"} |
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] |
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inputs = tokenizer.apply_chat_template( |
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messages, |
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tokenize=True, |
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add_generation_prompt=True, |
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return_tensors="pt" |
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).to("cuda") |
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# Generate |
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outputs = model.generate( |
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input_ids=inputs, |
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max_new_tokens=512, |
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temperature=0.1, |
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top_p=0.9, |
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do_sample=True |
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) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Intended Use |
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This model is designed for: |
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- Mapping German medical terminology to standardized LOINC codes |
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- Supporting clinical documentation systems |
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- Assisting healthcare professionals with terminology standardization |
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## Limitations |
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- Primarily trained on German medical terms |
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- Requires structured input format (Begriff, Einheit, Beschreibung) |
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- May not cover all edge cases in medical terminology |
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## Training Data |
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- Custom dataset of German LOINC mappings |
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- Augmented with synonyms from RELATEDNAMES2 |
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- Chain-of-Thought reasoning examples |
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## Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{loinc-mapper-2024, |
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title={LOINC Medical Terminology Mapper}, |
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author={IMESO IT GmbH}, |
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year={2024}, |
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publisher={Hugging Face}, |
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howpublished={\url{Franc105/loinc-mapper}} |
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} |
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``` |
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## License |
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Apache 2.0 |
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## Contact |
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For questions or issues, please open an issue on the model repository. |
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