Instructions to use YakovElm/Apache15SetFitModel_clean_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use YakovElm/Apache15SetFitModel_clean_data with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("YakovElm/Apache15SetFitModel_clean_data") 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 YakovElm/Apache15SetFitModel_clean_data with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("YakovElm/Apache15SetFitModel_clean_data") 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 YakovElm/Apache15SetFitModel_clean_data: direct link, hf CLI and curl.
- Browser
- Download file 6.99 kB
-
https://huggingface.co/YakovElm/Apache15SetFitModel_clean_data/resolve/refs%2Fpr%2F1/model_head.pkl
- Command line
-
hf download hf://YakovElm/Apache15SetFitModel_clean_data@refs/pr/1/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/YakovElm/Apache15SetFitModel_clean_data/resolve/refs%2Fpr%2F1/model_head.pkl
6.99 kB
- Xet hash:
- 0d5f744a55c9312bbc22f6b8078d12880733c1b1afcaa7dc57e7819fcbc9f00d
- Size of remote file:
- 6.99 kB
- SHA256:
- bce3cf914e768eaf28609a442ea3876f0f20d1f58e92b74663c2dff9779b0a3b
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