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

Fancy Man Lauches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction