T5-Small Fine-tuned for Clinical Summarization of FHIR Document Reference Clinical Notes

This model is a fine-tuned version of the t5-small model from Hugging Face, specifically tailored for the clinical summarization of FHIR Document Reference Clinical Notes.

Model Details

Original Model: T5-Small
Fine-tuned Model: dlyog/t5-small-finetuned
License: Apache-2.0 (same as the original T5 license)

Fine-tuning Process

The model was fine-tuned using a synthetic dataset created with tools like Synthea. This dataset was used to simulate real-world clinical notes, ensuring the model understands the nuances and intricacies of medical terminology and context.

Only the last two layers of the t5-small model were fine-tuned to retain most of the pre-trained knowledge while adapting it for better clinical summarization.

Usage

Using the model is straightforward with the Hugging Face Transformers library:

from transformers import T5ForConditionalGeneration, T5Tokenizer

model = T5ForConditionalGeneration.from_pretrained("dlyog/t5-small-finetuned")
tokenizer = T5Tokenizer.from_pretrained("dlyog/t5-small-finetuned")

def summarize(text):
    input_text = "summarize: " + text
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    summary_ids = model.generate(input_ids)
    summary = tokenizer.decode(summary_ids[0])
    return summary

# Example
text = "Your clinical note here..."
print(summarize(text))

# Acknowledgements
A big thanks to the creators of the original t5-small model and the Hugging Face community. Also, gratitude to tools like Synthea that enabled the creation of high-quality synthetic datasets for fine-tuning purposes.

# License
This model is licensed under the Apache-2.0 License, the same as the original T5 model.