A system for quickly generating training data with weak supervision
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Updated
Apr 8, 2022 - Python
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A system for quickly generating training data with weak supervision
Both the GoalFunctionResult and AttackResult abstract classes should provide a meaningful __str__ method so that they can be printed in a readable way.
High-Level Training, Data Augmentation, and Utilities for Pytorch
自然语言处理(nlp),小姜机器人(闲聊检索式chatbot),BERT句向量-相似度(Sentence Similarity),XLNET句向量-相似度(text xlnet embedding),文本分类(Text classification), 实体提取(ner,bert+bilstm+crf),数据增强(text augment, data enhance),同义句同义词生成,句子主干提取(mainpart),中文汉语短文本相似度,文本特征工程,keras-http-service调用
Data augmentation for NLP, presented at EMNLP 2019
List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.
一键中文数据增强包 ; NLP数据增强、bert数据增强、EDA:pip install nlpcda
An implement of the paper of EDA for Chinese corpus.中文语料的EDA数据增强工具。NLP数据增强。论文阅读笔记。
Data Augmentation For Object Detection
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning.
A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.
yolo(all versions) implementation in keras and tensorflow 2.x
Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST
Awesome Graph Self-Supervised Learning
Hi, guys!
I am trying to reuse the OCR transformation module in TextFlint, but I somehow find it rather trivial...
I quote the code about the OCR rules in the source code as below:
mapping = {
'0': ['8', '9', 'o', 'O', 'D'],
'1': ['4', '7', 'l', 'I'],
'2': ['z', 'Z'],
'5': ['8'],
'6': ['b'],
'8': ['s', 'S',Light-weight Single Person Pose Estimator
A Implementation of SpecAugment with Tensorflow & Pytorch, introduced by Google Brain
Collection of papers and resources for data augmentation for NLP.
Efficient Learning of Augmentation Policy Schedules
Deep Convolutional Neural Networks for Musical Source Separation
This transform takes a fraction of the end or the start of the audio and treats that part as padding. We can implement several modes:
Implementation of the mixup training method
DeltaPy - Tabular Data Augmentation (by @firmai)
Copy-paste augmentation for segmentation and detection tasks
Data augmentation tool for images
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
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Hi, I was wondering, is DALI currently compatible with tensorcom? And if so, are there any examples showing how to use DALI with a tensorcom data server, for example, like this?