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:
- 204990e8bcfc46d0d43c8c9a0223b845ddb236c1d9ec4f33055d625f50462978
- Size of remote file:
- 1.51 MB
- SHA256:
- 8e367004bee7e4a9a104b847af21f1839b8929075dfa934263361c96544b8c11
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