readme: # BioBERT-NLI
  
  This is the model [BioBERT](https://github.com/dmis-lab/biobert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) 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.
  
  **Parameters**:
  
  | 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](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) 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](https://github.com/gsarti/covid-papers-browser) repository.
  
  **References:**
  
  [1] J. Lee et al, [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://academic.oup.com/bioinformatics/article/36/4/1234/5566506)
  
  [2] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)
  
  [3] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)
  