Instructions to use ntu-spml/distilhubert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ntu-spml/distilhubert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ntu-spml/distilhubert")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("ntu-spml/distilhubert") model = AutoModel.from_pretrained("ntu-spml/distilhubert", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ntu-spml/distilhubert: direct link, hf CLI and curl.
- Browser
- Download file 94 MB
-
https://huggingface.co/ntu-spml/distilhubert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ntu-spml/distilhubert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ntu-spml/distilhubert/resolve/main/pytorch_model.bin
94 MB
- Xet hash:
- 71d7b69bbab3a027f682c14987aa2176884cb412a5e343695db149f2f74149ff
- Size of remote file:
- 94 MB
- SHA256:
- 6d7fb982c1365c60aae4e1a7af329ec0bbe50aec47ffafa9647e17e375009de6
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