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