pizza-embed-zh-small-v1

pizza-embed-zh-small-v1

A high-quality Chinese text embedding model optimized for ONNX Runtime inference. Part of the Pizza Engine model family by Infinilabs.

Model Details

Files

onnx/model.onnx          # ONNX graph (~476 KB)
onnx/model.onnx.data     # External weights (~91 MB)
tokenizer.json            # HuggingFace tokenizer
tokenizer_config.json     # Tokenizer configuration

Quick Start

Python (ONNX Runtime)

import onnxruntime as ort
from tokenizers import Tokenizer
import numpy as np

tokenizer = Tokenizer.from_file("tokenizer.json")
session = ort.InferenceSession("onnx/model.onnx")

# Tokenize
encoding = tokenizer.encode("搜索引擎技术")
input_ids = np.array([encoding.ids], dtype=np.int64)
attention_mask = np.array([encoding.attention_mask], dtype=np.int64)
token_type_ids = np.zeros_like(input_ids)

# Inference
outputs = session.run(None, {
    "input_ids": input_ids,
    "attention_mask": attention_mask,
    "token_type_ids": token_type_ids,
})

# Mean pooling + L2 normalize
hidden = outputs[0]  # [batch, seq, 512]
mask_expanded = attention_mask[:, :, None].astype(np.float32)
pooled = (hidden * mask_expanded).sum(axis=1) / mask_expanded.sum(axis=1)
embedding = pooled / np.linalg.norm(pooled, axis=-1, keepdims=True)
print(embedding.shape)  # (1, 512)

Rust (Pizza Engine CLI)

use pizza_cli::LocalEmbeddingClient;

let mut client = LocalEmbeddingClient::new("infinilabs/pizza-embed-zh-small-v1", 32)?;
let embeddings = client.embed(&["搜索引擎技术", "全文检索"])?;
// embeddings: Vec<Vec<f32>>, each 512-dim, L2-normalized

Rust (ort + tokenizers)

use ort::session::Session;
use tokenizers::Tokenizer;

let session = Session::builder()?.commit_from_file("onnx/model.onnx")?;
let tokenizer = Tokenizer::from_file("tokenizer.json")?;

let encoding = tokenizer.encode("搜索引擎技术", true)?;
// ... run session, mean-pool, L2-normalize

Use Cases

• Semantic search for Chinese documents
• Text similarity and clustering
• RAG (Retrieval-Augmented Generation) pipelines
• Question-answer matching

Performance

Optimized for CPU inference via ONNX Runtime. Suitable for edge deployment and embedded search engines without GPU requirements.

Citation

@misc{pizza-embed-zh-small-v1,
  title={pizza-embed-zh-small-v1: Chinese Text Embedding for Pizza Engine},
  author={Infinilabs},
  year={2026},
  url={https://huggingface.co/infinilabs/pizza-embed-zh-small-v1}
}

License

MIT