hBERTv1_new_pretrain_qnli

This model is a fine-tuned version of gokuls/bert_12_layer_model_v1_complete_training_new on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

• Loss: 0.6591
• Accuracy: 0.6031

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: 4e-05
• train_batch_size: 128
• eval_batch_size: 128
• seed: 10
• distributed_type: multi-GPU
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6783	1.0	819	0.6740	0.5861
0.6609	2.0	1638	0.6591	0.6031
0.6594	3.0	2457	0.6743	0.5923
0.6438	4.0	3276	0.6644	0.5876
0.6421	5.0	4095	0.6731	0.5883
0.6488	6.0	4914	0.6720	0.5936
0.6432	7.0	5733	0.6781	0.5923

Framework versions

• Transformers 4.29.2
• Pytorch 1.14.0a0+410ce96
• Datasets 2.12.0
• Tokenizers 0.13.3