Instructions to use kimnt93/query-cls-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kimnt93/query-cls-ft with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kimnt93/query-cls-ft") 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 kimnt93/query-cls-ft with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("kimnt93/query-cls-ft") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kimnt93/query-cls-ft: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/kimnt93/query-cls-ft/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kimnt93/query-cls-ft/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kimnt93/query-cls-ft/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 82e55bed6ccf28f14b95751224c5ed8410f557f7f687a62c41bd63439e2bb242
- Size of remote file:
- 1.11 GB
- SHA256:
- d53b36610d1c6e0c310d40f656fdc5d58f7ed2157f0537b9dd2f8930d7891c6f
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