Instructions to use hf-tiny-model-private/tiny-random-ReformerForMaskedLM 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-ReformerForMaskedLM 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-ReformerForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ReformerForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-ReformerForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38b635fbf00da88a167c8e0fba37849de11d839b2339db6b5e4641fc4aaf39b4
- Size of remote file:
- 432 kB
- SHA256:
- f2d36459f3a692b6b35b3b1990acaf7ad064c199591b55801de81c041aef04d9
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