bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0623
• Precision: 0.9406
• Recall: 0.9464
• F1: 0.9435
• Accuracy: 0.9860

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0389	1.0	1756	0.0623	0.9339	0.9386	0.9362	0.9845
0.019	2.0	3512	0.0623	0.9406	0.9464	0.9435	0.9860

Framework versions

• Transformers 4.46.3
• Pytorch 2.5.1+cu121
• Tokenizers 0.20.3