This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, 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"))["logits"],dim=2)[0].tolist()[1:-1]]
print(list(zip(t,p)))
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roberta-base-thai-syllable-upos is an open source model from GitHub that offers a free installation service, and any user can find roberta-base-thai-syllable-upos on GitHub to install. At the same time, huggingface.co provides the effect of roberta-base-thai-syllable-upos install, users can directly use roberta-base-thai-syllable-upos installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
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