Sentence Similarity
sentence-transformers
Safetensors
qwen2
feature-extraction
text-embeddings-inference
Instructions to use nomic-ai/nomic-embed-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nomic-ai/nomic-embed-code with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nomic-ai/nomic-embed-code") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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from sentence_transformers import SentenceTransformer
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queries = ['Calculate the n-th factorial']
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model = SentenceTransformer("nomic-ai/nomic-embed-code")
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query_emb = model.encode(queries, prompt_name="query")
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from sentence_transformers import SentenceTransformer
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queries = ['Calculate the n-th factorial']
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code_snippets = ['def fact(n):\n if n < 0:\n raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
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model = SentenceTransformer("nomic-ai/nomic-embed-code")
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query_emb = model.encode(queries, prompt_name="query")
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