bert-base-uncased_mrpc

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

• Loss: 0.4455
• Accuracy: 0.7868
• F1: 0.8482
• Combined Score: 0.8175

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
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
0.5933	1.0	15	0.5066	0.7745	0.8351	0.8048
0.4605	2.0	30	0.4455	0.7868	0.8482	0.8175
0.31	3.0	45	0.5169	0.8162	0.8777	0.8469
0.1871	4.0	60	0.4473	0.8407	0.8862	0.8634
0.1453	5.0	75	0.5061	0.8235	0.8672	0.8453
0.0963	6.0	90	0.5724	0.8284	0.8797	0.8541
0.0515	7.0	105	0.7238	0.8333	0.8863	0.8598

Framework versions

• Transformers 4.46.3
• Pytorch 2.2.1+cu118
• Datasets 2.17.0
• Tokenizers 0.20.3