cambridgeltl/SapBERT-from-PubMedBERT-fulltext

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…

ChemFIE-SA is a BERT-like sequence classifier for predicting synthesis accessibility given a SELFIES string of a compound, fine-tuned from gbyuvd/chemselfies-base-bertmlm on DeepSA's expanded dataset from Wang et al. 2023.

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 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.

This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST).