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:
- a977847b18cbca35dc2222988e6e6ba1b45661c0f82aaf3d84849ef12161b8a0
- Size of remote file:
- 4.38 MB
- SHA256:
- 9708ed3a4e14613439c5f3674f45f19b424bd1fd639e6910b495fbaf4739ea99
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