Model description

This model is train on thai UD Thai PUD corpus with Universal Part-of-speech (UPOS) tag to help with pos tagging in Thai language.

Example

python

from transformers import AutoModelForTokenClassification, AutoTokenizer, TokenClassificationPipeline

model = AutoModelForTokenClassification.from_pretrained("Pavarissy/wangchanberta-ud-thai-pud-upos")
tokenizer = AutoTokenizer.from_pretrained("Pavarissy/wangchanberta-ud-thai-pud-upos")

pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer, grouped_entities=True)
outputs = pipeline("ประเทศไทย อยู่ใน ทวีป เอเชีย")
print(outputs)
# [{'entity_group': 'NOUN', 'score': 0.419697, 'word': '', 'start': 0, 'end': 1}, {'entity_group': 'PROPN', 'score': 0.8809489, 'word': 'ประเทศไทย', 'start': 0, 'end': 9}, {'entity_group': 'VERB', 'score': 0.7754166, 'word': 'อยู่ใน', 'start': 9, 'end': 16}, {'entity_group': 'NOUN', 'score': 0.9976932, 'word': 'ทวีป', 'start': 16, 'end': 21}, {'entity_group': 'PROPN', 'score': 0.97770107, 'word': 'เอเชีย', 'start': 21, 'end': 28}]

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Macro avg precision	Macro avg recall	Macro avg f1	Weighted avg precision	Weighted avg recall	Weighted avg f1	Accuracy
No log	1.0	125	0.5563	0.8103	0.7235	0.7552	0.8574	0.8522	0.8495	0.8522
No log	2.0	250	0.2316	0.8701	0.8460	0.8564	0.9320	0.9315	0.9310	0.9315
No log	3.0	375	0.1635	0.8903	0.8729	0.8809	0.9511	0.9511	0.9508	0.9511
0.5782	4.0	500	0.1112	0.9037	0.8964	0.8998	0.9687	0.9685	0.9685	0.9685
0.5782	5.0	625	0.0860	0.9110	0.9050	0.9079	0.9752	0.9752	0.9751	0.9752
0.5782	6.0	750	0.0675	0.9160	0.9103	0.9131	0.9815	0.9814	0.9814	0.9814
0.5782	7.0	875	0.0588	0.9189	0.9138	0.9163	0.9839	0.9839	0.9839	0.9839
0.1073	8.0	1000	0.0514	0.9214	0.9155	0.9184	0.9858	0.9858	0.9858	0.9858
0.1073	9.0	1125	0.0463	0.9225	0.9171	0.9197	0.9877	0.9876	0.9876	0.9876
0.1073	10.0	1250	0.0442	0.9221	0.9178	0.9199	0.9883	0.9883	0.9883	0.9883

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1