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--- |
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license: apache-2.0 |
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base_model: deepseek-ai/deepseek-11m-7b-base |
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tags: |
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- phi-deidentification |
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- healthcare-nlp |
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- medical-text |
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- lora |
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- peft |
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- privacy |
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- ner |
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- safety |
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--- |
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# DeepSeek PHI De-identification Adapter |
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This repository hosts a LoRA adapter fine-tuned for safe detection and redaction of |
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Protected Health Information (PHI) in clinical text. |
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The model is trained on a large synthetic and de-identified corpus derived from |
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MIMIC-III-style clinical notes and is designed to operate as part of a configurable, |
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explainable medical text de-identification pipeline. |
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--- |
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## Model Details |
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- **Developed by:** Iftakhar Khandokar (Marquette University) |
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- **Funded by:** Academic research (EECE Department, Marquette University) |
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- **Shared by:** Iftakhar Khandokar |
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- **Model type:** LoRA adapter (PEFT) |
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- **Base model:** `deepseek-ai/deepseek-11m-7b-base` |
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- **Language:** English (clinical / biomedical NLP) |
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- **License:** Apache 2.0 |
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--- |
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## Intended Use |
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This adapter is intended for: |
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✅ Research on medical data de-identification |
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✅ Benchmarking privacy-preserving NLP pipelines |
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✅ Safety and explainability evaluation for clinical LLM workflows |
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--- |
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## Not Intended For |
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❌ Automated medical diagnosis |
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❌ Direct patient care deployment without regulatory review |
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❌ Generating synthetic patient records for real-world use |
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--- |
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## Loading the Model |
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```python |
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from transformers import AutoModelForCausalLM |
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from peft import PeftModel |
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base = AutoModelForCausalLM.from_pretrained( |
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"deepseek-ai/deepseek-11m-7b-base", trust_remote_code=True |
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) |
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model = PeftModel.from_pretrained( |
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base, |
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"Iftakhar/deepseek-phi-adapter" |
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) |
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