malaysia-news-classification-bert-malay – AI Model by YagiASAFAS | AlphaNeural AI

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YagiASAFAS/
malaysia-news-classification-bert-malay

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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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