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