Instructions to use devve1/Splade_PP_en_v2_onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devve1/Splade_PP_en_v2_onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="devve1/Splade_PP_en_v2_onnx")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("devve1/Splade_PP_en_v2_onnx") model = AutoModelForMaskedLM.from_pretrained("devve1/Splade_PP_en_v2_onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.onnx from devve1/Splade_PP_en_v2_onnx: direct link, hf CLI and curl.
- Browser
- Download file 532 MB
-
https://huggingface.co/devve1/Splade_PP_en_v2_onnx/resolve/main/model.onnx
- Command line
-
hf download hf://devve1/Splade_PP_en_v2_onnx/model.onnx
-
curl -L -o model.onnx https://huggingface.co/devve1/Splade_PP_en_v2_onnx/resolve/main/model.onnx
532 MB
- Xet hash:
- 6b4bcc6d954431e4a30d49e67b9d9ef6f46664177792493a96a5403fe87e07cf
- Size of remote file:
- 532 MB
- SHA256:
- a1135610adf9189c1037d76b1335214ce0aa7cfcb9fa8efee3d277df50e43818
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