distilbert_add_GLUE_Experiment_qqp_192

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

• Loss: 0.4419
• Accuracy: 0.7997
• F1: 0.7305
• Combined Score: 0.7651

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
• distributed_type: multi-GPU
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
0.5641	1.0	1422	0.5422	0.7304	0.6553	0.6929
0.5047	2.0	2844	0.5128	0.7504	0.6575	0.7039
0.487	3.0	4266	0.5020	0.7573	0.6656	0.7114
0.4729	4.0	5688	0.4907	0.7638	0.6695	0.7166
0.4502	5.0	7110	0.4759	0.7789	0.6736	0.7262
0.4139	6.0	8532	0.4635	0.7926	0.6909	0.7417
0.3728	7.0	9954	0.4419	0.7997	0.7305	0.7651
0.3334	8.0	11376	0.4760	0.8026	0.6964	0.7495
0.2998	9.0	12798	0.4597	0.8125	0.7314	0.7719
0.2704	10.0	14220	0.4692	0.8173	0.7403	0.7788
0.244	11.0	15642	0.4990	0.8195	0.7413	0.7804
0.2218	12.0	17064	0.5195	0.8198	0.7350	0.7774

Framework versions

• Transformers 4.26.0
• Pytorch 1.14.0a0+410ce96
• Datasets 2.8.0
• Tokenizers 0.13.2