Instructions to use hf-internal-testing/tiny-random-XLNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-XLNetModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLNetModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-XLNetModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2546685d4cbff4242170053997bd6b507828f2109d532e5e24e779947b19baaf
- Size of remote file:
- 4.4 MB
- SHA256:
- 6fbc97ab37207c4b25fa147f627579fca135581b13cae1d043637c2cbb85a0f4
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