Instructions to use Shankhdhar/classifier_onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shankhdhar/classifier_onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Shankhdhar/classifier_onnx")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Shankhdhar/classifier_onnx") model = AutoModel.from_pretrained("Shankhdhar/classifier_onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_optimized.onnx from Shankhdhar/classifier_onnx: direct link, hf CLI and curl.
- Browser
- Download file 218 MB
-
https://huggingface.co/Shankhdhar/classifier_onnx/resolve/main/model_optimized.onnx
- Command line
-
hf download hf://Shankhdhar/classifier_onnx/model_optimized.onnx
-
curl -L -o model_optimized.onnx https://huggingface.co/Shankhdhar/classifier_onnx/resolve/main/model_optimized.onnx
218 MB
- Xet hash:
- 9c61991739313ce78916765b6abf82e3a4e033561a288422034b619665b79e19
- Size of remote file:
- 218 MB
- SHA256:
- 8d2ddba485cb961d49353e87b66e6ec9544141557b2076a7c50f0cc82af0bc94
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