bert_uncased_L-4_H-128_A-2_qqp

This model is a fine-tuned version of google/bert_uncased_L-4_H-128_A-2 on the GLUE QQP dataset. It achieves the following results on the evaluation set:

• Loss: 0.3084
• Accuracy: 0.8694
• F1: 0.8238
• Combined Score: 0.8466

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
0.448	1.0	1422	0.3785	0.8204	0.7736	0.7970
0.3757	2.0	2844	0.3562	0.8351	0.7980	0.8165
0.3453	3.0	4266	0.3263	0.8521	0.8052	0.8286
0.3226	4.0	5688	0.3198	0.8560	0.8090	0.8325
0.3049	5.0	7110	0.3245	0.8552	0.8159	0.8355
0.2903	6.0	8532	0.3136	0.8602	0.8207	0.8405
0.277	7.0	9954	0.3188	0.8603	0.8200	0.8401
0.2667	8.0	11376	0.3146	0.8648	0.8258	0.8453
0.2551	9.0	12798	0.3239	0.8612	0.8240	0.8426
0.2444	10.0	14220	0.3084	0.8694	0.8238	0.8466
0.2358	11.0	15642	0.3224	0.8669	0.8296	0.8483
0.2276	12.0	17064	0.3147	0.8699	0.8313	0.8506
0.2191	13.0	18486	0.3185	0.8708	0.8332	0.8520
0.2111	14.0	19908	0.3282	0.8721	0.8337	0.8529
0.2048	15.0	21330	0.3194	0.8738	0.8306	0.8522

Framework versions

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