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
| 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](https://universaldependencies.org/cop/) for POS-tagging and dependency-parsing, derived from [deberta-base-coptic](https://huggingface.co/KoichiYasuoka/deberta-base-coptic). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | |
| ## How to Use | |
| ```py | |
| 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](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models | |