README.md

metadata

license: mit
base_model: google/embeddinggemma-300m
pipeline_tag: sentence-similarity
library_name: onnx
language:
  - multilingual
tags:
  - embedding
  - onnx

ONNX - embeddinggemma-300m

FP32 = model.onnx, model.onnx_data

INT8 = model_quantized.onnx, model_quantized.onnx_data

Usage

# onnxruntime-gpu for run it on GPU
pip install onnxruntime sentencepiece numpy

• src/helper.py = onnx session builder (provider selection, threads, memory options)
• src/example.py = full pipeline: sentencepiece tokenization, batching, inference, cosine similarity

python3 src/example.py

0.600231 | The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear, is a bear species endemic to China.
0.153004 | hi
0.554038 | パンダはクマ科の哺乳類で、中国の固有種である。

Note:

• No transformers/tokenizers dependency: the tokenizer is tokenizer.model directly with the native ids
  (bos_id, eos_id, pad_id from the sentencepiece model).
• Prompt prefix per mode:
  document = title: none | text: {text}
  query = task: search result | query: {text}
• Input format: [bos] text [eos], padded per batch to the longest sequence.
• Output = sentence_embedding, 768 dimensions.
• Normalize input with NFKC for consistent multilingual scores.