---
library_name: transformers
license: apache-2.0
language:
- km
pipeline_tag: fill-mask
---

# XLMRoBERTa for Khmer Language

Training from scratch using **Masked Language Modeling** task on 5M Khmer sentences or 162M words or 578K unique words for 1M steps.

Training data is created by crawling publicly available publicly news sites and Wikipedia.


## Why?

1. [xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) is big. (279M parameters, while this is only 49M parameters).
2. [xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) is not optimized for Khmer language.
3. [xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) Vocab size is bigger (250,002) and this model uses 8000 vocab size.

## Usage


```python
from transformers import pipeline

pipe = pipeline("fill-mask", "seanghay/xlm-roberta-khmer-small")

result = pipe("សួស្ដីកម្ពុ<mask>!")
print(result)
```

```python
[
  {"score": 0.8130345344543457, "token": 11, "token_str": "ជា", "sequence": "សួស្ដីកម្ពុជា!"},
  {"score": 0.17512884736061096, "token": 160, "token_str": "ជ", "sequence": "សួស្ដីកម្ពុជ!"},
  {"score": 0.0034702506382018328, "token": 143, "token_str": "ជា", "sequence": "សួស្ដីកម្ពុ ជា!"},
  {"score": 0.00305828545242548, "token": 16, "token_str": "រ", "sequence": "សួស្ដីកម្ពុរ!"},
  {"score": 0.0007526700501330197, "token": 133, "token_str": "គ", "sequence": "សួស្ដីកម្ពុគ!"},
]
```

## License

`Apache-2.0`

## Citation

No need. :)