Feature Extraction
Transformers.js
ONNX
GGUF
sentence-transformers
multilingual
qwen3
embedding
llama-cpp
jina-embeddings-v5
mteb
vllm
text-embeddings-inference
Instructions to use MaunikG/jina-embeddings-v5-text-small-clustering-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use MaunikG/jina-embeddings-v5-text-small-clustering-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'MaunikG/jina-embeddings-v5-text-small-clustering-ONNX'); - sentence-transformers
How to use MaunikG/jina-embeddings-v5-text-small-clustering-ONNX with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MaunikG/jina-embeddings-v5-text-small-clustering-ONNX") 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:
- 4a9c7c708bca725e56582754a902e75eb7c152ae4056ce415d4c4f27ea6cf43c
- Size of remote file:
- 2 Bytes
- SHA256:
- 7648eb81c4e9fef7b51879c6fcd6fb4b8863eea0cb2a978c508711dc52c80041
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.