gsarti/biobert-nli

The model was fine-tuned on SNLI and MultiNLI datasets using the sentence-transformers library to produce universal sentence embeddings [2]. The model uses the original BERT wordpiece vocabulary and was trained using the average pooling strategy and a softmax loss . The performance was evaluated on the test portion of the STS dataset using Spearman rank correlation and compared to the performances of a general BERT base model obtained with the same procedure to verify their similarity . An example usage for similarity-based scientific paper retrieval is provided in the Covid Papers Browser repository . The model has been used in the Kaggle Notebooks repository to train the model on the NVIDIA Tesla P,