citebert

Introduction

CiteBERT is a SciBERT pretrained language model that has been further fine-tuned for tasks involving masked language modeling and cite-worthiness detection. It is specifically trained using the CiteWorth dataset. The model is designed for further fine-tuning on downstream tasks related to scientific document understanding.

Architecture

CiteBERT builds upon the SciBERT architecture, which is a variant of BERT adapted for scientific literature. It leverages transformers and is implemented using PyTorch, allowing it to handle feature extraction and text embeddings, particularly in the context of scientific texts.

Training

The model has been fine-tuned on the CiteWorth dataset, focusing on masked language modeling and cite-worthiness detection. It is prepared to advance understanding and processing of scientific documents through additional task-specific fine-tuning.

Guide: Running Locally

To run CiteBERT locally, follow these steps:

1. Install Dependencies: Make sure Python and PyTorch are installed on your machine.
2. Clone the Repository: Download the model files from the Hugging Face model hub.
3. Load the Model: Use the Transformers library to load and configure the model.
4. Fine-tune as Required: Perform additional training on your specific dataset if necessary.

For enhanced performance, consider using cloud GPUs such as those offered by AWS, Google Cloud, or Azure.

License

The licensing information for CiteBERT is not explicitly provided in the provided document. Please check the model's page on the Hugging Face hub for detailed license terms.