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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc282da070f83ced27152d1b04ff35c0eb323be1c328ca5e10d78ee6e2999bcc
- Size of remote file:
- 775 kB
- SHA256:
- 337cf9fd5209acc6c7fa581e517e512ca5a4165a1ed7bf612f4f19b67ffd7b76
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