bert_uncased_L-2_H-128_A-2_mrpc

This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

• Loss: 0.5352
• Accuracy: 0.7328
• F1: 0.8233
• Combined Score: 0.7781

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
0.6493	1.0	15	0.6227	0.6838	0.8122	0.7480
0.6257	2.0	30	0.6134	0.6838	0.8122	0.7480
0.6126	3.0	45	0.6052	0.6838	0.8122	0.7480
0.6036	4.0	60	0.5954	0.6961	0.8176	0.7569
0.5897	5.0	75	0.5879	0.6985	0.8167	0.7576
0.5781	6.0	90	0.5741	0.7034	0.8158	0.7596
0.5635	7.0	105	0.5711	0.7108	0.8201	0.7655
0.5429	8.0	120	0.5674	0.7132	0.8208	0.7670
0.5228	9.0	135	0.5685	0.7206	0.8252	0.7729
0.5057	10.0	150	0.5497	0.7304	0.8281	0.7793
0.4856	11.0	165	0.5438	0.7377	0.8293	0.7835
0.4657	12.0	180	0.5352	0.7328	0.8233	0.7781
0.4447	13.0	195	0.5435	0.7402	0.8323	0.7862
0.4175	14.0	210	0.5562	0.7402	0.8328	0.7865
0.4039	15.0	225	0.5759	0.7426	0.8357	0.7892
0.3964	16.0	240	0.5610	0.7377	0.8299	0.7838
0.3735	17.0	255	0.5587	0.7377	0.8283	0.7830

Framework versions

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