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") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download model_head.pkl from Shankhdhar/ecommerce_query_classifier: direct link, hf CLI and curl.
- Browser
- Download file 31.6 kB
-
https://huggingface.co/Shankhdhar/ecommerce_query_classifier/resolve/main/model_head.pkl
- Command line
-
hf download hf://Shankhdhar/ecommerce_query_classifier/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/Shankhdhar/ecommerce_query_classifier/resolve/main/model_head.pkl
31.6 kB
- Xet hash:
- cde87c11b2828d45a7735f278b08654120ca045055b763da72f03d7a3868e9d1
- Size of remote file:
- 31.6 kB
- SHA256:
- 8819567dff5e39129e4a9b4bc61fe0da7347f1c54ce7493d19b4d6991ef2aec6
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