Feature Extraction
sentence-transformers
Safetensors
English
modernbert
sentence-similarity
information-retrieval
code-search
code-embedding
dense-retrieval
Generated from Trainer
dataset_size:4073472
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Shuu12121/NightJar-CodeSearch-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Shuu12121/NightJar-CodeSearch-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Shuu12121/NightJar-CodeSearch-Embedding") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 090275b88b6dbe3bf780df820b9a41708a87769c215ef9899621619782d447c1
- Size of remote file:
- 438 MB
- SHA256:
- 9e4b5d2ce23636ca8ab75c1f4c66da70609a7238f291bf12314708526f61232c
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