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
Commit History
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Adds missing tokenizer configuration file (#4)
Verified signature- Adds tokenizer_config.json file (2a893b2bed780a8bcf1ab4075edffb59453bba46)6996ce1
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (9c5d8ad72883f39ab85c2a7a461271f5d549f6fe) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>a51acd9
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update weight
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delete tf weight
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submit tf 2.0 model
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submit pytorch model
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submit vocab, config
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initial commit
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