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malaysia-news-classification-bert-english
YagiASAFAS/malaysia-news-classification-bert-english
malaysia-news-classification-bert-english 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 14, 2024
- Updated
- Apr 21, 2024
- SHA
- 9ec48945
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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-english-skewness-fixed
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. 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: 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 | 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
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Apr 14, 2024
- Updated
- Apr 21, 2024