Instructions to use hf-tiny-model-private/tiny-random-FNetForTokenClassification 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-FNetForTokenClassification 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-FNetForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FNetForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-FNetForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d83ccfb540c3f2320cd85d14e6f3db8f045372b0e8d4a0d3b7f0d62350636368
- Size of remote file:
- 4.23 MB
- SHA256:
- 15acaf2cd62eb3425f883523044b6869f59f8a2a7f8450e717d2d5bab0f5dce7
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