Sports B E R T

Introduction

SportsBERT is a domain-specific BERT model focused on sports articles. It is designed to improve performance in natural language processing tasks within the sports domain by using a targeted training corpus.

Architecture

SportsBERT utilizes the BERT base uncased architecture. It is a Masked Language Model (MLM) based transformer model, which is primarily used for tasks like filling in masked tokens in sentences. The model was trained with a specific sports vocabulary, derived from a custom tokenizer.

Training

The model was trained from scratch using approximately 8 million sports-related news articles collected over four years. These articles covered various sports such as Football, Basketball, Hockey, and others. Training was conducted on four V100 GPUs, and the model's primary task involves predicting masked tokens, enhancing its accuracy in sports contexts.

Guide: Running Locally

To run SportsBERT locally, follow these steps:

1. Setup Environment: Ensure you have Python and PyTorch installed on your machine.
2. Clone Repository: Clone the SportsBERT repository from Hugging Face's model hub.
3. Install Dependencies: Use the requirements file to install necessary libraries.
4. Load Model: Use the Hugging Face Transformers library to load the SportsBERT model and tokenizer.
5. Inference: Test the model by providing sports-related sentences and observe its predictions for masked tokens.

For optimal performance, it is recommended to use cloud GPUs like NVIDIA V100 available on platforms such as AWS or Google Cloud.

License

The specific licensing details for SportsBERT have not been provided in the documentation. Users should check the Hugging Face model card for any licensing information.