onnx region:us
This model is a quantized version of: [**SamLowe/universal-sentence-encoder-multilingual-3-onnx**](https://huggingface.co/SamLowe/universal-sentence-encoder-multilingual-3-onnx)

---

languages:

*   en
*   ar
*   zh
*   fr
*   de
*   it
*   ja
*   ko
*   nl
*   pl
*   pt
*   es
*   th
*   tr
*   ru  
    tags:
*   feature-extraction
*   onnx
*   use
*   text-embedding
*   tensorflow-hub  
    license: apache-2.0  
    inference: false  
    widget:
*   text: Thank goodness ONNX is available, it is lots faster!

---

### Universal Sentence Encoder Multilingual v3

ONNX version of [https://tfhub.dev/google/universal-sentence-encoder-multilingual/3](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3)

The original TFHub version of the model is referenced in other models here E.g. [https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3](https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3)

### Overview

See overview and license details at [https://tfhub.dev/google/universal-sentence-encoder-multilingual/3](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3)

This model is a full precision version of the TFHub original, in ONNX format.

It uses the [ONNXRuntime Extensions](https://github.com/microsoft/onnxruntime-extensions) to embed the tokenizer within the ONNX model, so no seperate tokenizer is needed, and text is fed directly into the ONNX model.

Post-processing (E.g. pooling, normalization) is also implemented within the ONNX model, so no separate processing is necessary.

### How to use

```python
import onnxruntime as ort
from onnxruntime_extensions import get_library_path
from os import cpu_count

sentences = ["hello world"]

def load_onnx_model(model_filepath):
  _options = ort.SessionOptions()
  _options.inter_op_num_threads, _options.intra_op_num_threads = cpu_count(), cpu_count()
  _options.register_custom_ops_library(get_library_path())
  _providers = ["CPUExecutionProvider"]  # could use ort.get_available_providers()
  return ort.InferenceSession(path_or_bytes=model_filepath, sess_options=_options, providers=_providers)

model = load_onnx_model("filepath_for_model_dot_onnx")

model_outputs = model.run(output_names=["outputs"], input_feed={"inputs": sentences})[0]
print(model_outputs)
```