bert_base_lda_wnli

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

• Loss: 0.6864
• 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: 0.001
• 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
1.1638	1.0	3	1.4608	0.5634
1.0198	2.0	6	1.1097	0.5634
1.1474	3.0	9	0.8995	0.5634
0.8846	4.0	12	0.8201	0.4366
0.7886	5.0	15	0.6994	0.4366
0.738	6.0	18	0.7087	0.5634
0.7195	7.0	21	0.7214	0.4366
0.7036	8.0	24	0.6931	0.5634
0.6935	9.0	27	0.6896	0.5634
0.6941	10.0	30	0.6926	0.5634
0.6949	11.0	33	0.6936	0.4366
0.6959	12.0	36	0.6911	0.5634
0.6927	13.0	39	0.6864	0.5634
0.6928	14.0	42	0.6893	0.5634
0.6958	15.0	45	0.6896	0.5634
0.6936	16.0	48	0.6911	0.5634
0.6955	17.0	51	0.6911	0.5634
0.6939	18.0	54	0.6906	0.5634

Framework versions

• Transformers 4.46.3
• Pytorch 2.2.1+cu118
• Datasets 2.17.0
• Tokenizers 0.20.3