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Medical-NER

Token Classification
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deberta-v2
generated_from_trainer
medical
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metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
  - medical
model-index:
  - name: deberta-med-ner-2
    results: []
widget:
  - text: 63 year old woman with history of CAD presented to ER
    example_title: Example-1
  - text: 63 year old woman diagnosed with CAD
    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
pipeline_tag: token-classification

deberta-med-ner-2

This model is a fine-tuned version of DeBERTa on the PubMED Dataset.

Model description

Medical NER Model finetuned on BERT to recognize 41 Medical entities.

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Usage

The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.

# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="Clinical-AI-Apollo/Medical-NER", aggregation_strategy='simple')
result = pipe('45 year old woman diagnosed with CAD')



# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Clinical-AI-Apollo/Medical-NER")
model = AutoModelForTokenClassification.from_pretrained("Clinical-AI-Apollo/Medical-NER")

Author

Author: Saketh Mattupalli

Framework versions

  • Transformers 4.37.0
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.1