pritamdeka/BioBERT mnli snli scinli scitail mednli stsb 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. It has been trained over the SNLI, MNLI, SCINLI, SCITAIL, MEDNLI and STSB datasets for providing robust sentence embeddings. 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. Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark : https://seb.sbert.net Training The model was trained with the parameters: DataLoader : torch.utils.data.dataloader.DataLoader of length 90 with parameters: Loss : sentence transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss Parameters of the fit() Method: Full Model Architecture Citing & Authors If you use the model kindly cite the foll…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy