Sentence Similarity
sentence-transformers
Safetensors
mistralbidirectional
swe-bench
code-similarity
code-retrieval
code-search
code-explanation
custom_code
Instructions to use nvidia/NV-EmbedCode-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nvidia/NV-EmbedCode-7b-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/NV-EmbedCode-7b-v1", trust_remote_code=True) 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
Atefeh Sohrabizadeh commited on
Commit ·
6340e30
1
Parent(s): cfff837
updated readme
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README.md
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## Introduction
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The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.
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---
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language: []
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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license: apache-2.0
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tags:
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- swe-bench
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- code-similarity
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- code-retrieval
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- code-search
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- code-explanation
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widget: []
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---
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## Introduction
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The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.
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