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:

• Loss: 0.0633
• Precision: 0.9294
• Recall: 0.9456
• F1: 0.9374
• Accuracy: 0.9852

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 OptimizerNames.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
0.0779	1.0	1756	0.0695	0.8938	0.9268	0.9100	0.9810
0.0334	2.0	3512	0.0633	0.9294	0.9456	0.9374	0.9852

Framework versions

• Transformers 4.46.3
• Pytorch 2.5.1+cpu
• Datasets 3.1.0
• Tokenizers 0.20.0