bert_base_km_50_v1_wnli

This model is a fine-tuned version of Hartunka/bert_base_km_50_v1 on the GLUE WNLI dataset. It achieves the following results on the evaluation set:

• Loss: 0.7430
• Accuracy: 0.2817

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
0.7348	1.0	3	0.7430	0.2817
0.6984	2.0	6	0.7746	0.2394
0.6854	3.0	9	0.7891	0.2958
0.6873	4.0	12	0.8266	0.2113
0.6765	5.0	15	0.8794	0.1549
0.6781	6.0	18	0.9071	0.1127

Framework versions

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