distilbert_sa_GLUE_Experiment_mrpc_192

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

• Loss: 0.5927
• Accuracy: 0.6887
• F1: 0.7784
• Combined Score: 0.7335

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
0.6412	1.0	15	0.6239	0.6838	0.8122	0.7480
0.6281	2.0	30	0.6238	0.6838	0.8122	0.7480
0.629	3.0	45	0.6239	0.6838	0.8122	0.7480
0.6296	4.0	60	0.6236	0.6838	0.8122	0.7480
0.6323	5.0	75	0.6228	0.6838	0.8122	0.7480
0.6272	6.0	90	0.6209	0.6838	0.8122	0.7480
0.6175	7.0	105	0.6000	0.6838	0.8122	0.7480
0.5733	8.0	120	0.5927	0.6887	0.7784	0.7335
0.5199	9.0	135	0.5969	0.6936	0.7818	0.7377
0.4423	10.0	150	0.6369	0.6765	0.7700	0.7233
0.3645	11.0	165	0.6708	0.6838	0.7832	0.7335
0.3203	12.0	180	0.7179	0.6446	0.7249	0.6847
0.2778	13.0	195	0.7517	0.6740	0.7726	0.7233

Framework versions

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