This model is a fine-tuned version of Hartunka/distilbert_rand_50_v2 on the GLUE QQP dataset. It achieves the following results on the evaluation set:

Loss: 0.3923
Accuracy: 0.8168
F1: 0.7451
Combined Score: 0.7810

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
0.4752	1.0	1422	0.4499	0.7853	0.6534	0.7193
0.3679	2.0	2844	0.3923	0.8168	0.7451	0.7810
0.2972	3.0	4266	0.4011	0.8264	0.7674	0.7969
0.2404	4.0	5688	0.4421	0.8301	0.7519	0.7910
0.1957	5.0	7110	0.4787	0.8347	0.7634	0.7991
0.1604	6.0	8532	0.4975	0.8346	0.7698	0.8022
0.1326	7.0	9954	0.5227	0.8370	0.7826	0.8098

Framework versions

Transformers 4.50.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.21.1

Hartunka/distilbert_rand_50_v2_qqp

distilbert_rand_50_v2_qqp

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Hartunka/
distilbert_rand_50_v2_qqp