Sentence Similarity
sentence-transformers
Safetensors
qwen2
feature-extraction
text-embeddings-inference
Instructions to use swankier/nomic-embed-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use swankier/nomic-embed-code with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("swankier/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 config_sentence_transformers.json
Browse files
config_sentence_transformers.json
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"transformers": "4.45.2",
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"pytorch": "2.4.1+cu121"
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},
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"prompts": {
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"default_prompt_name": null,
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"similarity_fn_name": "cosine"
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}
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"transformers": "4.45.2",
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"pytorch": "2.4.1+cu121"
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},
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"prompts": {
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"query": "Represent this query for searching relevant code: "
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},
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"default_prompt_name": null,
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"similarity_fn_name": "cosine"
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}
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