README.md

---
language: ms
license: apache-2.0
tags:
- sentiment-analysis
- malay
- bert
- text-classification
datasets:
- custom
metrics:
- accuracy
- f1
model-index:
- name: ft-Malay-bert
  results:
  - task:
      type: text-classification
      name: Sentiment Analysis
    dataset:
      type: custom
      name: Malay Sentiment Dataset
    metrics:
    - type: accuracy
      value: 0.85
      name: Accuracy
---

# Malay BERT for Sentiment Analysis

Fine-tuned BERT model for Malay sentiment analysis with 3-class classification.

## Label Mapping

**Important**: This model uses the following label mapping:

```python
id2label = {
    0: "negative",
    1: "neutral", 
    2: "positive"
}

label2id = {
    "negative": 0,
    "neutral": 1,
    "positive": 2
}
```

## Quick Usage

```python
from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="rmtariq/ft-Malay-bert")
result = classifier("Saya sangat gembira!")
print(result)
# [{'label': 'LABEL_2', 'score': 0.995}]
# LABEL_2 = positive
```

## Label Interpretation

- `LABEL_0` or `0` → **negative** sentiment
- `LABEL_1` or `1` → **neutral** sentiment  
- `LABEL_2` or `2` → **positive** sentiment

## Model Details

- **Language**: Malay (Bahasa Malaysia)
- **Task**: Sentiment Analysis
- **Classes**: 3 (negative, neutral, positive)
- **Base Model**: BERT

## Training

This model was fine-tuned on Malay sentiment analysis data.

## Limitations

- Optimized for Malaysian Malay text
- May have reduced performance on other Malay dialects
- Mixed language performance may vary

## Citation

```bibtex
@misc{ft-malay-bert,
  author = {rmtariq},
  title = {Fine-tuned Malay BERT for Sentiment Analysis},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/rmtariq/ft-Malay-bert}
}
```