distilbert-base-uncased__sst2__train-32-9

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5625
• Accuracy: 0.7353

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: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• 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
0.7057	1.0	13	0.6805	0.5385
0.6642	2.0	26	0.6526	0.7692
0.5869	3.0	39	0.5773	0.8462
0.4085	4.0	52	0.4959	0.8462
0.2181	5.0	65	0.4902	0.6923
0.069	6.0	78	0.5065	0.8462
0.0522	7.0	91	0.6082	0.7692
0.0135	8.0	104	0.6924	0.7692
0.0084	9.0	117	0.5921	0.7692
0.0061	10.0	130	0.6477	0.7692
0.0047	11.0	143	0.6648	0.7692
0.0035	12.0	156	0.6640	0.7692
0.0031	13.0	169	0.6615	0.7692
0.0029	14.0	182	0.6605	0.7692
0.0026	15.0	195	0.6538	0.8462

Framework versions

• Transformers 4.15.0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2
• Tokenizers 0.10.3