Ateeqq/Text-Rewriter-Paraphraser

Text Rewriter Paraphraser

This repository contains a fine-tuned text-rewriting model based on the T5-Base with 223M parameters.

Developed by: https://exnrt.com

Key Features:

• Fine-tuned on t5-base: Leverages the power of a pre-trained text-to-text transfer model for effective paraphrasing.
• Large Dataset (430k examples): Trained on a comprehensive dataset combining three open-source sources and cleaned using various techniques for optimal performance.
• High Quality Paraphrases: Generates paraphrases that significantly alter sentence structure while maintaining accuracy and factual correctness.
• Non-AI Detectable: Aims to produce paraphrases that appear natural and indistinguishable from human-written text.

Model Performance:

• Train Loss: 1.0645
• Validation Loss: 0.8761

Getting Started:

T5 model expects a task related prefix: since it is a paraphrasing task, we will add a prefix "paraphraser: "

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("Ateeqq/Text-Rewriter-Paraphraser", token='your_token')
model = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/Text-Rewriter-Paraphraser", token='your_token').to(device)

def generate_title(text):
    input_ids = tokenizer(f'paraphraser: {text}', return_tensors="pt", padding="longest", truncation=True, max_length=64).input_ids.to(device)
    outputs = model.generate(
        input_ids,
        num_beams=4,
        num_beam_groups=4,
        num_return_sequences=4,
        repetition_penalty=10.0,
        diversity_penalty=3.0,
        no_repeat_ngram_size=2,
        temperature=0.8,
        max_length=64
    )
    return tokenizer.batch_decode(outputs, skip_special_tokens=True)

text = 'By leveraging prior model training through transfer learning, fine-tuning can reduce the amount of expensive computing power and labeled data needed to obtain large models tailored to niche use cases and business needs.'
generate_title(text)

Output:

 ['The fine-tuning can reduce the amount of expensive computing power and labeled data required to obtain large models adapted for niche use cases and business needs by using prior model training through transfer learning.',
 'fine-tuning, by utilizing prior model training through transfer learning, can reduce the amount of expensive computing power and labeled data required to obtain large models tailored for niche use cases and business needs.',
 'Fine-tunering by using prior model training through transfer learning can reduce the amount of expensive computing power and labeled data required to obtain large models adapted for niche use cases and business needs.',
 'Using transfer learning to use prior model training, fine-tuning can reduce the amount of expensive computing power and labeled data required for large models that are suitable in niche usage cases or businesses.']

Disclaimer:

• Limited Use: It grants a non-exclusive, non-transferable license to use the this model same as Llama-3. This means you can't freely share it with others or sell the model itself.
• Commercial Use Allowed: You can use the model for commercial purposes, but under the terms of the license agreement.
• Attribution Required: You need to abide by the agreement's terms regarding attribution. It is essential to use the paraphrased text responsibly and ethically, with proper attribution of the original source.

Further Development:

(Mention any ongoing development or areas for future improvement in Discussions.)