This model is a fine-tuned version of bert-base-multilingual-uncased on the universal_dependencies dataset. It achieves the following results on the evaluation set:

Loss: 0.1368
Precision: 0.9594
Recall: 0.9615
F1: 0.9604
Accuracy: 0.9653

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
0.1135	1.0	1568	0.1439	0.9554	0.9581	0.9567	0.9616
0.0661	2.0	3136	0.1368	0.9594	0.9615	0.9604	0.9653

Framework versions

Transformers 4.48.2
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

AndyLiang12/bert-finetuned-pos

bert-finetuned-pos

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

AndyLiang12/
bert-finetuned-pos