Instructions to use nitinyadav/continual_learning_ner_task2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use nitinyadav/continual_learning_ner_task2 with spaCy:
!pip install https://huggingface.co/nitinyadav/continual_learning_ner_task2/resolve/main/continual_learning_ner_task2-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("continual_learning_ner_task2") # Importing as module. import continual_learning_ner_task2 nlp = continual_learning_ner_task2.load() - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9bd718a480bfec18a9211672ec6891e8a35a610497a26dad4fb8c7f42e39e67
- Size of remote file:
- 3.84 MB
- SHA256:
- 6befdd20af5904c9735f255d41f87620ea3ec8db3c014b016fb11fc74434c1a5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.