Feature Extraction
sentence-transformers
ONNX
English
bert
sentence-similarity
text-embeddings-inference
Instructions to use EmbeddedLLM/paraphrase-MiniLM-L3-v2-onnx-o3-cpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use EmbeddedLLM/paraphrase-MiniLM-L3-v2-onnx-o3-cpu with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EmbeddedLLM/paraphrase-MiniLM-L3-v2-onnx-o3-cpu") 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
Jia Huei Tan commited on
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Parent(s): 9370dad
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README.md
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license: apache-2.0
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# ONNX Conversion of [sentence-transformers/paraphrase-
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- ONNX model for CPU with O3 optimisation
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- This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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license: apache-2.0
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# ONNX Conversion of [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2)
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- ONNX model for CPU with O3 optimisation
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- This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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