论文标题
Ktrain：用于增强机器学习的低代码库
ktrain: A Low-Code Library for Augmented Machine Learning
论文作者
论文摘要
我们提出了Ktrain，这是一个低代码Python库，它使机器学习更容易访问和更易于应用。作为Tensorflow和许多其他库（例如变形金刚，Scikit-Learn，Stellargraph）的包装纸，它旨在使精致的，最先进的机器学习模型易于构建，培训，检查和应用初学者和经验丰富的从业者。具有支持文本数据的模块（例如，文本分类，序列标记，开放域的问题 - 解答），视觉数据（例如，图像分类），图形数据（例如，节点分类，链接预测）和表格数据，Ktrain构成了一个简单的统一界面，可以快速求解一个范围的范围，即在少量或三个或三个或三个或三个或三个命令中的范围。
We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply. As a wrapper to TensorFlow and many other libraries (e.g., transformers, scikit-learn, stellargraph), it is designed to make sophisticated, state-of-the-art machine learning models simple to build, train, inspect, and apply by both beginners and experienced practitioners. Featuring modules that support text data (e.g., text classification, sequence tagging, open-domain question-answering), vision data (e.g., image classification), graph data (e.g., node classification, link prediction), and tabular data, ktrain presents a simple unified interface enabling one to quickly solve a wide range of tasks in as little as three or four "commands" or lines of code.
