bert-finetuned-np-chunking

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0673
• Np: {'precision': 0.9644760213143873, 'recall': 0.9718742009716185, 'f1': 0.968160978094753, 'number': 7822}
• Overall Precision: 0.9645
• Overall Recall: 0.9719
• Overall F1: 0.9682
• Overall Accuracy: 0.9813

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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Np	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0646	1.0	3751	0.0673	{'precision': 0.9644760213143873, 'recall': 0.9718742009716185, 'f1': 0.968160978094753, 'number': 7822}	0.9645	0.9719	0.9682	0.9813

Framework versions

• Transformers 4.46.2
• Pytorch 2.4.1+cu118
• Datasets 3.1.0
• Tokenizers 0.20.3