phobert-large-vsmec-5class

This model is a fine-tuned version of vinai/phobert-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.3865
• Accuracy: 0.6735
• F1 Weighted: 0.6684
• F1 Macro: 0.6619

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: 32
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 0.2
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Weighted	F1 Macro
1.6386	1.0	359	1.6093	0.2726	0.2867	0.2451
1.3278	2.0	718	1.2181	0.5394	0.5385	0.5015
1.1186	3.0	1077	1.0814	0.5816	0.5855	0.5645
1.0724	4.0	1436	1.0846	0.6210	0.6168	0.5964
0.8941	5.0	1795	1.1514	0.6370	0.6305	0.6197
0.8185	6.0	2154	1.1291	0.6706	0.6736	0.6518
0.7954	7.0	2513	1.1931	0.6458	0.6448	0.6198
0.7019	8.0	2872	1.2620	0.6560	0.6583	0.6475
0.6637	9.0	3231	1.3124	0.6574	0.6591	0.6320
0.6326	10.0	3590	1.3566	0.6691	0.6657	0.6534
0.6074	11.0	3949	1.3491	0.6603	0.6643	0.6454
0.6072	12.0	4308	1.3870	0.6735	0.6684	0.6619
0.5517	13.0	4667	1.4099	0.6560	0.6514	0.6366
0.6018	14.0	5026	1.4134	0.6633	0.6653	0.6545
0.5688	15.0	5385	1.4302	0.6676	0.6678	0.6507
0.5826	16.0	5744	1.4399	0.6560	0.6562	0.6301
0.5909	17.0	6103	1.4440	0.6487	0.6504	0.6183

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.8.5
• Tokenizers 0.22.2