roberta-base-thai-syllable-upos

Model Description

This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging, derived from roberta-base-thai-syllable. Every word is tagged by UPOS (Universal Part-Of-Speech).

How to Use

import torch
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-thai-syllable-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-base-thai-syllable-upos")
s="หลายหัวดีกว่าหัวเดียว"
t=tokenizer.tokenize(s)
p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))[0],dim=2)[0].tolist()[1:-1]]
print(list(zip(t,p)))

See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa models

New

Select AutoNLP in the “Train” menu to fine-tune this model automatically.

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Token Classification
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