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:
- 1864cab9d016291a07426820e4c1c55cbdbf11f334466157f7fcff7869b53840
- Size of remote file:
- 4.46 MB
- SHA256:
- 6a05dda959527dee9c6a028ddd9ab73c2e3005d8d977a6682cde41318e3e8cb6
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