This model is a fine-tuned version of bert-base-multilingual-cased on the tmnam20/VieGLUE/MNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.5343
Accuracy: 0.8063

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: 32
eval_batch_size: 16
seed: 100
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.62	0.41	5000	0.6193	0.7459
0.5923	0.81	10000	0.5911	0.7610
0.5136	1.22	15000	0.5670	0.7808
0.4927	1.63	20000	0.5558	0.7852
0.4425	2.04	25000	0.5809	0.7844
0.4301	2.44	30000	0.5546	0.7940
0.4017	2.85	35000	0.5565	0.7963

Framework versions

Transformers 4.36.0
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0

tmnam20/bert-base-multilingual-cased-mnli-100

bert-base-multilingual-cased-mnli-100

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tmnam20/
bert-base-multilingual-cased-mnli-100