---
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