This model is a fine-tuned version of distilbert/distilgpt2 on the tsilva/clinical-field-mappings 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.