metadata

license: llama3
inference:
  parameters:
    num_beams: 3
    num_beam_groups: 3
    num_return_sequences: 1
    repetition_penalty: 10
    diversity_penalty: 3.01
    no_repeat_ngram_size: 2
    temperature: 0.8
    max_length: 128
widget:
  - text: >-
      Learn to build generative AI applications with an expert AWS instructor
      with the 2-day Developing Generative AI Applications on AWS course.
    example_title: AWS course
  - text: >-
      In healthcare, Generative AI can help generate synthetic medical data to
      train machine learning models, develop new drug candidates, and design
      clinical trials.
    example_title: Generative AI
  - 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.
    example_title: Fine Tuning

Text Rewriter Paraphraser

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

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:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Replace 'YOUR_TOKEN' with your actual Hugging Face access token
tokenizer = AutoTokenizer.from_pretrained("Ateeqq/Text-Rewriter-Paraphraser", token='YOUR_TOKEN')
model = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/Text-Rewriter-Paraphraser", token='YOUR_TOKEN')

text = "Data science is a field that deals with extracting knowledge and insights from data. "

inputs = tokenizer(text, return_tensors="pt")

output = model.generate(**inputs, max_length=50)

print(tokenizer.decode(output[0]))

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.)