Instructions to use hf-tiny-model-private/tiny-random-MPNetForMaskedLM 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-MPNetForMaskedLM 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-MPNetForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MPNetForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-MPNetForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1a352eda403662fe71a5f1c4bc54b8363f699af6d41c2b2f4833fe5d7e51005
- Size of remote file:
- 979 kB
- SHA256:
- 00e11060eab393bde6b5aa21d18b325f3486f5eed35d7a2c5be075224f5386c7
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