general_character_bert

Introduction

CharacterBERT is a variant of the BERT model designed to improve word-level, open-vocabulary representations by using characters rather than subword units. It is particularly aimed at specialized domains like the medical field, where predefined wordpiece vocabularies may not be suitable. This model was introduced by El Boukkouri et al. at the 28th International Conference on Computational Linguistics in 2020.

Architecture

CharacterBERT leverages the Transformer architecture but replaces the traditional wordpiece tokenization with a Character-CNN module. This module processes entire words based on their character sequences, allowing for more flexible and robust representations without predefined vocabularies. This approach helps maintain efficiency while improving performance in specific domains.

Training

The model was pretrained using datasets such as Wikipedia and OpenWebText to develop its language understanding capabilities. The Character-CNN module allows CharacterBERT to handle words as sequences of characters, which can be particularly beneficial for tasks requiring domain-specific knowledge.

Guide: Running Locally

1. Clone the Repository: Download the CharacterBERT repository from GitHub.
2. Install Dependencies: Ensure you have Python and PyTorch installed. Install other necessary packages as listed in the repository's requirements file.
3. Download Pretrained Model: Obtain the pretrained CharacterBERT model from the Hugging Face Model Hub.
4. Run the Model: Use the provided scripts to load and run the model on your local machine. Modify the scripts to fit your specific task or dataset.
5. Consider Cloud GPUs: For faster processing, consider leveraging cloud-based GPU services such as AWS, Google Cloud, or Azure.

License

CharacterBERT is licensed under the terms specified in its repository. Users should review the license details to ensure compliance with any usage restrictions or obligations.