ktrain: A Low-Code Library for Augmented Machine Learning
arXiv:2004.10703
Abstract
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.
9 pages
References in corpus (1)
Cited by in corpus (8)
- Graph Neural Networks: Methods, Applications, and Opportunities
- NLP-CUET@DravidianLangTech-EACL2021: Offensive Language Detection from Multilingual Code-Mixed Text using Transformers
- NLP-CUET@DravidianLangTech-EACL2021: Investigating Visual and Textual Features to Identify Trolls from Multimodal Social Media Memes
- ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemic
- Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations
- Probabilistic Impact Score Generation using Ktrain-BERT to Identify Hate Words from Twitter Discussions
- CausalNLP: A Practical Toolkit for Causal Inference with Text
- Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning