w11wo/lao-roberta-base-pos-tagger

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.