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 model. The model was originally the pre-trained Lao RoBERTa Base model, which is then fine-tuned on the 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 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
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. All computation and development are done on Google Colaboratory using their free GPU access.