Text Classification
setfit
ONNX
attention-weights
context-compression
intent-classification
multilingual
Instructions to use naranor/SetFit-Multilingual-ONNX-Router-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use naranor/SetFit-Multilingual-ONNX-Router-V1 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("naranor/SetFit-Multilingual-ONNX-Router-V1") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download model.onnx.data from naranor/SetFit-Multilingual-ONNX-Router-V1: direct link, hf CLI and curl.
- Browser
- Download file 470 MB
-
https://huggingface.co/naranor/SetFit-Multilingual-ONNX-Router-V1/resolve/main/model.onnx.data
- Command line
-
hf download hf://naranor/SetFit-Multilingual-ONNX-Router-V1/model.onnx.data
-
curl -L -o model.onnx.data https://huggingface.co/naranor/SetFit-Multilingual-ONNX-Router-V1/resolve/main/model.onnx.data
470 MB
- Xet hash:
- 5c13a1fc7ab9fd14d52c7fbdd9549f864f12c98ee993f50a0ce02106bea9ace9
- Size of remote file:
- 470 MB
- SHA256:
- dceef97beff1382e8880bbd42c1e182980e4b0b000eadc40ffad67c3b530bbed
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