Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.

Smile - Statistical Machine Intelligence & Learning Engine

Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build chatbots on Facebook, Slack, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram etc. Rasa's primary purpose is to help you build contextual, layered conversations with lots of back-and-forth. To have a real conversation, you need to have some memory and build on things that were said earlier. Rasa lets you do that in a scalable way.

Rasa - Create chatbots and voice assistants

Stanza is a Python NLP Library for Many Human Languages. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. A new collection of biomedical and clinical English model packages are now available, offering seamless experience for syntactic analysis and named entity recognition (NER) from biomedical literature text and clinical notes.

Stanza - A Python NLP Library for Many Human Languages

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, and tabular data.
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<ul>
<li>Quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code.</li>
<li>Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge.</li>
<li>Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing.</li>
<li>Easily improve/tune your bespoke models and data pipelines, or customize <cite>AutoGluon</cite> for your use-case.</li>
</ul>

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, and tabular data.

AutoGluon - AutoML for Text, Image, and Tabular Data

 The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services. OpenNLP also includes maximum entropy and perceptron based machine learning. 
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The Apache OpenNLP library contains several components, enabling one to build a full natural language processing pipeline. These components include: sentence detector, tokenizer, name finder, document categorizer, part-of-speech tagger, chunker, parser, coreference resolution. Components contain parts which enable one to execute the respective natural language processing task, to train a model and often also to evaluate a model. Each of these facilities is accessible via its application program interface (API). In addition, a command line interface (CLI) is provided for convenience of experiments and training. 

 The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services. OpenNLP also includes maximum entropy and perceptron based machine learning. 

OpenNLP -  Machine learning based toolkit for the processing of natural language text

Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.
Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Rubrix is designed to close this gap, enabling you to iterate as much as you need.

RUBRIX -  Python framework to explore, label, and monitor data for NLP

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Smile - Statistical Machine Intelligence & Learning Engine

Rasa - Create chatbots and voice assistants

Stanza - A Python NLP Library for Many Human Languages

AutoGluon - AutoML for Text, Image, and Tabular Data

OpenNLP - Machine learning based toolkit for the processing of natural language text

RUBRIX - Python framework to explore, label, and monitor data for NLP

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Smile - Statistical Machine Intelligence & Learning Engine

Rasa - Create chatbots and voice assistants

Stanza - A Python NLP Library for Many Human Languages

AutoGluon - AutoML for Text, Image, and Tabular Data

OpenNLP - Machine learning based toolkit for the processing of natural language text

RUBRIX - Python framework to explore, label, and monitor data for NLP

TechStack

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