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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: malaysia-news-classification-bert-english-skewness-fixed |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# malaysia-news-classification-bert-english-skewness-fixed |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on tnwei/ms-newspapers dataset. |
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It is a fixed version of YagiASAFAS/malaysia-news-classification-bert-english, which fixed the skewness of imbalanced distribution among categories. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2051 |
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- Accuracy: 0.8436 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 16 |
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- mixed_precision_training: Native AMP |
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## Label Mappings |
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This model can predict the following labels: |
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- `0`: Election |
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- `1`: Political Issue |
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- `2`: Corruption |
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- `3`: Democracy |
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- `4`: Economic Growth |
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- `5`: Economic Disparity |
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- `6`: Economic Subsidy |
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- `7`: Ethnic Discrimination |
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- `8`: Ethnic Relation |
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- `9`: Ethnic Culture |
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- `10`: Religious Issue |
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- `11`: Business and Finance |
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- `12`: Sport |
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- `13`: Food |
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- `14`: Entertainment |
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- `15`: Environmental Issue |
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- `16`: Domestic News |
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- `17`: World News |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 358 | 0.9357 | 0.7486 | |
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| 1.3554 | 2.0 | 716 | 0.9041 | 0.7807 | |
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| 0.4851 | 3.0 | 1074 | 0.7842 | 0.8282 | |
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| 0.4851 | 4.0 | 1432 | 0.9478 | 0.8226 | |
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| 0.2558 | 5.0 | 1790 | 1.0765 | 0.8282 | |
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| 0.1084 | 6.0 | 2148 | 1.1310 | 0.8380 | |
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| 0.0625 | 7.0 | 2506 | 1.0999 | 0.8464 | |
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| 0.0625 | 8.0 | 2864 | 1.1391 | 0.8408 | |
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| 0.0301 | 9.0 | 3222 | 1.1036 | 0.8506 | |
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| 0.0171 | 10.0 | 3580 | 1.0765 | 0.8534 | |
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| 0.0171 | 11.0 | 3938 | 1.1291 | 0.8506 | |
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| 0.0129 | 12.0 | 4296 | 1.1360 | 0.8520 | |
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| 0.0035 | 13.0 | 4654 | 1.1619 | 0.8450 | |
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| 0.0039 | 14.0 | 5012 | 1.1727 | 0.8534 | |
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| 0.0039 | 15.0 | 5370 | 1.2079 | 0.8408 | |
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| 0.0031 | 16.0 | 5728 | 1.2051 | 0.8436 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.12.1 |
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