t5 base grammar correction

Introduction

The T5-base-grammar-correction model by Vennify is designed to correct grammatical errors in text. It leverages the T5 architecture for text-to-text generation tasks, aiming to improve the grammatical quality of input text.

Architecture

The model is based on the T5 architecture, a transformer model known for its capabilities in text-to-text generation tasks. It is implemented in PyTorch and specifically fine-tuned for grammar correction tasks using the JFLEG dataset.

Training

The model was fine-tuned using the Happy Transformer library, a user-friendly interface for training transformer models. The training dataset, JFLEG, is recognized for its focus on grammatical error correction. Detailed training processes can be found in the article provided by Vennify.

Guide: Running Locally

To run the model locally, follow these steps:

1. Install the Happy Transformer library:

   pip install happytransformer
   

2. Import and initialize the model:

   from happytransformer import HappyTextToText, TTSettings
   
   happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction")
   

3. Configure settings and generate corrected text:

   args = TTSettings(num_beams=5, min_length=1)
   result = happy_tt.generate_text("grammar: This sentences has has bads grammar.", args=args)
   print(result.text)  # Output: This sentence has bad grammar.
   

For optimal performance, consider using cloud GPU services such as AWS EC2, Google Cloud, or Azure to handle computation-intensive tasks.

License

The model is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (cc-by-nc-sa-4.0).