hBERTv2_data_aug_qnli

This model is a fine-tuned version of gokuls/bert_12_layer_model_v2 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

• Loss: 0.6931
• Accuracy: 0.5054

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
• distributed_type: multi-GPU
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6934	1.0	16604	0.6931	0.5054
0.6932	2.0	33208	0.6931	0.5054
0.6932	3.0	49812	0.6931	0.4946
0.6932	4.0	66416	0.6931	0.5054
0.6932	5.0	83020	0.6932	0.4946
0.6932	6.0	99624	0.6931	0.5054

Framework versions

• Transformers 4.26.1
• Pytorch 1.14.0a0+410ce96
• Datasets 2.10.1
• Tokenizers 0.13.2