Pegasus_paraphrase Open-Source Text Paraphrasing Model - Freely Generate Sentences with the Same Semantics but Different Expressions

Pegasus Paraphrase

Developed by tuner007

A text paraphrasing model fine-tuned based on the PEGASUS architecture, capable of generating sentences with the same meaning but different expressions.

Text Generation

Transformers

EnglishOpen Source License:Apache-2.0 #Text Paraphrasing #Multi-sentence Generation #High Fluency

Downloads 209.03k

Release Time : 3/2/2022

Model Overview

This model is specifically designed for text paraphrasing tasks, capable of converting input text into multiple sentences with the same meaning but different expressions, suitable for content rewriting, data augmentation, and other scenarios.

Model Features

Diverse Paraphrasing

Capable of generating multiple different paraphrased versions for the same input.

Semantic Preservation

Maintains the original semantics while changing the expression.

High-Quality Output

Generates fluent and natural paraphrased text that adheres to grammatical rules.

Model Capabilities

Text Paraphrasing

Content Rewriting

Data Augmentation

Use Cases

Content Creation

Article Rewriting

Generates multiple expression versions for existing articles.

Enhances content diversity and avoids repetition.

Education

Learning Assistance

Provides multiple expressions for learning materials.

Helps students understand concepts from different perspectives.

🚀 Pegasus Paraphrasing Model

A fine - tuned PEGASUS model for paraphrasing tasks, offering high - quality text rephrasing capabilities.

🚀 Quick Start

This model is a fine - tuned version of PEGASUS designed for paraphrasing.

💻 Usage Examples

Basic Usage

import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
model_name = 'tuner007/pegasus_paraphrase'
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)

def get_response(input_text,num_return_sequences,num_beams):
  batch = tokenizer([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
  translated = model.generate(**batch,max_length=60,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
  return tgt_text

Advanced Usage

num_beams = 10
num_return_sequences = 10
context = "The ultimate test of your knowledge is your capacity to convey it to another."
get_response(context,num_return_sequences,num_beams)
# output:
['The test of your knowledge is your ability to convey it.',
 'The ability to convey your knowledge is the ultimate test of your knowledge.',
 'The ability to convey your knowledge is the most important test of your knowledge.',
 'Your capacity to convey your knowledge is the ultimate test of it.',
 'The test of your knowledge is your ability to communicate it.',
 'Your capacity to convey your knowledge is the ultimate test of your knowledge.',
 'Your capacity to convey your knowledge to another is the ultimate test of your knowledge.',
 'Your capacity to convey your knowledge is the most important test of your knowledge.',
 'The test of your knowledge is how well you can convey it.',
 'Your capacity to convey your knowledge is the ultimate test.']

📄 License

This project is licensed under the Apache 2.0 license.

Created by Arpit Rajauria

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