malaysia-news-classification-bert-malay

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

• Loss: 1.0311
• Accuracy: 0.7601

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: 3e-05
• train_batch_size: 16
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10
• 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	225	1.0295	0.7104
No log	2.0	450	0.9205	0.7409
1.1064	3.0	675	0.8432	0.7590
1.1064	4.0	900	0.8552	0.7695
0.5596	5.0	1125	0.8836	0.7612
0.5596	6.0	1350	0.9057	0.7665
0.3499	7.0	1575	0.9766	0.7590
0.3499	8.0	1800	0.9974	0.7640
0.2144	9.0	2025	1.0211	0.7612
0.2144	10.0	2250	1.0311	0.7601

Framework versions

• Transformers 4.18.0
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.12.1