spaCy
English
b2b_ecommerce_ner
named-entity-recognition
b2b
ecommerce
order-processing
product-extraction
Eval Results (legacy)
Instructions to use Purva17/b2b-ecomm-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Purva17/b2b-ecomm-ner with spaCy:
!pip install https://huggingface.co/Purva17/b2b-ecomm-ner/resolve/main/b2b-ecomm-ner-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("b2b-ecomm-ner") # Importing as module. import b2b-ecomm-ner nlp = b2b-ecomm-ner.load() - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 258c6058553476171053c86c4936ec15fd859a395260f215d8daeb08f6d7fcd8
- Size of remote file:
- 6.27 MB
- SHA256:
- e84fc06eb319c94d28e460fc334e292120b01f18baa5dc8b50c977459820a090
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