bert-base-NER

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0642
• Precision: 1.0
• Recall: 0.9711
• F1: 0.9853
• Accuracy: 0.9790

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	8	0.0885	1.0	0.9480	0.9733	0.9622
No log	2.0	16	0.0642	1.0	0.9711	0.9853	0.9790

Framework versions

• Transformers 4.37.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2