Instructions to use hf-tiny-model-private/tiny-random-LukeForTokenClassification 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-LukeForTokenClassification 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-LukeForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LukeForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-LukeForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48b0e04245ec5d33916963414f0f8e961f02be1db06fe4525413829e8aae2f7c
- Size of remote file:
- 6.81 MB
- SHA256:
- b661766cf11bcfcf3d955a0dad115c61c0e8849e4dec44e24f14ff8273e31fac
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