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:
- 6cc2a45d9a4278ed390e7fa70aa6164b9c4015ab2406ac1c1bee2129c09e8765
- Size of remote file:
- 445 kB
- SHA256:
- 5b6d4ae7247588f361249b8c7eb71b4fb27ee7427515e9eda277f8aa9833c515
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