medicine-ner

This model is a fine-tuned version of distilbert-base-uncased on the jxner dataset. It achieves the following results on the evaluation set:

• Loss: 0.7996
• Precision: 0.0
• Recall: 0.0
• F1: 0.0
• Accuracy: 0.8594

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
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	1	0.8644	0.0	0.0	0.0	0.8594
No log	2.0	2	0.7996	0.0	0.0	0.0	0.8594

Framework versions

• Transformers 4.27.3
• Pytorch 1.13.1+cu116
• Datasets 2.10.1
• Tokenizers 0.13.2