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

Loss: 0.1521
Precision: 0.8352
Recall: 0.8605
F1: 0.8477
Accuracy: 0.9715

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0855	1.0	1041	0.1281	0.8272	0.8371	0.8321	0.9688
0.0536	2.0	2082	0.1357	0.8134	0.8465	0.8296	0.9686
0.0333	3.0	3123	0.1227	0.8593	0.8713	0.8653	0.9740
0.0221	4.0	4164	0.1482	0.8474	0.8564	0.8519	0.9710
0.0163	5.0	5205	0.1521	0.8352	0.8605	0.8477	0.9715

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.1
Tokenizers 0.21.1

raulgdp/NER-finetuning-Bert-base

NER-finetuning-Bert-base

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

raulgdp/
NER-finetuning-Bert-base