Instructions to use hf-internal-testing/tiny-random-FNetForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FNetForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-FNetForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FNetForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-FNetForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd46c5c93b23d4879564f4d21d3ebf5e8461c947b9653247dfbed9ca63eb1f9b
- Size of remote file:
- 4.23 MB
- SHA256:
- 13b15401d47e18af86cdae23333fb144db1d9725e95210770057bdaa34bcad88
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