Instructions to use Mahmoud8/mpnet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud8/mpnet-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud8/mpnet-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud8/mpnet-base") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud8/mpnet-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mahmoud8/mpnet-base: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Mahmoud8/mpnet-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mahmoud8/mpnet-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mahmoud8/mpnet-base/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- c02c81c28660b75d6bd06be676b88a78c29ca1a5dd93aa949055ac843073e874
- Size of remote file:
- 438 MB
- SHA256:
- 38ffd0e0a9cae0439b91e994c2fba2e966470363dca69ad7720067c6689cb837
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