This is the model BioBERT [1] fine-tuned on the SNLI and the 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.

Base model: monologg/biobert_v1.1_pubmed from HuggingFace's AutoModel.

Training time: ~6 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.


Parameter Value
Batch size 64
Training steps 30000
Warmup steps 1450
Lowercasing False
Max. Seq. Length 128

Performances: 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.

Model Score
biobert-nli (this) 73.40
gsarti/scibert-nli 74.50
bert-base-nli-mean-tokens[3] 77.12

An example usage for similarity-based scientific paper retrieval is provided in the Covid Papers Browser repository.


[1] J. Lee et al, BioBERT: a pre-trained biomedical language representation model for biomedical text mining

[2] A. Conneau et al., Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

[3] N. Reimers et I. Gurevych, Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

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