YagiASAFAS/malaysia-news-classification-bert-malay-skewness-fixed

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malaysia-news-classification-bert-malay-skewness-fixed

This model is a fine-tuned version of bert-base-multilingual-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: 1.0191
• —Accuracy: 0.7277

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
• —trainbatchsize: 16
• —evalbatchsize: 32
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 64
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 10
• —mixedprecisiontraining: 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

Framework versions

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