# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-coptic-upos")
model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-coptic-upos")Quick Links
deberta-base-coptic-upos
Model Description
This is a DeBERTa(V2) model pre-trained with UD_Coptic for POS-tagging and dependency-parsing, derived from deberta-base-coptic. Every word is tagged by UPOS (Universal Part-Of-Speech).
How to Use
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-coptic-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-coptic-upos")
or
import esupar
nlp=esupar.load("KoichiYasuoka/deberta-base-coptic-upos")
See Also
esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
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Model tree for KoichiYasuoka/deberta-base-coptic-upos
Base model
KoichiYasuoka/deberta-base-coptic
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KoichiYasuoka/deberta-base-coptic-upos")