Instructions to use hf-internal-testing/tiny-random-XLNetForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLNetForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-XLNetForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLNetForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-XLNetForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b0fa90065dd85289893365b05658b1b34e2c4ad855f461ce4448b4f9ecf0f22
- Size of remote file:
- 4.38 MB
- SHA256:
- 1c4768324ef3eab681a6a5ccae38620d775a0721e024c503662b5add5ec07b90
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