File size: 2,312 Bytes
3517cc1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 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)
``` |