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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 072ebba7acfe972dff69266ff59f36207f31891da2b871c6036a42ae1fd97e15
- Size of remote file:
- 4.38 MB
- SHA256:
- 7ca2d460e9e019909a8f1852c575efea9784ffa53d7e92ab46f771b33a799568
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