albert-base-ours-run-4

This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.9565
• Accuracy: 0.72
• Precision: 0.6790
• Recall: 0.6770
• F1: 0.6766

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: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.0253	1.0	200	0.8974	0.605	0.7186	0.5341	0.4555
0.8121	2.0	400	0.8260	0.675	0.6792	0.6308	0.6112
0.6153	3.0	600	0.8504	0.66	0.6180	0.6026	0.6073
0.441	4.0	800	0.8917	0.685	0.6463	0.6385	0.6403
0.3273	5.0	1000	0.9384	0.69	0.6534	0.6602	0.6561
0.2138	6.0	1200	1.3501	0.705	0.6573	0.6374	0.6388
0.1435	7.0	1400	1.4614	0.71	0.6693	0.6553	0.6601
0.1202	8.0	1600	1.5825	0.7	0.6648	0.6592	0.6530
0.0587	9.0	1800	1.7755	0.72	0.6839	0.6849	0.6840
0.0237	10.0	2000	1.7240	0.735	0.6960	0.6924	0.6940
0.018	11.0	2200	1.7230	0.745	0.7105	0.7003	0.7026
0.0096	12.0	2400	1.7812	0.75	0.7225	0.7142	0.7158
0.006	13.0	2600	1.8223	0.75	0.7265	0.7082	0.7147
0.0033	14.0	2800	1.9872	0.76	0.7434	0.7107	0.7188
0.003	15.0	3000	1.8818	0.72	0.6778	0.6766	0.6765
0.0027	16.0	3200	1.9816	0.75	0.7125	0.6990	0.7043
0.002	17.0	3400	1.9268	0.725	0.6832	0.6834	0.6825
0.0023	18.0	3600	1.9456	0.73	0.6913	0.6898	0.6898
0.0025	19.0	3800	1.9543	0.72	0.6790	0.6770	0.6766
0.0016	20.0	4000	1.9565	0.72	0.6790	0.6770	0.6766

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Tokenizers 0.13.2