Instructions to use hf-tiny-model-private/tiny-random-XLNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-XLNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-XLNetModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-XLNetModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 477f0e5f586c8d42558388152d004dccabb855679e53fc1de43feb720864e665
- Size of remote file:
- 4.45 MB
- SHA256:
- 7fc33f6129f7de879393d1c667a838dabdda2704a75527ab0c1feae347e40dd8
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