README.md

---
license: openrail
inference:
  parameters:
    num_beams: 3
    num_beam_groups: 3
    num_return_sequences: 1
    repetition_penalty: 3
    diversity_penalty: 3.01
    no_repeat_ngram_size: 2
    temperature: 0.8
    max_length: 64
widget:
- text: >-
    paraphraser: 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: >-
    paraphraser: 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: >-
    paraphraser: 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.

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:

```python
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 = "paraphraser:" + "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=64)

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.)