distilbert_add_GLUE_Experiment_mrpc_96

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: 0.6239
• Accuracy: 0.6838
• F1: 0.8122
• Combined Score: 0.7480

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
0.6686	1.0	15	0.6467	0.6838	0.8122	0.7480
0.6433	2.0	30	0.6372	0.6838	0.8122	0.7480
0.6378	3.0	45	0.6319	0.6838	0.8122	0.7480
0.6344	4.0	60	0.6284	0.6838	0.8122	0.7480
0.6343	5.0	75	0.6266	0.6838	0.8122	0.7480
0.6299	6.0	90	0.6252	0.6838	0.8122	0.7480
0.6335	7.0	105	0.6247	0.6838	0.8122	0.7480
0.6308	8.0	120	0.6243	0.6838	0.8122	0.7480
0.6306	9.0	135	0.6243	0.6838	0.8122	0.7480
0.6302	10.0	150	0.6241	0.6838	0.8122	0.7480
0.6296	11.0	165	0.6241	0.6838	0.8122	0.7480
0.6305	12.0	180	0.6239	0.6838	0.8122	0.7480
0.634	13.0	195	0.6242	0.6838	0.8122	0.7480
0.63	14.0	210	0.6243	0.6838	0.8122	0.7480
0.6314	15.0	225	0.6242	0.6838	0.8122	0.7480
0.6286	16.0	240	0.6239	0.6838	0.8122	0.7480
0.6326	17.0	255	0.6242	0.6838	0.8122	0.7480

Framework versions

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