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_quantized.onnx from naranor/SetFit-Multilingual-ONNX-Router-V1: direct link, hf CLI and curl.
- Browser
- Download file 119 MB
-
https://huggingface.co/naranor/SetFit-Multilingual-ONNX-Router-V1/resolve/main/model_quantized.onnx
- Command line
-
hf download hf://naranor/SetFit-Multilingual-ONNX-Router-V1/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/naranor/SetFit-Multilingual-ONNX-Router-V1/resolve/main/model_quantized.onnx
119 MB
- Xet hash:
- 46c60a3a5f0368b1a1f05e15fe715acd9afab24fb2d3d0ced1e6220cd9b7ae06
- Size of remote file:
- 119 MB
- SHA256:
- 9150bcebd64dfdcd9e65e0b56b6d202c1230e406785ab665066cf19f8d2b3d7b
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