add_BERT_no_pretrain_qqp

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

• Loss: 0.5939
• Accuracy: 0.6824
• F1: 0.4705
• Combined Score: 0.5764

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	F1	Combined Score
0.657	1.0	2843	0.6438	0.6490	0.1608	0.4049
0.6273	2.0	5686	0.6302	0.6443	0.1919	0.4181
0.6273	3.0	8529	0.6265	0.6527	0.3602	0.5064
0.6093	4.0	11372	0.5939	0.6824	0.4705	0.5764
0.5932	5.0	14215	0.5962	0.6802	0.4170	0.5486
0.599	6.0	17058	0.5981	0.6757	0.4795	0.5776
0.6063	7.0	19901	0.6511	0.6318	0.0	0.3159
0.6264	8.0	22744	0.6261	0.6532	0.2074	0.4303
0.6348	9.0	25587	0.6774	0.6318	0.0	0.3159

Framework versions

• Transformers 4.30.2
• Pytorch 1.14.0a0+410ce96
• Datasets 2.12.0
• Tokenizers 0.13.3