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malaysia-news-classification-bert-malay-skewness-fixed
YagiASAFAS/malaysia-news-classification-bert-malay-skewness-fixed
malaysia-news-classification-bert-malay-skewness-fixed is a text classification model from YagiASAFAS. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Apr 21, 2024
- Updated
- Apr 21, 2024
- SHA
- a99889fc
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
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
- 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: Election1: Political Issue2: Corruption3: Democracy4: Economic Growth5: Economic Disparity6: Economic Subsidy7: Ethnic Discrimination8: Ethnic Relation9: Ethnic Culture10: Religious Issue11: Business and Finance12: Sport13: Food14: Entertainment15: Environmental Issue16: Domestic News17: World News
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.98 | 44 | 2.0942 | 0.4525 |
| No log | 1.98 | 88 | 1.5309 | 0.6103 |
| No log | 2.98 | 132 | 1.2585 | 0.6774 |
| No log | 3.98 | 176 | 1.1239 | 0.6955 |
| No log | 4.98 | 220 | 1.0726 | 0.7165 |
| No log | 5.98 | 264 | 1.0592 | 0.7151 |
| No log | 6.98 | 308 | 1.0330 | 0.7221 |
| No log | 7.98 | 352 | 1.0473 | 0.7123 |
| No log | 8.98 | 396 | 1.0356 | 0.7207 |
| No log | 9.98 | 440 | 1.0191 | 0.7277 |
Framework versions
- Transformers 4.18.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Apr 21, 2024
- Updated
- Apr 21, 2024