Instructions to use hf-tiny-model-private/tiny-random-XLNetForTokenClassification 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-XLNetForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-XLNetForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLNetForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-XLNetForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf2a341976e0ca0c90c8a6a2f76eae5c6af6b20a28e1018d2ac4baaa7316c2ec
- Size of remote file:
- 4.4 MB
- SHA256:
- ce043830ffa14c1ebb08af6c4331c487912a80291d8da6a0b5bd4a5797fa0b9b
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