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在自然语言处理中,这个任务处理的就是离散数据,所以一般会把对抗扰动添加到嵌入层中,为了最大化对抗样本的扰动能力,使用梯度上升方式生成对抗样本。 可以看2017年的论文《Adversarial Training Methods For Semi-Supervised Text Classification》,虽然这篇文章不是那么新,但这个思路可以作为当今训练的一个手段,提升你模型2...
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification - johnson7788/MixText
Anaconda3 (python 3.6) Pytorch 1.3.1 gensim 3.6.0 Easy Run cd ./model/code/ python train.py You may change the dataset by modifying the variable "dataset = 'example'" in the top of the code "train.py" or use arguments (see train.py). ...
At this point, we will rank different types of machine learning algorithms in Python by using scikit-learn to create a set of different models. It will then be easy to see which one performs the best.Logistic regression with varying numbers of polynomials Support vector machine with a linear ...
Train the classifier(s) on this corpus by means of a software library such as Python's scikit-learn (which we will be using below) Use the classifier to label new documents, in an automated, ongoing manner. Assess the "classification rate" and other associated performance metrics of the cla...
Applications/Improvements: The results of this comparison are shown at the end of the paper along with the desktop application for the same which helps in classification of SPAM and HAM. This is also developed and executed in python.doi:10.1007/978-981-13-0212-1_15SureshMerugu...
Boeing Engineer Greg DeVore gives an introduction to supervised learning in Python, including how to choose the appropriate model for a regression or classification problem, as well as how to evaluate its performance.
An implement of EMNLP 2019 paper "Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification". Thank you for your interest in our work! 😄 Requirements Anaconda3 (python 3.6) Pytorch 1.3.1 gensim 3.6.0 Easy Run ...