  Biomedical literature is a rapidly expanding field of science and technology.
Classification of biomedical texts is an essential part of biomedicine
research, especially in the field of biology. This work proposes the fine-tuned
DistilBERT, a methodology-specific, pre-trained generative classification
language model for mining biomedicine texts. The model has proven its
effectiveness in linguistic understanding capabilities and has reduced the size
of BERT models by 40\% but by 60\% faster. The main objective of this project
is to improve the model and assess the performance of the model compared to the
non-fine-tuned model. We used DistilBert as a support model and pre-trained on
a corpus of 32,000 abstracts and complete text articles; our results were
impressive and surpassed those of traditional literature classification methods
by using RNN or LSTM. Our aim is to integrate this highly specialised and
specific model into different research industries.
