| 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) | |
| ``` |