Sugandha-Chauhan/T5-Medical-Summarizer

T5-Small GenMedX

Model Description

This is a fine-tuned version of t5-small for medical text generation tasks.

Base Model: t5-small (60M parameters)
Fine-tuning Task: Text-to-text generation on medical domain data

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
model_name = "USERNAME/t5-small-genmedx"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Prepare input
input_text = "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True)

# Generate output
outputs = model.generate(
    inputs.input_ids,
    max_length=160,
    num_beams=4,
    early_stopping=True
)

# Decode output
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Training Configuration

• Maximum Source Length: 256 tokens
• Maximum Target Length: 160 tokens
• Batch Size: 4
• Number of Epochs: 15
• Learning Rate: 5e-5

Intended Use

This model is intended for:

• Medical text generation tasks
• Research purposes in the medical NLP domain

Disclaimer

This model is provided as-is for research and educational purposes.