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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e5d07101a21be1d75e3178e99f8500655466b0d4fbd743d521dd1c4a87d5134
- Size of remote file:
- 4.24 MB
- SHA256:
- 1883127507801ec5ba927d4fbc770f7b4273cc3123c2a6b79318dfd86ffaf452
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