pritamdeka/S PubMedBert MS MARCO This is a sentence transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This is the microsoft/BiomedNLP PubMedBERT base uncased abstract fulltext model which has been fine tuned over the MS MARCO dataset using sentence transformers framework. It can be used for the information retrieval task in the medical/health text domain. Usage (Sentence Transformers) Using this model becomes easy when you have sentence transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without sentence transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling operation on top of the contextualized word embeddings. Training The model was trained with the parameters: DataLoader : torch.utils.data.dataloader.DataLoader of length 31434 with parameters: Loss : beir.losses.margin mse loss.MarginMSELoss Parameters of the fit() Method: Full Model Architecture Citing & Authors
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