Feature Extraction
sentence-transformers
ONNX
Transformers
bert
onnxruntime
reranker
int8
int4
text-embeddings-inference
Instructions to use jrc2139/e5-small-v2-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jrc2139/e5-small-v2-ONNX with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jrc2139/e5-small-v2-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] - Transformers
How to use jrc2139/e5-small-v2-ONNX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jrc2139/e5-small-v2-ONNX")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jrc2139/e5-small-v2-ONNX") model = AutoModel.from_pretrained("jrc2139/e5-small-v2-ONNX") - Notebooks
- Google Colab
- Kaggle
ONNX Quantized versions of intfloat/e5-small-v2
This repository contains ONNX export and multiple quantized versions of intfloat/e5-small-v2.
Usage
from sentence_transformers import SentenceTransformer
# Load Int8 model (ARM64 example)
model = SentenceTransformer(
"jrc2139/e5-small-v2-ONNX",
backend="onnx",
model_kwargs={"file_name": "onnx/model_q4.onnx"},
trust_remote_code=True
)
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Model tree for jrc2139/e5-small-v2-ONNX
Base model
intfloat/e5-small-v2