This model is a fine-tuned version of Hartunka/distilbert_rand_10_v2 on the GLUE WNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.7056
Accuracy: 0.4225

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
0.716	1.0	3	0.7056	0.4225
0.7053	2.0	6	0.7251	0.2817
0.6934	3.0	9	0.7280	0.4507
0.6861	4.0	12	0.7464	0.3239
0.6893	5.0	15	0.7720	0.2394
0.6942	6.0	18	0.7827	0.3099

Framework versions

Transformers 4.50.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.21.1

Hartunka/distilbert_rand_10_v2_wnli

distilbert_rand_10_v2_wnli

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Hartunka/
distilbert_rand_10_v2_wnli