This model is a fine-tuned version of gokulsrinivasagan/bert_base_lda_20 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.6933
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: 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
0.7224	1.0	410	0.6933	0.5054
0.6932	2.0	820	0.6935	0.4946
0.6933	3.0	1230	0.6934	0.4946
0.6931	4.0	1640	0.6936	0.4946
0.6931	5.0	2050	0.6935	0.4946
0.6932	6.0	2460	0.6933	0.5054
0.6932	7.0	2870	0.6933	0.5054
0.6929	8.0	3280	0.6934	0.4946
0.6933	9.0	3690	0.6933	0.5054
0.6931	10.0	4100	0.6934	0.4946
0.6933	11.0	4510	0.6933	0.5054

Framework versions

Transformers 4.46.3
Pytorch 2.2.1+cu118
Datasets 2.17.0
Tokenizers 0.20.3

gokulsrinivasagan/bert_base_lda_20_qnli

bert_base_lda_20_qnli

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokulsrinivasagan/
bert_base_lda_20_qnli