Instructions to use microsoft/mpnet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/mpnet-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="microsoft/mpnet-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("microsoft/mpnet-base") model = AutoModelForMaskedLM.from_pretrained("microsoft/mpnet-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/mpnet-base: direct link, hf CLI and curl.
- Browser
- Download file 532 MB
-
https://huggingface.co/microsoft/mpnet-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/mpnet-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/mpnet-base/resolve/main/pytorch_model.bin
532 MB
- Xet hash:
- c0d97943e4efdd2c69c80ad66d24305e07e3c7648ddf869a8fa3fc37ac674014
- Size of remote file:
- 532 MB
- SHA256:
- 0a4bb0b65f1710348313a848d71e54303592d38b576351a547316c5df434a945
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