update README.md

README.md

@@ -1,3 +1,65 @@
----
-license: apache-2.0
----
+---
+language: en
+license: apache-2.0
+pipeline_tag: text-generation
+base_model: t5-small
+library_name: transformers
+
+widget:
+  - text: "A 35-year-old female presents with a 2-week history of persistent cough..."
+---
+
+# Medical Generation Model
+
+## Overview
+
+This repository contains a fine-tuned T5 model designed to generate medical diagnoses and treatment recommendations. The model was trained on clinical scenarios to provide accurate and contextually relevant medical outputs based on input prompts.
+
+## Model Details
+
+- **Model Type**: T5
+- **Model Size**: small
+- **Tokenizer**: T5 tokenizer
+- **Training Data**: Clinical scenarios and medical texts
+
+## Installation
+
+To use this model, install the required libraries with `pip`:
+
+```bash
+pip install transformers
+pip install tensorflow
+
+# Load the fine-tuned model and tokenizer
+from transformers import T5Tokenizer, TFT5ForConditionalGeneration
+
+model_id = "Ra-Is/medical-gen-small"
+model = TFT5ForConditionalGeneration.from_pretrained(model_id)
+tokenizer = T5Tokenizer.from_pretrained(model_id)
+
+# Prepare a sample input prompt
+input_prompt = ("A 35-year-old female presents with a 2-week history of "
+                "persistent cough, shortness of breath, and fatigue. She has "
+                "a history of asthma and has recently been exposed to a sick "
+                "family member with a respiratory infection. Chest X-ray shows "
+                "bilateral infiltrates. What is the likely diagnosis, and what "
+                "should be the treatment?")
+
+# Tokenize the input
+input_ids = tokenizer(input_prompt, return_tensors="tf").input_ids
+
+# Generate the output (diagnosis)
+outputs = model.generate(
+        input_ids,
+        max_length=512,
+        num_beams=5,
+        temperature=1,
+        top_k=50,
+        top_p=0.9,
+        do_sample=True,  # Enable sampling
+        early_stopping=True
+    )
+
+# Decode and print the output
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)