| # CovidBERT-NLI | |
| This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses. | |
| The model uses the original BERT wordpiece vocabulary and was subsequently 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 [1] using the **average pooling strategy** and a **softmax loss**. | |
| Parameter details for the original training on CORD-19 are available on [DeepSet's MLFlow](https://public-mlflow.deepset.ai/#/experiments/2/runs/ba27d00c30044ef6a33b1d307b4a6cba) | |
| **Base model**: `deepset/covid_bert_base` 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 | 23000 | | |
| | Warmup steps | 1450 | | |
| | Lowercasing | True | | |
| | 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 similar models obtained with the same procedure to verify its performances. | |
| | Model | Score | | |
| |-------------------------------|-------------| | |
| | `covidbert-nli` (this) | 67.52 | | |
| | `gsarti/biobert-nli` | 73.40 | | |
| | `gsarti/scibert-nli` | 74.50 | | |
| | `bert-base-nli-mean-tokens`[2]| 77.12 | | |
| An example usage for similarity-based scientific paper retrieval is provided in the [Covid-19 Semantic Browser](https://github.com/gsarti/covid-papers-browser) repository. | |
| **References:** | |
| [1] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/) | |
| [2] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/) | |