readme: ---
  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.
  ```python
  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:
  > https://github.com/dreji18/Bio-Epidemiology-NER