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--- |
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library_name: transformers |
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license: apache-2.0 |
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tags: |
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- healthcare |
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- column-normalization |
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- text-classification |
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- distilgpt2 |
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model-index: |
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- name: tsilva/clinical-field-mapper-classification |
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results: |
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- task: |
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name: Field Classification |
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type: text-classification |
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dataset: |
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name: tsilva/clinical-field-mappings |
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type: healthcare |
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metrics: |
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- name: train Accuracy |
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type: accuracy |
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value: 0.9471 |
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- name: validation Accuracy |
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type: accuracy |
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value: 0.9144 |
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- name: test Accuracy |
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type: accuracy |
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value: 0.9156 |
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--- |
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# Model Card for tsilva/clinical-field-mapper-classification |
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This model is a fine-tuned version of `distilbert/distilgpt2` on the [`tsilva/clinical-field-mappings`](https://huggingface.co/datasets/tsilva/clinical-field-mappings/tree/4d4cdba1b7e9b1eff2893c7014cfc08fe58a73bc) dataset. |
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Its purpose is to normalize healthcare database column names to a standardized set of target column names. |
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## Task |
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This model is a sequence classification model that maps free-text field names to a set of standardized schema terms. |
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## Usage |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("tsilva/clinical-field-mapper-classification") |
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model = AutoModelForSequenceClassification.from_pretrained("tsilva/clinical-field-mapper-classification") |
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def predict(input_text): |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model(**inputs) |
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pred = outputs.logits.argmax(-1).item() |
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label = model.config.id2label[str(pred)] if hasattr(model.config, 'id2label') else pred |
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print(f"Predicted label: family_history_reported") |
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predict('cardi@') |
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## Evaluation Results |
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- **train accuracy**: 94.71% |
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- **validation accuracy**: 91.44% |
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- **test accuracy**: 91.56% |
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## Training Details |
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- **Seed**: 42 |
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- **Epochs scheduled**: 50 |
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- **Epochs completed**: 34 |
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- **Early stopping triggered**: Yes |
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- **Final training loss**: 1.0888 |
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- **Final evaluation loss**: 0.9916 |
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- **Optimizer**: adamw_bnb_8bit |
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- **Learning rate**: 0.0005 |
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- **Batch size**: 1024 |
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- **Precision**: fp16 |
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- **DeepSpeed enabled**: True |
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- **Gradient accumulation steps**: 1 |
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## License |
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Specify your license here (e.g., Apache 2.0, MIT, etc.) |
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## Limitations and Bias |
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- Model was trained on a specific clinical mapping dataset. |
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- Performance may vary on out-of-distribution column names. |
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- Ensure you validate model outputs in production environments. |
