Sentence Similarity
Transformers.js
ONNX
sentence-transformers
English
code
modernbert
feature-extraction
ColBERT
PyLate
Generated from Trainer
dataset_size:2117771
loss:Contrastive
embeddings
retrieval
code search
Eval Results (legacy)
text-embeddings-inference
Instructions to use aigentic/LateOn-Code-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use aigentic/LateOn-Code-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'aigentic/LateOn-Code-ONNX'); - sentence-transformers
How to use aigentic/LateOn-Code-ONNX with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="aigentic/LateOn-Code-ONNX") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Download onnx/model_quantized.onnx from aigentic/LateOn-Code-ONNX: direct link, hf CLI and curl.
- Browser
- Download file 150 MB
-
https://huggingface.co/aigentic/LateOn-Code-ONNX/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://aigentic/LateOn-Code-ONNX/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/aigentic/LateOn-Code-ONNX/resolve/main/onnx/model_quantized.onnx
150 MB
- Xet hash:
- 755c4058e8e5ff9a779f292721e79b6e8eb13dc87c0c3026a3bce1047fe3026a
- Size of remote file:
- 150 MB
- SHA256:
- ae394ba1a601fbfef05437c32dcfcad945c240fc14c085baf77f6e0583c18fc4
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