hBERTv1_new_pretrain_wnli

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

• Loss: 0.6852
• Accuracy: 0.5634

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
0.8538	1.0	5	0.6975	0.4366
0.7194	2.0	10	0.6922	0.5634
0.7223	3.0	15	0.6893	0.5634
0.713	4.0	20	0.7205	0.4366
0.7081	5.0	25	0.6865	0.5634
0.7028	6.0	30	0.7048	0.4366
0.697	7.0	35	0.6852	0.5634
0.7002	8.0	40	0.6967	0.4366
0.7017	9.0	45	0.7156	0.4366
0.702	10.0	50	0.6885	0.5634
0.6945	11.0	55	0.6927	0.4930
0.7002	12.0	60	0.6922	0.4648

Framework versions

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