File size: 2,254 Bytes
798e9c1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
# 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/)
|