universal-dependencies/universal_dependencies
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How to use KoichiYasuoka/deberta-base-korean-morph-upos with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="KoichiYasuoka/deberta-base-korean-morph-upos") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-korean-morph-upos")
model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-korean-morph-upos")This is a DeBERTa(V3) model pre-trained on Korean texts for POS-tagging and dependency-parsing, derived from deberta-v3-base-korean and morphUD-korean. Every morpheme (형태소) is tagged by UPOS(Universal Part-Of-Speech).
from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-korean-morph-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-korean-morph-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
import esupar
nlp=esupar.load("KoichiYasuoka/deberta-base-korean-morph-upos")
print(nlp("홍시 맛이 나서 홍시라 생각한다."))
esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
Base model
team-lucid/deberta-v3-base-korean