Programming language classifier
Language classifier for Atom
Natural Language Classifier sample application
A BrainJS neural network natural language classifier
Programming language classifier of a code for node.js
A programming language classifier that uses a bayesian algorithm to determine likelihood of a language being a particular programming language
Javascript Natural Language Classifier
IBM Watson Node Red Node for Natural Language Classifier
A bot based on Watson Natural Language Classifier for finding the Bluemix service(s) matching a description of the needs
MediaPipe Text Tasks
Organizes aggregated content into a virtual file catalog for use in an Antora documentation pipeline.
Classify the color along the reference color. using algorithm the CIEDE2000, RGB, HSV.
Library for NLU (Natural Language Understanding) done in Node.js
Naive Bayes Text Classifier
MediaPipe Audio Tasks
KNN Classifier for TensorFlow.js
CART decision tree algorithm
Toxicity model in TensorFlow.js
Work with IANA language tags.
Random forest for classification and regression
No description provided.
NLP Functions for amplifying negations, managing elisions, creating ngrams, stems, phonetic codes to tokens and more.
Full BCP 47 language subtag data from the official IANA repository, in JSON format with multiple indices.
Types used by the Language server for node
Cross-language UserAgent classifier library, ruby implementation
It is a library that classifies Japanese language. It depends on classifier-reborn and natto.
Watson Natural Language Classifier in Ruby
This Ruby gem leverages Machine Learning(ML) techniques to make predictions(forecasts) and classifications in various applications. It provides capabilities such as predicting next month's billing, forecasting upcoming sales orders, identifying patient's potential findings(like Diabetes), determining user approval status, classifying text, generating similarity scores, and making recommendations. It uses Python3 under the hood, powered by popular machine learning techniques including NLP(Natural Language Processing), Decision Tree, K-Nearest Neighbors and Logistic Regression, Random Forest and Linear Regression algorithms.
RSence is a different and unique development model and software frameworks designed first-hand for real-time web applications. RSence consists of separate, but tigtly integrated data- and user interface frameworks. RSence could be classified as a thin server - thick client system. Applications and submobules are installed as indepenent plugin bundles into the plugins folder of a RSence environment, which in itself is a self-contained bundle. A big part of RSence itself is implemented as shared plugin bundles. The user interface framework of RSence is implemented in high-level user interface widget classes. The widget classes share a common foundation API and access the browser's native API's using an abstracted event- and element layer, which provides exceptional cross-browser compatibility. The data framework of RSence is a event-driven system, which synchronized shared values between the client and server. It's like a realtime bidirectional form-submission engine that handles data changes intelligently. On the client, changed values trigger events on user interface widgets. On the server, changed values trigger events on value responder methods of server plugin modules. It doesn't matter if the change originates on client or server, it's all synchronized and propagated automatically. The server framework is implemented as a high-level, modular data-event-driven system, which handles delegation of tasks impossible to implement using a client-only approach. Client sessions are selectively connected to other client sessions and legacy back-ends via the server by using the data framework. The client is written in Javascript and the server is written in Ruby. The client also supports CoffeeScript for custom logic. In many cases, no custom client logic is needed; the user interfaces can be defined in tree-like data models. By default, the models are parsed from YAML files, and other structured data formats are possible, including XML, JSON, databases or any custom logic capable of producing similar objects. The server can connect to custom environments and legacy backends accessible on the server, including software written in other languages.
RSence is a different and unique development model and software frameworks designed first-hand for real-time web applications. RSence consists of separate, but tigtly integrated data- and user interface frameworks. RSence could be classified as a thin server - thick client system. Applications and submobules are installed as indepenent plugin bundles into the plugins folder of a RSence environment, which in itself is a self-contained bundle. A big part of RSence itself is implemented as shared plugin bundles. The user interface framework of RSence is implemented in high-level user interface widget classes. The widget classes share a common foundation API and access the browser's native API's using an abstracted event- and element layer, which provides exceptional cross-browser compatibility. The data framework of RSence is a event-driven system, which synchronized shared values between the client and server. It's like a realtime bidirectional form-submission engine that handles data changes intelligently. On the client, changed values trigger events on user interface widgets. On the server, changed values trigger events on value responder methods of server plugin modules. It doesn't matter if the change originates on client or server, it's all synchronized and propagated automatically. The server framework is implemented as a high-level, modular data-event-driven system, which handles delegation of tasks impossible to implement using a client-only approach. Client sessions are selectively connected to other client sessions and legacy back-ends via the server by using the data framework. The client is written in Javascript and the server is written in Ruby. The client also supports CoffeeScript for custom logic. In many cases, no custom client logic is needed; the user interfaces can be defined in tree-like data models. By default, the models are parsed from YAML files, and other structured data formats are possible, including XML, JSON, databases or any custom logic capable of producing similar objects. The server can connect to custom environments and legacy backends accessible on the server, including software written in other languages.
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