Upload 2 files

README.md

@@ -0,0 +1,71 @@
+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)
+```

embedding_model_quantized.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8b05b01f0861094f0475cb288ad1b303ae8aee2ebe96e650d210805d5ca70f7
+size 71794489