clinical-ner

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

clinical-ner

This model is a fine-tuned version of microsoft/deberta-v3-base on the Medical dataset. It achieves the following results on the evaluation set:

• Loss: 0.8058
• Precision: 0.5786
• Recall: 0.6683
• F1: 0.6202
• Accuracy: 0.8099

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• 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: 45
• mixed_precision_training: Native AMP

Python Code:

# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="blaze999/clinical-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("blaze999/clinical-ner")
model = AutoModelForTokenClassification.from_pretrained("blaze999/clinical-ner")

Training results

Framework versions

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