Instructions to use vgarg/query_type_classifier_int-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vgarg/query_type_classifier_int-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vgarg/query_type_classifier_int-e5-large") 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 vgarg/query_type_classifier_int-e5-large with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("vgarg/query_type_classifier_int-e5-large") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from vgarg/query_type_classifier_int-e5-large: direct link, hf CLI and curl.
- Browser
- Download file 5.07 MB
-
https://huggingface.co/vgarg/query_type_classifier_int-e5-large/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://vgarg/query_type_classifier_int-e5-large/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/vgarg/query_type_classifier_int-e5-large/resolve/main/sentencepiece.bpe.model
5.07 MB
- Xet hash:
- 8b03b9e079abc849bdd27d0942fa6a77f9e7836db188512be97e4b3d52f415a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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