This is the model
CovidBERT
trained by DeepSet on AllenAI's
CORD19 Dataset
of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the
SNLI
and the
MultiNLI
datasets using the
sentence-transformers
library
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
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
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
repository.
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