Instructions to use jcorrie/en_extract_definitions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use jcorrie/en_extract_definitions with spaCy:
!pip install https://huggingface.co/jcorrie/en_extract_definitions/resolve/main/en_extract_definitions-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_extract_definitions") # Importing as module. import en_extract_definitions nlp = en_extract_definitions.load() - Notebooks
- Google Colab
- Kaggle
Download tok2vec/model from jcorrie/en_extract_definitions: direct link, hf CLI and curl.
- Browser
- Download file 6.5 MB
-
https://huggingface.co/jcorrie/en_extract_definitions/resolve/main/tok2vec/model
- Command line
-
hf download hf://jcorrie/en_extract_definitions/tok2vec/model
-
curl -L -o model https://huggingface.co/jcorrie/en_extract_definitions/resolve/main/tok2vec/model
6.5 MB
- Xet hash:
- ad71b0d6c6d8ede7459794af1addc33289a98d4d3b64fc0a368558319a4d2073
- Size of remote file:
- 6.5 MB
- SHA256:
- 6e9e8dbfb491bbd35fb51bdf07ef167208e336f2cebde3223bc004a8f2e52cb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.