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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e2c38392313744bc7029b3f3a93d9107674a9a5df4163c2c64574defb6230cc
- Size of remote file:
- 4.24 MB
- SHA256:
- f70be5646f1f12016cd40a86e4f844514cfa8eea633b610e97b32c8129a6271f
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