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---
license: cc-by-nc-nd-4.0
inference:
parameters:
num_beams: 3
num_beam_groups: 3
num_return_sequences: 1
repetition_penalty: 10.0
diversity_penalty: 3.01
no_repeat_ngram_size: 2
temperature: 0.8
max_length: 128
widget:
- text: >-
Data scientists need to be able to communicate their findings to others in a clear and concise way.
example_title: Data scientists
- text: >-
Search engine optimization (SEO) is the practice of getting targeted traffic to a website from a search engine's organic rankings.
example_title: SEO
- 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:
```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')
```
```python
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:**
This model is intended for research and creative writing purposes. 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.)
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