This model is a fine-tuned version of gokulsrinivasagan/bert_base_lda_5 on the GLUE WNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.6849
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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.121	1.0	3	0.8205	0.5634
1.6034	2.0	6	1.7293	0.4366
0.9483	3.0	9	0.7649	0.4366
0.7514	4.0	12	0.7557	0.5634
0.746	5.0	15	0.8105	0.4366
0.7896	6.0	18	0.7383	0.4366
0.7573	7.0	21	0.6853	0.5634
0.6951	8.0	24	0.8346	0.4366
0.746	9.0	27	0.6906	0.5634
0.6992	10.0	30	0.6849	0.5634
0.6942	11.0	33	0.7009	0.4366
0.7	12.0	36	0.6951	0.4366
0.6976	13.0	39	0.6854	0.5634
0.6999	14.0	42	0.6901	0.5634
0.6948	15.0	45	0.6926	0.5634

Framework versions

Transformers 4.46.3
Pytorch 2.2.1+cu118
Datasets 2.17.0
Tokenizers 0.20.3

gokulsrinivasagan/bert_base_lda_5_wnli

bert_base_lda_5_wnli

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokulsrinivasagan/
bert_base_lda_5_wnli