Instructions to use kenkwon/vi_ner_task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use kenkwon/vi_ner_task with spaCy:
!pip install https://huggingface.co/kenkwon/vi_ner_task/resolve/main/vi_ner_task-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("vi_ner_task") # Importing as module. import vi_ner_task nlp = vi_ner_task.load() - Notebooks
- Google Colab
- Kaggle
Download ner/moves from kenkwon/vi_ner_task: direct link, hf CLI and curl.
- Browser
- Download file 259 Bytes
-
https://huggingface.co/kenkwon/vi_ner_task/resolve/main/ner/moves
- Command line
-
hf download hf://kenkwon/vi_ner_task/ner/moves
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curl -L -o moves https://huggingface.co/kenkwon/vi_ner_task/resolve/main/ner/moves
259 Bytes
| ��moves��{"0":{},"1":{"TASK":3050,"DATE":987,"TIME":783,"PERSON":405},"2":{"TASK":3050,"DATE":987,"TIME":783,"PERSON":405},"3":{"TASK":3050,"DATE":987,"TIME":783,"PERSON":405},"4":{"TASK":3050,"DATE":987,"TIME":783,"PERSON":405,"":1},"5":{"":1}}�cfg��neg_key� |