bert_base_km_20_v2_wnli

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

• Loss: 0.7320
• Accuracy: 0.3944

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
0.7432	1.0	3	0.7320	0.3944
0.7018	2.0	6	0.7808	0.3380
0.6914	3.0	9	0.7764	0.2676
0.6971	4.0	12	0.7966	0.3380
0.6782	5.0	15	0.8305	0.2394
0.6864	6.0	18	0.8585	0.1831

Framework versions

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