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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c40fac03b13d8fd56d92e4cb8cfcaf3ee2983f56d854d61a0d7980fded86ade
- Size of remote file:
- 4.38 MB
- SHA256:
- 2747780c8f719623c55671d9d8f767fabee9de16028aba96f4c1e7f7b34ffe18
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