malaysia-news-classification-bert-english-skewness-fixed

This model is a fine-tuned version of bert-base-uncased on tnwei/ms-newspapers dataset. It is a fixed version of YagiASAFAS/malaysia-news-classification-bert-english, which fixed the skewness of imbalanced distribution among categories. It achieves the following results on the evaluation set:

• Loss: 1.2051
• Accuracy: 0.8436

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: 8
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 16
• mixed_precision_training: Native AMP

Label Mappings

This model can predict the following labels:

• 0: Election
• 1: Political Issue
• 2: Corruption
• 3: Democracy
• 4: Economic Growth
• 5: Economic Disparity
• 6: Economic Subsidy
• 7: Ethnic Discrimination
• 8: Ethnic Relation
• 9: Ethnic Culture
• 10: Religious Issue
• 11: Business and Finance
• 12: Sport
• 13: Food
• 14: Entertainment
• 15: Environmental Issue
• 16: Domestic News
• 17: World News

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	358	0.9357	0.7486
1.3554	2.0	716	0.9041	0.7807
0.4851	3.0	1074	0.7842	0.8282
0.4851	4.0	1432	0.9478	0.8226
0.2558	5.0	1790	1.0765	0.8282
0.1084	6.0	2148	1.1310	0.8380
0.0625	7.0	2506	1.0999	0.8464
0.0625	8.0	2864	1.1391	0.8408
0.0301	9.0	3222	1.1036	0.8506
0.0171	10.0	3580	1.0765	0.8534
0.0171	11.0	3938	1.1291	0.8506
0.0129	12.0	4296	1.1360	0.8520
0.0035	13.0	4654	1.1619	0.8450
0.0039	14.0	5012	1.1727	0.8534
0.0039	15.0	5370	1.2079	0.8408
0.0031	16.0	5728	1.2051	0.8436

Framework versions

• Transformers 4.18.0
• Pytorch 2.2.1+cu121
• Datasets 2.19.0
• Tokenizers 0.12.1