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:
- ea6a2fbf368cac6675e84a4aade58d1fb68c52022cd71f9725a4c0b8b4304eec
- Size of remote file:
- 242 kB
- SHA256:
- 14478412faa9cfe39a1a77ddf447f2466ae5eabc3d389d73e2444cf47001e260
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