Instructions to use hf-tiny-model-private/tiny-random-FNetForMaskedLM 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-FNetForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-FNetForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FNetForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-FNetForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4a6127a12b15a06e211008baa540cde0d6d0b5b6bc0f797f266ae34a46168cb
- Size of remote file:
- 4.37 MB
- SHA256:
- 1a590787c9370d600c6b43ecd1412c4202bca5fa7609c2d393d567680667a531
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