albert-base-ours-run-1

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: 2.3970
• Accuracy: 0.735
• Precision: 0.7033
• Recall: 0.6790
• F1: 0.6873

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
0.9719	1.0	200	0.8460	0.635	0.6534	0.5920	0.5547
0.7793	2.0	400	0.7762	0.675	0.6965	0.6323	0.5936
0.5734	3.0	600	0.8149	0.67	0.6200	0.6192	0.6196
0.3877	4.0	800	0.9555	0.7	0.6724	0.6482	0.6549
0.2426	5.0	1000	1.1248	0.695	0.6529	0.6437	0.6452
0.183	6.0	1200	1.3497	0.705	0.6717	0.6489	0.6563
0.1011	7.0	1400	1.6369	0.7	0.6620	0.6532	0.6560
0.0602	8.0	1600	1.8171	0.7	0.6763	0.6615	0.6654
0.0335	9.0	1800	1.9601	0.695	0.6640	0.6490	0.6545
0.0158	10.0	2000	2.0206	0.71	0.6802	0.6751	0.6768
0.0148	11.0	2200	2.0881	0.675	0.6252	0.6242	0.6232
0.0057	12.0	2400	2.2708	0.735	0.7146	0.6790	0.6904
0.0079	13.0	2600	2.2348	0.72	0.6917	0.6659	0.6746
0.0018	14.0	2800	2.2978	0.725	0.6968	0.6662	0.6761
0.0025	15.0	3000	2.3180	0.735	0.7067	0.6790	0.6883
0.0028	16.0	3200	2.3910	0.74	0.7153	0.6854	0.6953
0.0002	17.0	3400	2.3830	0.735	0.7033	0.6790	0.6873
0.0002	18.0	3600	2.3899	0.735	0.7033	0.6790	0.6873
0.0001	19.0	3800	2.3922	0.735	0.7033	0.6790	0.6873
0.0001	20.0	4000	2.3970	0.735	0.7033	0.6790	0.6873

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Tokenizers 0.13.2