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:
- b94d331f00bb0edbb41d22ab5c4740319d812420df87b30656d41128f7436693
- Size of remote file:
- 960 kB
- SHA256:
- e941395c6ed45a48fdb47b424aa6765aa03b9d2b0be3666b4089e79f4093699b
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