This model is a fine-tuned version of Hartunka/bert_base_rand_100_v1 on the GLUE WNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.7060
Accuracy: 0.5070

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
0.6987	1.0	3	0.7065	0.2958
0.6957	2.0	6	0.7060	0.5070
0.694	3.0	9	0.7249	0.3099
0.6932	4.0	12	0.7266	0.4085
0.693	5.0	15	0.7479	0.2254
0.6904	6.0	18	0.7845	0.2535
0.6908	7.0	21	0.8210	0.1972

Framework versions

Transformers 4.50.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.21.1

Hartunka/bert_base_rand_100_v1_wnli

bert_base_rand_100_v1_wnli

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Hartunka/
bert_base_rand_100_v1_wnli