NER-finetuning-BETO-PRO

This model is a fine-tuned version of google-bert/bert-base-cased on the conll2002 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1391
• Precision: 0.7331
• Recall: 0.7923
• F1: 0.7616
• Accuracy: 0.9655

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
0.1028	1.0	1041	0.1424	0.7051	0.7603	0.7317	0.9618
0.0678	2.0	2082	0.1391	0.7331	0.7923	0.7616	0.9655

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3