Instructions to use BernardJoshua/text-to-sql-spacy-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use BernardJoshua/text-to-sql-spacy-ner with spaCy:
!pip install https://huggingface.co/BernardJoshua/text-to-sql-spacy-ner/resolve/main/text-to-sql-spacy-ner-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("text-to-sql-spacy-ner") # Importing as module. import text-to-sql-spacy-ner nlp = text-to-sql-spacy-ner.load() - Notebooks
- Google Colab
- Kaggle
Download ner/model from BernardJoshua/text-to-sql-spacy-ner: direct link, hf CLI and curl.
- Browser
- Download file 3.84 MB
-
https://huggingface.co/BernardJoshua/text-to-sql-spacy-ner/resolve/main/ner/model
- Command line
-
hf download hf://BernardJoshua/text-to-sql-spacy-ner/ner/model
-
curl -L -o model https://huggingface.co/BernardJoshua/text-to-sql-spacy-ner/resolve/main/ner/model
3.84 MB
- Xet hash:
- 669eea0b66e51100633b0e9620cdb4f0fd61cec719d0e4ca1ae89eaf245e24b3
- Size of remote file:
- 3.84 MB
- SHA256:
- f08d8ef5d7d5165049a31ff9c5ddb390ba506ea1089d86a846fa162435429be3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.