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rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.

Draft is a streamlined online word processor with version control.

It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.

It can be used to complement any regular touch user interface with a real time voice user interface. It offers real time feedback for faster and more intuitive experience that enables end user to recover from possible errors quickly and with no interruptions.

It is a fast, open source text processor and publishing toolchain for converting AsciiDoc content to HTML5, DocBook, PDF, and other formats. Asciidoctor is written in Ruby and runs on all major operating systems

Turn emails, tweets, surveys or any text into actionable data. Automate business workflows and saveExtract and classify information from text. Integrate with your App within minutes. Get started for free.

It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.

That transforms AI-generated content into natural, undetectable human-like writing. Bypass AI detection systems with intelligent text humanization technology

It provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various tasks.

It is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices.