bert_base_km_20_v2_mnli

This model is a fine-tuned version of Hartunka/bert_base_km_20_v2 on the GLUE MNLI dataset. It achieves the following results on the evaluation set:

• Loss: 0.7873
• Accuracy: 0.6645

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: 5e-05
• train_batch_size: 256
• eval_batch_size: 256
• seed: 10
• 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9876	1.0	1534	0.9146	0.5727
0.8776	2.0	3068	0.8655	0.6046
0.7975	3.0	4602	0.8097	0.6418
0.7185	4.0	6136	0.8000	0.6566
0.6415	5.0	7670	0.7951	0.6616
0.5611	6.0	9204	0.8547	0.6600
0.4782	7.0	10738	0.9200	0.6601
0.3996	8.0	12272	1.0102	0.6587
0.3293	9.0	13806	1.1861	0.6507
0.2684	10.0	15340	1.2364	0.6570

Framework versions

• Transformers 4.50.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.21.1