alabnii/jmedroberta-base-sentencepiece-vocab50000

PetBERT is a masked language model based on the BERT architecture further trained on over 500 million additional words from first-opinion veterinary clinicians from across the UK

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This model is a lightweight model pre-trained on SELFIES (Self-Referencing Embedded Strings) representations of molecules. It is trained on 2.7M unique and valid molecules taken from COCONUTDB and ChemBL34, with 7.3M total generated masked examples.

The Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the Rescue? . The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into…

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

This is the pretrained VetBERT model from the github repo: https://github.com/havocy28/VetBERT