Instructions to use KoichiYasuoka/deberta-base-coptic-upos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/deberta-base-coptic-upos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KoichiYasuoka/deberta-base-coptic-upos")# 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") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- cop
tags:
- coptic
- token-classification
- pos
- dependency-parsing
base_model: KoichiYasuoka/deberta-base-coptic
datasets:
- universal_dependencies
license: cc-by-sa-4.0
pipeline_tag: token-classification
widget:
- text: ⲧⲉⲛⲟⲩⲇⲉⲛ̄ⲟⲩⲟⲉⲓⲛϩ︤ⲙ︥ⲡϫⲟⲉⲓⲥ·
- text: ⲙⲟⲟϣⲉϩⲱⲥϣⲏⲣⲉⲙ̄ⲡⲟⲩⲟⲉⲓⲛ·
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