metadata
license: apache-2.0
language:
- en
tags:
- Token Classification
co2_eq_emissions: 0.0279399890043426
widget:
- text: >-
CASE: A 28-year-old previously healthy man presented with a 6-week history
of palpitations. The symptoms occurred during rest, 2–3 times per week,
lasted up to 30 minutes at a time and were associated with dyspnea. Except
for a grade 2/6 holosystolic tricuspid regurgitation murmur (best heard at
the left sternal border with inspiratory accentuation), physical
examination yielded unremarkable findings.
example_title: example 1
- text: >-
A 63-year-old woman with no known cardiac history presented with a sudden
onset of dyspnea requiring intubation and ventilatory support out of
hospital. She denied preceding symptoms of chest discomfort, palpitations,
syncope or infection. The patient was afebrile and normotensive, with a
sinus tachycardia of 140 beats/min.
example_title: example 2
- text: >-
A 48 year-old female presented with vaginal bleeding and abnormal Pap
smears. Upon diagnosis of invasive non-keratinizing SCC of the cervix, she
underwent a radical hysterectomy with salpingo-oophorectomy which
demonstrated positive spread to the pelvic lymph nodes and the
parametrium. Pathological examination revealed that the tumour also
extensively involved the lower uterine segment.
example_title: example 3
datasets:
- tner/bc5cdr
- commanderstrife/jnlpba
- bc2gm_corpus
- drAbreu/bc4chemd_ner
- linnaeus
- chintagunta85/ncbi_disease
About the Model
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
- Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942
- Carbon emission: 0.0279399890043426 Kg
- Training time: 30.16527 minutes
- GPU used : 1 x GeForce RTX 3060 Laptop GPU
Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18
Usage
The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""")
Author
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: