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README.md CHANGED
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # T5-Small Fine-tuned for Clinical Summarization of FHIR Document Reference Clinical Notes
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+
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+ 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.
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+
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+ ## Model Details
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+
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+ - **Original Model**: [T5-Small](https://huggingface.co/t5-small)
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+ - **Fine-tuned Model**: [dlyog/t5-small-finetuned](https://huggingface.co/dlyog/t5-small-finetuned/)
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+ - **License**: Apache-2.0 (same as the original T5 license)
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+
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+ ## Fine-tuning Process
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+
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+ The model was fine-tuned using a synthetic dataset created with tools like [Synthea](https://synthetichealth.github.io/synthea/). This dataset was used to simulate real-world clinical notes, ensuring the model understands the nuances and intricacies of medical terminology and context.
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+
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+ 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.
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+
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+ ## Usage
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+
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+ Using the model is straightforward with the Hugging Face Transformers library:
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+
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+ ```python
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+ from transformers import T5ForConditionalGeneration, T5Tokenizer
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+
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+ model = T5ForConditionalGeneration.from_pretrained("dlyog/t5-small-finetuned")
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+ tokenizer = T5Tokenizer.from_pretrained("dlyog/t5-small-finetuned")
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+
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+ def summarize(text):
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+ input_text = "summarize: " + text
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+ input_ids = tokenizer.encode(input_text, return_tensors="pt")
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+ summary_ids = model.generate(input_ids)
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+ summary = tokenizer.decode(summary_ids[0])
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+ return summary
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+
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+ # Example
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+ text = "Your clinical note here..."
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+ print(summarize(text))
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+
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+ # Acknowledgements
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+ 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.
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+
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+ # License
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+ This model is licensed under the Apache-2.0 License, the same as the original T5 model.
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