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
Download tokenizer.json from swankier/nomic-embed-code: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/swankier/nomic-embed-code/resolve/main/tokenizer.json
- Command line
-
hf download hf://swankier/nomic-embed-code/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/swankier/nomic-embed-code/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- a7030cf2e58dead38199a68a8cd6f6f1a609a6072d7fb38ba5f85b3bb7e21557
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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