unnat17/Text-Summarizer

T5 Dialogue Summarizer

A fine-tuned T5-small model for text and dialogue summarization.

Model Details

• Base model: T5-small
• Task: Text summarization
• Framework: PyTorch
• Tokenizer: T5Tokenizer (max_length: 512)
• Decoding: Beam search (num_beams=4, max_length=150, early_stopping=True)

Usage

Using Pipeline

from transformers import pipeline

summarizer = pipeline("summarization", model="unnat17/t5-dialogue-summarizer")
result = summarizer("Your text here...")
print(result[0]["summary_text"])

Direct Loading

from transformers import T5ForConditionalGeneration, T5Tokenizer

model = T5ForConditionalGeneration.from_pretrained("unnat17/t5-dialogue-summarizer")
tokenizer = T5Tokenizer.from_pretrained("unnat17/t5-dialogue-summarizer")

input_text = "summarize: " + "Your text here..."
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
output = model.generate(**inputs, num_beams=4, max_length=150, early_stopping=True)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Web Application

A full-stack web application using this model is available at: github.com/unnat-git/Text-Summarizer