oborchers

from onnxruntime import InferenceSession, SessionOptions
from onnxruntime_customops import get_library_path

opt = rt.SessionOptions()
opt.register_custom_ops_library(get_library_path())

sess = rt.InferenceSession("universal-sentence-encoder-5.onnx", opt, providers=ONNX_PROVIDERS)

sess.run(
    output_names=["outputs"],

oborchers

#!/bin/bash
mkdir universal-sentence-encoder-5
cd universal-sentence-encoder-5
wget https://storage.googleapis.com/tfhub-modules/google/universal-sentence-encoder-large/5.tar.gz
tar -xvzf 5.tar.gz
rm 5.tar.gz
cd ..
python -m tf2onnx.convert --saved-model universal-sentence-encoder-5 --output universal-sentence-encoder-5.onnx --opset 12 --extra_opset ai.onnx.contrib:1 --tag serve

oborchers

del model_args["dynamic_axes"]["output_0"] # Delete unused output
del model_args["dynamic_axes"]["output_1"] # Delete unused output
model_args["dynamic_axes"]["sentence_embedding"] = {0: "batch"}

model_args["output_names"] = ["sentence_embedding"]

oborchers

class SentenceTransformer(transformers.BertModel):
    def __init__(self, config):
        super().__init__(config)
        # Naming alias for ONNX output specification
        # Makes it easier to identify the layer
        self.sentence_embedding = torch.nn.Identity()

    def forward(self, input_ids, token_type_ids, attention_mask):
        # Get the token embeddings from the base model
        token_embeddings = super().forward(

oborchers

def print_transformers_shape_inference(name_or_path: str):
    """Prints the transformers shape inference for onnx."""
    res = {}
    
    model_pipeline = transformers.FeatureExtractionPipeline(
        model=transformers.AutoModel.from_pretrained(name_or_path),
        tokenizer=transformers.AutoTokenizer.from_pretrained(
            name_or_path, use_fast=True
        ),
        framework="pt",

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# We start by working with CUDA only
ONNX_PROVIDERS = ["CUDAExecutionProvider", "CPUExecutionProvider"]
opt = rt.SessionOptions()
sess = rt.InferenceSession(str(model_pth), opt, providers=ONNX_PROVIDERS)

model_input = tokenizer.encode_plus(span)
model_input = {name : np.atleast_2d(value) for name, value in model_input.items()}
onnx_result = sess.run(None, model_input)

print(onnx_result[0].shape)

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# Script to export a transformers model
model_name = "sentence-transformers/bert-base-nli-stsb-mean-tokens"
pipeline_name = "feature-extraction"
model_pth = Path(f"encoder/{model_name}.onnx")

nlp = transformers.pipeline(pipeline_name, model=model_name, tokenizer=model_name, device=0)
tokenizer = nlp.tokenizer

if model_pth.exists():
    model_pth.unlink()

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apt-get update

# Install ONNX ML
export ONNX_ML=1
pip install -U onnx

# Clone ONNX Runtime and build
git clone https://github.com/microsoft/onnxruntime.git --branch v1.7.1 --single-branch
/bin/sh onnxruntime/dockerfiles/scripts/install_common_deps.sh
cd onnxruntime/

oborchers

TY  - ICOMM
T1  - Your Labels and Data are Noisy? LASSO The Traitors!
A1  - Borchers, Oliver
A1  - Ringel, Daniel M.
Y1  - 2020///
JF  - Towards Data Science
UR  - https://medium.com/@oliverbor/lasso-the-traitors-dd33ea5942bc
N2  - This article develops the LASSO The Traitors (LTT) method. LTT filters out noisy observations from a dataset based on an exogenous performance metric. LTT significantly improves the performance of estimators based on the cleaned dataset. LTT is fast, easily applicable, and task agnostic.
ER  - 

oborchers

@misc{Borchers2020,
abstract = {This article develops the LASSO The Traitors (LTT) method. LTT filters out noisy observations from a dataset based on an exogenous performance metric. LTT significantly improves the performance of estimators based on the cleaned dataset. LTT is fast, easily applicable, and task agnostic.},
author = {Borchers, Oliver and Ringel, Daniel M.},
booktitle = {Towards Data Science},
title = {{Your Labels and Data are Noisy? LASSO The Traitors!}},
url = {https://medium.com/@oliverbor/lasso-the-traitors-dd33ea5942bc},
year = {2020}
}