distilbert_sa_GLUE_Experiment_data_aug_qqp_384

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: 0.5363
• Accuracy: 0.7995
• F1: 0.7338
• Combined Score: 0.7666

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	Accuracy	Combined Score	F1	Validation Loss
0.3538	1.0	29671	0.7995	0.7666	0.7338	0.5363
0.1571	2.0	59342	0.7215	0.8000	0.7396	0.7698
0.0894	3.0	89013	0.7922	0.7998	0.7407	0.7702
0.0596	4.0	118684	0.8829	0.8045	0.7399	0.7722
0.0433	5.0	148355	0.8505	0.8110	0.7443	0.7777
0.0334	6.0	178026	1.0843	0.8047	0.7446	0.7746

Framework versions

• Transformers 4.26.0
• Pytorch 1.14.0a0+410ce96
• Datasets 2.9.0
• Tokenizers 0.13.2