universal-dependencies/universal_dependencies
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How to use KoichiYasuoka/roberta-classical-chinese-large-upos with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="KoichiYasuoka/roberta-classical-chinese-large-upos") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-large-upos")
model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-classical-chinese-large-upos")This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from roberta-classical-chinese-large-char. Every word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-large-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-classical-chinese-large-upos")
or
import esupar
nlp=esupar.load("KoichiYasuoka/roberta-classical-chinese-large-upos")
Koichi Yasuoka: Universal Dependencies Treebank of the Four Books in Classical Chinese, DADH2019: 10th International Conference of Digital Archives and Digital Humanities (December 2019), pp.20-28.
esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
Base model
ethanyt/guwenbert-large