# 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/)

{}
transformers pytorch jax bert feature-extraction endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us 

BioBERT-NLI
===========


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.


Parameters:



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.



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


References:


[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



TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us 
