README.md

metadata

license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
model-index:
  - name: deberta-med-ner-2
    results: []
widget:
  - 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 1

deberta-med-ner-2

This model is a fine-tuned version of DeBERTaV3 on the PubMED Dataset.

Model description

MED-NER Model was finetuned on BERT to recognize 41 Medical entities.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 32
• seed: 69
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 25
• 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="NeuronZero/MED-NER", aggregation_strategy='simple')

result = pipe('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.')



# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("NeuronZero/MED-NER")
model = AutoModelForTokenClassification.from_pretrained("NeuronZero/MED-NER")