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
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
repository.
More Information About biobert-nli huggingface.co Model
biobert-nli huggingface.co
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gsarti biobert-nli online free url in huggingface.co:
biobert-nli is an open source model from GitHub that offers a free installation service, and any user can find biobert-nli on GitHub to install. At the same time, huggingface.co provides the effect of biobert-nli install, users can directly use biobert-nli installed effect in huggingface.co for debugging and trial. It also supports api for free installation.