Instructions to use hf-internal-testing/tiny-random-LukeForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LukeForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-LukeForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LukeForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-LukeForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06762eb022558d03da864c519dccf2a019ea04a87f857d0e1abb02939d482f95
- Size of remote file:
- 6.78 MB
- SHA256:
- a96ffb1713bb9fe886b3839f2f019553a61eea3654847b263b0ea434d352ea36
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