bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:

• Accuracy: 0.9826
• F1: 0.9216
• Loss: 0.0611
• Precision: 0.9068
• Recall: 0.9369

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Accuracy	F1	Validation Loss	Precision	Recall
0.078	1.0	1756	0.9826	0.9216	0.0611	0.9068	0.9369

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• Datasets 2.16.1
• Tokenizers 0.19.1