Instructions to use andreids/en_textcat_services_rendered_in with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use andreids/en_textcat_services_rendered_in with spaCy:
!pip install https://huggingface.co/andreids/en_textcat_services_rendered_in/resolve/main/en_textcat_services_rendered_in-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_textcat_services_rendered_in") # Importing as module. import en_textcat_services_rendered_in nlp = en_textcat_services_rendered_in.load() - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ee4e7f1f3e512d59314951be58aa77352367f0a2b595b300136daeefab2c2c9
- Size of remote file:
- 4.8 MB
- SHA256:
- 58ae4d875624c8636efc61b126aa9abbd025205030db6af95216c812436ca229
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.