  The practice of fine-tuning Pre-trained Language Models (PLMs) from general
or domain-specific data to a specific task with limited resources, has gained
popularity within the field of natural language processing (NLP). In this work,
we re-visit this assumption and carry out an investigation in clinical NLP,
specifically Named Entity Recognition on drugs and their related attributes. We
compare Transformer models that are trained from scratch to fine-tuned
BERT-based LLMs namely BERT, BioBERT, and ClinicalBERT. Furthermore, we examine
the impact of an additional CRF layer on such models to encourage contextual
learning. We use n2c2-2018 shared task data for model development and
evaluations. The experimental outcomes show that 1) CRF layers improved all
language models; 2) referring to BIO-strict span level evaluation using
macro-average F1 score, although the fine-tuned LLMs achieved 0.83+ scores, the
TransformerCRF model trained from scratch achieved 0.78+, demonstrating
comparable performances with much lower cost - e.g. with 39.80\% less training
parameters; 3) referring to BIO-strict span-level evaluation using
weighted-average F1 score, ClinicalBERT-CRF, BERT-CRF, and TransformerCRF
exhibited lower score differences, with 97.59\%/97.44\%/96.84\% respectively.
4) applying efficient training by down-sampling for better data distribution
further reduced the training cost and need for data, while maintaining similar
scores - i.e. around 0.02 points lower compared to using the full dataset. Our
models will be hosted at \url{https://github.com/HECTA-UoM/TransformerCRF}
