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README.md

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-license: apache-2.0
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+# 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](https://huggingface.co/t5-small)
+- **Fine-tuned Model**: [dlyog/t5-small-finetuned](https://huggingface.co/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](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.
+
+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:
+
+```python
+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.