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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af078f2f5823596f1985cf9d045a41bf25888b336e8d8e49ab335d42dc7b98a4
- Size of remote file:
- 2.92 MB
- SHA256:
- 34ea1a465b65d78634cf1e061766faa5056ef22d5e6ab47f9c25d6fa7f3a018f
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