  Automatic or semi-automatic conversion of protocols specifying steps in
performing a lab procedure into machine-readable format benefits biological
research a lot. These noisy, dense, and domain-specific lab protocols
processing draws more and more interests with the development of deep learning.
This paper presents our teamwork on WNUT 2020 shared task-1: wet lab entity
extract, that we conducted studies in several models, including a BiLSTM CRF
model and a Bert case model which can be used to complete wet lab entity
extraction. And we mainly discussed the performance differences of \textbf{Bert
case} under different situations such as \emph{transformers} versions, case
sensitivity that may don't get enough attention before.

Computation and Language;Machine Learning