
# roberta-base-thai-char-upos

## Model Description

This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from [roberta-base-thai-char](https://huggingface.co/KoichiYasuoka/roberta-base-thai-char). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).

## How to Use

```py
import torch
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-thai-char-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-base-thai-char-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)))
```

or

```
import esupar
nlp=esupar.load("KoichiYasuoka/roberta-base-thai-char-upos")
print(nlp("หลายหัวดีกว่าหัวเดียว"))
```

## See Also

[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models


{"language": ["th"], "license": "apache-2.0", "tags": ["thai", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u0e2b\u0e25\u0e32\u0e22\u0e2b\u0e31\u0e27\u0e14\u0e35\u0e01\u0e27\u0e48\u0e32\u0e2b\u0e31\u0e27\u0e40\u0e14\u0e35\u0e22\u0e27"}]}
transformers pytorch roberta token-classification thai pos wikipedia dependency-parsing th dataset:universal_dependencies license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #thai #pos #wikipedia #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# roberta-base-thai-char-upos

## Model Description

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

## How to Use



or



## See Also

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


# roberta-base-thai-char-upos ## Model Description

This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from roberta-base-thai-char. Every word is tagged by UPOS (Universal Part-Of-Speech). ## How to Use



or ## See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
TAGS
#transformers #pytorch #roberta #token-classification #thai #pos #wikipedia #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # roberta-base-thai-char-upos ## Model Description

This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from roberta-base-thai-char. Every word is tagged by UPOS (Universal Part-Of-Speech). ## How to Use



or ## See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
passage: TAGS
#transformers #pytorch #roberta #token-classification #thai #pos #wikipedia #dependency-parsing #th #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# roberta-base-thai-char-upos## Model Description

This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from roberta-base-thai-char. Every word is tagged by UPOS (Universal Part-Of-Speech).## How to Use



or## See Also

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