Instructions to use raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed") model = AutoModelForTokenClassification.from_pretrained("raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed") - Notebooks
- Google Colab
- Kaggle
How to apply this model on PubMed full-text?
#2
by bear007 - opened
Hi @raynardj , I am trying to apply this model to highlight Disease entity on the full-text of a pubmed document. However, using all the default parameters, I noticed only the disease terms in the Abstract section were highlighted. I understood this model was trained on the ncbi_disease dataset which is 'a collection of 793 PubMed abstracts'. Is that why it's only able to highlight entities in the Abstract section? Is there any parameter I can apply to make the model applicable to the full-text of a pubmed paper?
Thanks!