Feature Extraction
sentence-transformers
PyTorch
English
mpnet
sentence-similarity
text-embeddings-inference
Instructions to use allberto/all-mpnet-base-v2-feature-extraction-pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use allberto/all-mpnet-base-v2-feature-extraction-pipeline with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("allberto/all-mpnet-base-v2-feature-extraction-pipeline") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
all-mpnet-base-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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