Model Card for PyMUSAS Neural English Small BEM A fine tuned 17 Million (17M) parameter English ModernBERT architecture semantic tagger. The tagger outputs semantic tags at the token level from the USAS tagset. The semantic tagger is a variation of the Bi Encoder Model (BEM) from Blevins and Zettlemoyer 2020 a Word Sense Disambiguation (WSD) model. Table of contents Quick start Installation Requires Python 3.10 or greater, it is best that you install the version of PyTorch you would like to use, e.g. CPU/GPU version etc before installing this package else you will get the default version of PyTorch for your operating system/setup, but we do require torch =2.2,<3.0 . Usage Model Description For more details about the model and how it was trained please see the citation/technical report, as well as the links in the model sources section. Model Sources The training repository contains the code used to train this model. The inference repository contains the code used to run the model as shown in the usage section. Training Repository: https://github.com/UCREL/experimental wsd Inference/Usage Repository: https://github.com/UCREL/WSD Torch Models Model Architecture Parameter 17M English…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy