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
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During training, we employ a novel curriculum-based hard negative mining strategy to ensure the model learns from challenging examples. We use a softmax-based sampling strategy to progressively sample hard negatives with increasing difficulty over time.
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## Join the Nomic Community
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- Website: [https://nomic.ai](https://nomic.ai)
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- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
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- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
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During training, we employ a novel curriculum-based hard negative mining strategy to ensure the model learns from challenging examples. We use a softmax-based sampling strategy to progressively sample hard negatives with increasing difficulty over time.
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## Join the Nomic Community
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- Nomic Embed Ecosystem: [https://www.nomic.ai/embed](https://www.nomic.ai/embed)
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- Website: [https://nomic.ai](https://nomic.ai)
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- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
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- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
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