Instructions to use radames/all-mpnet-base-v2-feature-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radames/all-mpnet-base-v2-feature-extraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="radames/all-mpnet-base-v2-feature-extraction")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("radames/all-mpnet-base-v2-feature-extraction") model = AutoModelForMaskedLM.from_pretrained("radames/all-mpnet-base-v2-feature-extraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Duplicate from sentence-transformers/all-mpnet-base-v2
Radamés Ajna +1: julien-c Co-authored-by: Julien Chaumond <julien-c@users.noreply.huggingface.co>401ec9f