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

Loss: 0.5808
Accuracy: 0.7108
F1: 0.8033
Combined Score: 0.7571

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	F1	Combined Score
0.6241	1.0	15	0.5959	0.6936	0.8092	0.7514
0.5778	2.0	30	0.5808	0.7108	0.8033	0.7571
0.5155	3.0	45	0.5847	0.7083	0.8090	0.7587
0.3911	4.0	60	0.7104	0.6961	0.7926	0.7444
0.2574	5.0	75	0.9413	0.6936	0.7811	0.7374
0.1479	6.0	90	1.1874	0.6716	0.7519	0.7117
0.0953	7.0	105	1.4011	0.6495	0.7357	0.6926

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_mrpc

bert_base_rand_100_v1_mrpc

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Hartunka/
bert_base_rand_100_v1_mrpc