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")# pip install -U transformers accelerate # 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
Commit History
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (42b1d1cbce6a86fef88a800c77e5406ce3b78ccb) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>fa87d96
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Update README.md
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Merge branch 'main' of https://huggingface.co/anton-l/distilhubert
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Modify weights
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initial commit
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