bert_base_km_10_v1_qnli

This model is a fine-tuned version of Hartunka/bert_base_km_10_v1 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

• Loss: 0.6279
• Accuracy: 0.6498

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
0.6642	1.0	410	0.6346	0.6396
0.6203	2.0	820	0.6279	0.6498
0.5422	3.0	1230	0.6750	0.6304
0.422	4.0	1640	0.7333	0.6467
0.2952	5.0	2050	0.8972	0.6551
0.1998	6.0	2460	1.0510	0.6445
0.1454	7.0	2870	1.2518	0.6469

Framework versions

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