This model is a fine-tuned version of gokulsrinivasagan/distilbert_lda_5 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.6914
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
0.7034	1.0	410	0.6934	0.4946
0.6932	2.0	820	0.6946	0.4946
0.6933	3.0	1230	0.6933	0.5054
0.6931	4.0	1640	0.6939	0.4946
0.6932	5.0	2050	0.6935	0.4946
0.6932	6.0	2460	0.6933	0.5054
0.6931	7.0	2870	0.6914	0.5054
0.6929	8.0	3280	0.6934	0.4946
0.6931	9.0	3690	0.6933	0.5054
0.6931	10.0	4100	0.6934	0.4946
0.6971	11.0	4510	0.6933	0.5054
0.6932	12.0	4920	0.6934	0.4946

Framework versions

Transformers 4.46.3
Pytorch 2.2.1+cu118
Datasets 2.17.0
Tokenizers 0.20.3

gokulsrinivasagan/distilbert_lda_5_qnli

distilbert_lda_5_qnli

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokulsrinivasagan/
distilbert_lda_5_qnli