distilbert-base-uncased__sst2__train-16-1

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: 0.6012
• Accuracy: 0.6766

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
0.6983	1.0	7	0.7036	0.2857
0.6836	2.0	14	0.7181	0.2857
0.645	3.0	21	0.7381	0.2857
0.5902	4.0	28	0.7746	0.2857
0.5799	5.0	35	0.7242	0.5714
0.3584	6.0	42	0.6935	0.5714
0.2596	7.0	49	0.7041	0.5714
0.1815	8.0	56	0.5930	0.7143
0.0827	9.0	63	0.6976	0.7143
0.0613	10.0	70	0.7346	0.7143
0.0356	11.0	77	0.6992	0.5714
0.0158	12.0	84	0.7328	0.5714
0.013	13.0	91	0.7819	0.5714
0.0103	14.0	98	0.8589	0.5714
0.0087	15.0	105	0.9177	0.5714
0.0076	16.0	112	0.9519	0.5714
0.0078	17.0	119	0.9556	0.5714
0.006	18.0	126	0.9542	0.5714

Framework versions

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