Instructions to use hf-tiny-model-private/tiny-random-NezhaForMaskedLM 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-NezhaForMaskedLM 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-NezhaForMaskedLM")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-NezhaForMaskedLM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d54360aa5bb8d98b06fbb543320af8874808abae9969055921d146afb587d9b6
- Size of remote file:
- 2.94 MB
- SHA256:
- d2e7909798b7149f7f550267a661975a9842526098c13f349a82c16eb3e6f37b
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