Instructions to use hf-tiny-model-private/tiny-random-LongformerForMaskedLM 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-LongformerForMaskedLM 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-LongformerForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ccf2c0fb94b39d9c443ab81f329d54b6ce6b16215cf12bd6cce6e9d466c62c6
- Size of remote file:
- 421 kB
- SHA256:
- 965558b10e953cb9b9c64416019dc3235092fe2161ae16c32403f7620c51455e
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