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