Text mining in the clinical domain has become increasingly important with the number of biomedical documents currently out there with valuable information waiting to be deciphered and optimized by NLP techniques. With the accelerated progress in NLP, pre-trained language models now carry millions (or even billions) of parameters and can leverage massive amounts of textual knowledge for downstream tasks such as question answering, natural language inference, and in the case that we will work through, biomedical text tagging via named-entity recognition. All of the code can be found on my GitHub.
Keyword tagging artworks with GPT4 and Google Vision
I have no data to back this up (a great way to start a blog about data), but I think when people visit a museum’s…