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:
- 87e72db349be9b84fce2efe3598c2911129dd25afb8a18a62233a7abed92fbc8
- Size of remote file:
- 6.81 MB
- SHA256:
- c7241e4026e09703d15b9010bb99e04fb964602856c720943b985fee6ce43eba
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