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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: malaysia-news-classification-bert-english-skewness-fixed
results: []
---
<!-- 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](https://huggingface.co/bert-base-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: 1.2051
- 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`: 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 | 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
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