ailm/pegsus-text-summarization

Pegasus-based Text Summarization Model

Model Name: pegsus-text-summarization

Model Description

This model is a fine-tuned version of the Pegasus model, specifically adapted for the task of text summarization. It is trained on the SAMSum dataset, which is designed for summarizing conversations.

Usage

This model can be used to generate concise summaries of input text, particularly for conversational text or dialogue-based inputs.

How to Use

You can use this model with the Hugging Face transformers library. Below is an example code snippet:

from transformers import PegasusForConditionalGeneration, PegasusTokenizer

# Load the pre-trained model and tokenizer
model_name = "ailm/pegsus-text-summarization"
model = PegasusForConditionalGeneration.from_pretrained(model_name)
tokenizer = PegasusTokenizer.from_pretrained(model_name)

# Define the input text
text = "Your input text here"

# Tokenize the input text
tokens = tokenizer(text, truncation=True, padding="longest", return_tensors="pt")

# Generate the summary
summary = model.generate(**tokens)

# Decode and print the summary
print(tokenizer.decode(summary[0], skip_special_tokens=True))