|
--- |
|
language: |
|
- "ko" |
|
tags: |
|
- "korean" |
|
- "token-classification" |
|
- "pos" |
|
- "dependency-parsing" |
|
datasets: |
|
- "universal_dependencies" |
|
license: "apache-2.0" |
|
pipeline_tag: "token-classification" |
|
widget: |
|
- text: "홍시 맛이 나서 홍시라 생각한다." |
|
- text: "紅柹 맛이 나서 紅柹라 生覺한다." |
|
--- |
|
|
|
# deberta-base-korean-upos |
|
|
|
## Model Description |
|
|
|
This is a RoBERTa model pre-trained on Korean texts for POS-tagging and dependency-parsing, derived from [deberta-v3-base-korean](https://huggingface.co/team-lucid/deberta-v3-base-korean). Every word (어절) is tagged by [UPOS](https://universaldependencies.org/u/pos/)(Universal Part-Of-Speech). |
|
|
|
## How to Use |
|
|
|
```py |
|
from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline |
|
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-korean-upos") |
|
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-korean-upos") |
|
pipeline=TokenClassificationPipeline(tokenizer=tokenizer,model=model,aggregation_strategy="simple") |
|
nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)] |
|
print(nlp("홍시 맛이 나서 홍시라 생각한다.")) |
|
``` |
|
|
|
or |
|
|
|
```py |
|
import esupar |
|
nlp=esupar.load("KoichiYasuoka/deberta-base-korean-upos") |
|
print(nlp("홍시 맛이 나서 홍시라 생각한다.")) |
|
``` |
|
|
|
## See Also |
|
|
|
[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models |
|
|
