spelling correction english base

Introduction

This document provides an overview of the Spelling-Correction-English-Base model by Oliver Guhr, a spelling correction model designed for the English language. It is a text-to-text generation model that aims to correct typos and punctuation in English text.

Architecture

The model is built on the BART architecture and utilizes the Hugging Face Transformers library. It is compatible with various frameworks including PyTorch, TensorBoard, and ONNX, and is optimized for inference endpoints.

Training

The model has been trained to correct spelling errors in English text. It is still experimental and may produce artifacts. Specific training metrics can be accessed through TensorBoard.

Guide: Running Locally

1. Installation:

   • Ensure you have Python and pip installed.
   • Install the Transformers library:

     pip install transformers
     

2. Running the Model:

   • Use the following Python code to test the model:

     from transformers import pipeline
     
     fix_spelling = pipeline("text2text-generation", model="oliverguhr/spelling-correction-english-base")
     print(fix_spelling("lets do a comparsion", max_length=2048))
     

3. Hardware Suggestions:

   • For optimal performance, especially for large-scale text processing, consider using cloud-based GPUs from providers like AWS, Google Cloud, or Azure.

License

The model is released under the MIT License, allowing for wide use and modification with minimal restrictions.