bert-large-5-ner

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

• Loss: 1.3380
• Precision: 0.0572
• Recall: 0.0409
• F1: 0.0477
• Accuracy: 0.7758

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: 1e-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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	3	1.6779	0.0398	0.0825	0.0537	0.6719
No log	2.0	6	1.4283	0.0579	0.0527	0.0551	0.7628
No log	3.0	9	1.3380	0.0572	0.0409	0.0477	0.7758

Framework versions

• Transformers 4.51.2
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1