Instructions to use Shankhdhar/ecommerce_query_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Shankhdhar/ecommerce_query_classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Shankhdhar/ecommerce_query_classifier") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - setfit
How to use Shankhdhar/ecommerce_query_classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("Shankhdhar/ecommerce_query_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9792838dbad1ee4f5a3a0a2099be31617eb432f194a0458b7d527ead5a1a5ec9
- Size of remote file:
- 438 MB
- SHA256:
- fd63c3ec1ebd1d9ce98bea924a592d98feb9e4f4fc60e9cd0ab2644fb2309e3c
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