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Ashx098/
Jina-v5-nano_ONNX-for-vespa

Name: Jina-v5-nano_ONNX-for-vespa
Availability: InStock

onnx

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Jina v5 Nano ONNX for Vespa

This is a Jina v5 nano embedding model in ONNX format, optimized for use with Vespa database.

Model Details

Property	Value
Architecture	Jina v5 nano embedding model
Embedding Dimension	768
Input	Token IDs (int64) + Attention Mask
Output	last_hidden_state (needs pooling)

Files

File	Description
`model.onnx`	ONNX model
`tokenizer.json`	HuggingFace tokenizer
`special_tokens_map.json`	Special tokens mapping

Usage with ONNX Runtime

python

import onnxruntime as ort
from transformers import PreTrainedTokenizerFast
import numpy as np

# Load ONNX model
session = ort.InferenceSession('model.onnx')

# Load tokenizer
tokenizer = PreTrainedTokenizerFast(tokenizer_file='tokenizer.json')

# Generate embedding
text = 'Your search query'
inputs = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors='np')

outputs = session.run(None, {
    'input_ids': inputs['input_ids'],
    'attention_mask': inputs['attention_mask']
})

# Mean pooling
hidden = outputs[0]
mask = inputs['attention_mask']
mask_expanded = np.expand_dims(mask, -1)
pooled = np.sum(hidden * mask_expanded, axis=1) / np.clip(mask_expanded.sum(axis=1), a_min=1e-9)

print(pooled.shape)  # (1, 768)

Requirements

onnx
onnxruntime
transformers
numpy

Ashx098/Jina-v5-nano_ONNX-for-vespa