BERT fine-tuned CORD-19 NER Dataset

This Named Entities dataset is implemented by employing the widely used Large Language Model (LLM), BERT, on the CORD-19 biomedical literature corpus. By fine-tuning the pre-trained BERT on the CORD-NER dataset, the model gains the ability to comprehend the context and semantics of biomedical named entities. The refined model is then utilized on the CORD-19 to extract more contextually relevant and updated named entities. However, fine-tuning large datasets with LLMs poses a challenge. To counter this, two distinct sampling methodologies are utilized. First, for the NER task on the CORD-19, a Latent Dirichlet Allocation (LDA) topic modeling technique is employed. This maintains the sentence structure while concentrating on related content. Second, a straightforward greedy method is deployed to gather the most informative data of 25 entity types from the CORD-NER dataset.