Accelerated NLP pipelines for fast inference on CPU and GPU. Built with Transformers, Optimum and ONNX Runtime.

Related tags

Deep Learningnlpbenchmarknatural-language-processingpipelinetransformersinfinityoptimumonnxhuggingfaceonnxruntime
Overview

Optimum Transformers

Tests License PyPI

Accelerated NLP pipelines for fast inference 🚀 on CPU and GPU. Built with 🤗 Transformers, Optimum and ONNX runtime.

Installation:

With PyPI:

pip install optimum-transformers

Or directly from GitHub:

pip install git+https://github.com/AlekseyKorshuk/optimum-transformers

Usage:

The pipeline API is similar to transformers pipeline with just a few differences which are explained below.

Just provide the path/url to the model, and it'll download the model if needed from the hub and automatically create onnx graph and run inference.

from optimum_transformers import pipeline

# Initialize a pipeline by passing the task name and 
# set onnx to True (default value is also True)
nlp = pipeline("sentiment-analysis", use_onnx=True)
nlp("Transformers and onnx runtime is an awesome combo!")
# [{'label': 'POSITIVE', 'score': 0.999721109867096}]

Or provide a different model using the model argument.

from optimum_transformers import pipeline

nlp = pipeline("question-answering", model="deepset/roberta-base-squad2", use_onnx=True)
nlp(question="What is ONNX Runtime ?",
         context="ONNX Runtime is a highly performant single inference engine for multiple platforms and hardware")
# {'answer': 'highly performant single inference engine for multiple platforms and hardware', 'end': 94,
# 'score': 0.751201868057251, 'start': 18}
from optimum_transformers import pipeline

nlp = pipeline("ner", model="mys/electra-base-turkish-cased-ner", use_onnx=True, optimize=True,
                    grouped_entities=True)
nlp("adana kebap ülkemizin önemli lezzetlerinden biridir.")
# [{'entity_group': 'B-food', 'score': 0.869149774312973, 'word': 'adana kebap'}]

Set use_onnx to False for standard torch inference. Set optimize to True for quantize with ONNX. ( set use_onnx to True)

Supported pipelines

You can create Pipeline objects for the following down-stream tasks:

  • feature-extraction: Generates a tensor representation for the input sequence
  • ner and token-classification: Generates named entity mapping for each word in the input sequence.
  • sentiment-analysis: Gives the polarity (positive / negative) of the whole input sequence. Can be used for any text classification model.
  • question-answering: Provided some context and a question referring to the context, it will extract the answer to the question in the context.
  • text-classification: Classifies sequences according to a given number of classes from training.
  • zero-shot-classification: Classifies sequences according to a given number of classes directly in runtime.
  • fill-mask: The task of masking tokens in a sequence with a masking token, and prompting the model to fill that mask with an appropriate token.
  • text-generation: The task of generating text according to the previous text provided.

Calling the pipeline for the first time loads the model, creates the onnx graph, and caches it for future use. Due to this, the first load will take some time. Subsequent calls to the same model will load the onnx graph automatically from the cache.

Benchmarks

Note: For some reason, onnx is slow on colab notebook, so you won't notice any speed-up there. Benchmark it on your own hardware.

Check our example of benchmarking: example.

For detailed benchmarks and other information refer to this blog post and notebook.

Note: These results were collected on my local machine. So if you have high performance machine to benchmark, please contact me.

Benchmark sentiment-analysis pipeline

Benchmark zero-shot-classification pipeline

Benchmark token-classification pipeline

Benchmark question-answering pipeline

Benchmark fill-mask pipeline

About

Built by Aleksey Korshuk

Follow

Follow

Follow

🚀 If you want to contribute to this project OR create something cool together — contact me: link

Star this repository:

GitHub stars

Resources

You might also like...
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

ONNX Runtime is a cross-platform inference and training machine-learning accelerator. ONNX Runtime inference can enable faster customer experiences an

Microsoft 8k Jan 4, 2023
Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.
Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

scc4onnx Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel

Katsuya Hyodo 16 Dec 22, 2022
A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.
A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.

sam4onnx A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for

Katsuya Hyodo 6 May 15, 2022
A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]
A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]

PINTO_model_zoo Please read the contents of the LICENSE file located directly under each folder before using the model. My model conversion scripts ar

Katsuya Hyodo 2.4k Jan 5, 2023
ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX
ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

Ibai Gorordo 18 Nov 6, 2022
ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX
ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Ibai Gorordo 14 Dec 9, 2022
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.

sne4onnx A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or

Katsuya Hyodo 10 Aug 30, 2022
Simple ONNX operation generator. Simple Operation Generator for ONNX.
Simple ONNX operation generator. Simple Operation Generator for ONNX.

sog4onnx Simple ONNX operation generator. Simple Operation Generator for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools Key concept V

Katsuya Hyodo 6 May 15, 2022
Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.
Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

snc4onnx Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools 1.

Katsuya Hyodo 8 Oct 13, 2022
Releases(v0.2.1-upd)
Owner
Aleksey Korshuk
Aleksey Korshuk
GitHub Repository
Place holder for HOPE: a human-centric and task-oriented MT evaluation framework using professional post-editing

HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation Place holder for dat

Lifeng Han 1 Apr 25, 2022
The official code for paper "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling".

R2D2 This is the official code for paper titled "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Mode

Alipay 49 Dec 17, 2022
This project contains an implemented version of Face Detection using OpenCV and Mediapipe. This is a code snippet and can be used in projects.

Live-Face-Detection Project Description: In this project, we will be using the live video feed from the camera to detect Faces. It will also detect so

Hassan Shahzad 3 Oct 02, 2021
PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020).

NHDRRNet-PyTorch This is the PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020). 0. Differences between Original Paper and

Yutong Zhang 1 Mar 01, 2022
Full body anonymization - Realistic Full-Body Anonymization with Surface-Guided GANs

Realistic Full-Body Anonymization with Surface-Guided GANs This is the official

Håkon Hukkelås 30 Nov 18, 2022
Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"

CSA: Contextual Similarity Aggregation with Self-attention for Visual Re-ranking PyTorch training code for CSA (Contextual Similarity Aggregation). We

Hui Wu 19 Oct 21, 2022
Proto-RL: Reinforcement Learning with Prototypical Representations

Proto-RL: Reinforcement Learning with Prototypical Representations This is a PyTorch implementation of Proto-RL from Reinforcement Learning with Proto

Denis Yarats 74 Dec 06, 2022
[CVPR 2022] "The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy" by Tianlong Chen, Zhenyu Zhang, Yu Cheng, Ahmed Awadallah, Zhangyang Wang

The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy Codes for this paper: [CVPR 2022] The Pr

VITA 16 Nov 26, 2022
Luminaire is a python package that provides ML driven solutions for monitoring time series data.

A hands-off Anomaly Detection Library Table of contents What is Luminaire Quick Start Time Series Outlier Detection Workflow Anomaly Detection for Hig

Zillow 670 Jan 02, 2023
This repo. is an implementation of ACFFNet, which is accepted for in Image and Vision Computing.

Attention-Guided-Contextual-Feature-Fusion-Network-for-Salient-Object-Detection This repo. is an implementation of ACFFNet, which is accepted for in I

5 Nov 21, 2022
Predict stock movement with Machine Learning and Deep Learning algorithms

Project Overview Stock market movement prediction using LSTM Deep Neural Networks and machine learning algorithms Software and Library Requirements Th

Naz Delam 46 Sep 13, 2022
HybVIO visual-inertial odometry and SLAM system

HybVIO A visual-inertial odometry system with an optional SLAM module. This is a research-oriented codebase, which has been published for the purposes

Spectacular AI 320 Jan 03, 2023
PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models

octconv.pytorch PyTorch implementation of Octave Convolution in Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octa

Duo Li 273 Dec 18, 2022
Chainer Implementation of Semantic Segmentation using Adversarial Networks

Semantic Segmentation using Adversarial Networks Requirements Chainer (1.23.0) Differences Use of FCN-VGG16 instead of Dilated8 as Segmentor. Caution

Taiki Oyama 99 Jun 28, 2022
Semi-supervised semantic segmentation needs strong, varied perturbations

Semi-supervised semantic segmentation using CutMix and Colour Augmentation Implementations of our papers: Semi-supervised semantic segmentation needs

146 Dec 20, 2022
The code used for the free [email protected] Webinar series on Reinforcement Learning in Finance

Reinforcement Learning in Finance [email protected] Webinar This repository provides the code f

Yves Hilpisch 62 Dec 22, 2022
Code for the paper "Reinforced Active Learning for Image Segmentation"

Reinforced Active Learning for Image Segmentation (RALIS) Code for the paper Reinforced Active Learning for Image Segmentation Dependencies python 3.6

Arantxa Casanova 79 Dec 19, 2022
Pytorch library for seismic data augmentation

Pytorch library for seismic data augmentation

Artemii Novoselov 27 Nov 22, 2022
ParmeSan: Sanitizer-guided Greybox Fuzzing

ParmeSan: Sanitizer-guided Greybox Fuzzing ParmeSan is a sanitizer-guided greybox fuzzer based on Angora. Published Work USENIX Security 2020: ParmeSa

VUSec 158 Dec 31, 2022
Gym-TORCS is the reinforcement learning (RL) environment in TORCS domain with OpenAI-gym-like interface.

Gym-TORCS Gym-TORCS is the reinforcement learning (RL) environment in TORCS domain with OpenAI-gym-like interface. TORCS is the open-rource realistic

naoto yoshida 400 Dec 27, 2022