phobert-sentiment-analysis

This model is a fine-tuned version of vinai/phobert-base-v2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1279
• Accuracy: 0.9768
• F1 Positive: 0.9845
• F1 Neutral: 0.9723
• F1 Negative: 0.9738
• Macro F1: 0.9768
• Macro Precision: 0.9768
• Macro Recall: 0.9769

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Positive	F1 Neutral	F1 Negative	Macro F1	Macro Precision	Macro Recall
0.1902	1.0	1965	0.1841	0.9442	0.9573	0.9384	0.9365	0.9441	0.9434	0.9455
0.0958	2.0	3930	0.1998	0.9526	0.9633	0.9421	0.9526	0.9527	0.9526	0.9547
0.0925	3.0	5895	0.1155	0.9707	0.9777	0.9652	0.9696	0.9708	0.9706	0.9711
0.0219	4.0	7860	0.1154	0.9768	0.9818	0.9724	0.9767	0.9770	0.9770	0.9769
0.0079	5.0	9825	0.1279	0.9768	0.9845	0.9723	0.9738	0.9768	0.9768	0.9769

Framework versions

• Transformers 4.54.1
• Pytorch 2.6.0+cu124
• Datasets 4.0.0
• Tokenizers 0.21.4