This is an ONNX version of
d4data/biomedical-ner-all. It was automatically converted and uploaded using
this Hugging Face Space.
An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased
Checkout the tutorial video for explanation of this model and corresponding python library:
https://youtu.be/xpiDPdBpS18The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
1from transformers import pipeline
2from transformers import AutoTokenizer, AutoModelForTokenClassification
3
4tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
5model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
6
7pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
8pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""")
This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:
