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

Loss: 0.7022
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: 5e-05
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
0.6996	1.0	3	0.7022	0.5634
0.6937	2.0	6	0.7077	0.3239
0.6921	3.0	9	0.7160	0.2817
0.6919	4.0	12	0.7135	0.2958
0.6926	5.0	15	0.7214	0.2817
0.6897	6.0	18	0.7356	0.1972

Framework versions

Transformers 4.46.3
Pytorch 2.2.1+cu118
Datasets 2.17.0
Tokenizers 0.20.3

gokulsrinivasagan/distilbert-base-uncased_wnli

distilbert-base-uncased_wnli

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokulsrinivasagan/
distilbert-base-uncased_wnli