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---
language: lo
tags:
- lao-roberta-base-pos-tagger
license: mit
widget:
- text: "ຮ້ອງ ມ່ວນ ແທ້ ສຽງດີ ອິຫຼີ"
---
## Lao RoBERTa Base POS Tagger
Lao RoBERTa Base POS Tagger is a part-of-speech token-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Lao RoBERTa Base](https://huggingface.co/w11wo/lao-roberta-base) model, which is then fine-tuned on the [`Yunshan Cup 2020`](https://github.com/GKLMIP/Yunshan-Cup-2020) dataset consisting of tag-labelled Lao corpus.
After training, the model achieved an evaluation accuracy of 83.14%. On the benchmark test set, the model achieved an accuracy of 83.30%.
Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ----------------------------- | ------- | ------------ | ------------------------------- |
| `lao-roberta-base-pos-tagger` | 124M | RoBERTa Base | `Yunshan Cup 2020` |
## Evaluation Results
The model was trained for 15 epochs, with a batch size of 8, a learning rate of 5e-5, with cosine annealing to 0. The best model was loaded at the end.
| Epoch | Training Loss | Validation Loss | Accuracy |
| ----- | ------------- | --------------- | -------- |
| 1 | 1.026100 | 0.733780 | 0.746021 |
| 2 | 0.646900 | 0.659625 | 0.775688 |
| 3 | 0.500400 | 0.576214 | 0.798523 |
| 4 | 0.385400 | 0.606503 | 0.805269 |
| 5 | 0.288000 | 0.652493 | 0.809092 |
| 6 | 0.204600 | 0.671678 | 0.815216 |
| 7 | 0.145200 | 0.704693 | 0.818209 |
| 8 | 0.098700 | 0.830561 | 0.816998 |
| 9 | 0.066100 | 0.883329 | 0.825232 |
| 10 | 0.043900 | 0.933347 | 0.825664 |
| 11 | 0.027200 | 0.992055 | 0.828449 |
| 12 | 0.017300 | 1.054874 | 0.830819 |
| 13 | 0.011500 | 1.081638 | 0.830940 |
| 14 | 0.008500 | 1.094252 | 0.831304 |
| 15 | 0.007400 | 1.097428 | 0.831442 |
## How to Use
### As Token Classifier
```python
from transformers import pipeline
pretrained_name = "w11wo/lao-roberta-base-pos-tagger"
nlp = pipeline(
"token-classification",
model=pretrained_name,
tokenizer=pretrained_name
)
nlp("ຮ້ອງ ມ່ວນ ແທ້ ສຽງດີ ອິຫຼີ")
```
## Disclaimer
Do consider the biases which come from both the pre-trained RoBERTa model and the `Yunshan Cup 2020` dataset that may be carried over into the results of this model.
## Author
Lao RoBERTa Base POS Tagger was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access.
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