This model is a fine-tuned version of
bert-base-uncased on the medical-ner-bleurt-separated dataset.
It achieves the following results on the evaluation set:
The DeepNeural NER-I model is exclusively designed to identify body parts in textual documents.
This clinical support model is one of many to be released, and is a crucial aspect of clinical support systems.
The model is meant to be used for research and development purposes by Data Scientists, ML & Software Engineers for the development of NER applications
capable of identifying body parts in medical EHR systems to augment patient health processing.
The DeepNeural_NER-I model was trained with precision and accuracy in mind, and therefore
the model was trained for 3 epochs and 13500 global steps per epoch. The training scores utilized
are highlighted in the table below.
