Sentence Similarity
sentence-transformers
ONNX
English
code
bert
code-search
embeddings
LoRA
E5
cqs
Eval Results
text-embeddings-inference
Instructions to use jamie8johnson/e5-base-v2-code-search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jamie8johnson/e5-base-v2-code-search with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jamie8johnson/e5-base-v2-code-search") 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
fix: add shape inference (997 value_info entries) for CUDA EP compatibility
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model.onnx
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