Feature Extraction
sentence-transformers
ONNX
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
Download tokenizer.json from Shuu12121/NightJar-CodeSearch-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 3.52 MB
-
https://huggingface.co/Shuu12121/NightJar-CodeSearch-Embedding/resolve/main/tokenizer.json
- Command line
-
hf download hf://Shuu12121/NightJar-CodeSearch-Embedding/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Shuu12121/NightJar-CodeSearch-Embedding/resolve/main/tokenizer.json
3.52 MB
File too large to display, you can check the raw version instead.