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:
- d811d68901e6e1a0365659ba26bc3c25b9042bdc9c0ff662974add173a938e9b
- Size of remote file:
- 4.45 MB
- SHA256:
- a84c3e3a94c6c123a269e55d2a3b65341d8ef77a9cbaf7cc9231d86481159aa3
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