Displaying 1 to 20 from 50 results

limdu - Machine-learning for Node.js

  •    Javascript

Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.

fastText - Library for fast text representation and classification.

  •    HTML

fastText is a library for efficient learning of word representations and sentence classification. You can find answers to frequently asked questions on our website.




snips-nlu - Snips Python library to extract meaning from text

  •    Python

Snips NLU (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts structured information. To find out how to use Snips NLU please refer to our documentation, it will provide you with a step-by-step guide on how to use and setup our library.

NeuronBlocks - NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

  •    Python

NeuronBlocks is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages. NeuronBlocks consists of two major components: Block Zoo and Model Zoo.

text-analytics-with-python - Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer

  •    Python

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

delta - DELTA is a deep learning based natural language and speech processing platform.

  •    Python

DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper.


tf-rnn-attention - Tensorflow implementation of attention mechanism for text classification tasks.

  •    Python

Tensorflow implementation of attention mechanism for text classification tasks. Inspired by "Hierarchical Attention Networks for Document Classification", Zichao Yang et al. (http://www.aclweb.org/anthology/N16-1174).

Obsei - Low code AI powered automation tool

  •    Python

Obsei is a low code AI powered automation tool. It can be used in various business flows like social listening, AI based alerting, brand image analysis, comparative study and more. It consist of Observer, Analyzer and Informer. Observer observes the platform like Twitter, Facebook, App Stores, Google reviews, Amazon reviews, News, Website etc and feed that information. Analyzer performs text analysis like classification, sentiment, translation, PII on the analyzed data. Informer sends it to ticketing system, data store, dataframe etc for further action and analysis.

spacy-streamlit - 👑 spaCy building blocks and visualizers for Streamlit apps

  •    Python

This package contains utilities for visualizing spaCy models and building interactive spaCy-powered apps with Streamlit. It includes various building blocks you can use in your own Streamlit app, like visualizers for syntactic dependencies, named entities, text classification, semantic similarity via word vectors, token attributes, and more. The package includes building blocks that call into Streamlit and set up all the required elements for you. You can either use the individual components directly and combine them with other elements in your app, or call the visualize function to embed the whole visualizer.

cnn-text-classification-tf-chinese - CNN for Chinese Text Classification in Tensorflow

  •    Python

This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.

zhihu-text-classification - [2017知乎看山杯 多标签 文本分类] ye组(第六名) 解题方案

  •    Jupyter

和 creat_batch_data.py 相同,只是对 content 部分进行句子划分。用于分层模型。 划分句子长度: wd_title_len = 30, wd_sent_len = 30, wd_doc_len = 10.(即content划分为10个句子,每个句子长度为30个词) ch_title_len = 52, ch_sent_len = 52, ch_doc_len = 10. 不划分句子: wd_title_len = 30, wd_content_len = 150. ch_title_len = 52, ch_content_len = 300.

text-classification-models-tf - Tensorflow implementations of Text Classification Models.

  •    Python

Tensorflow implementation of Text Classification Models. Semi-supervised text classification(Transfer learning) models are implemented at [dongjun-Lee/transfer-learning-text-tf].

whatlang-rs - Natural language detection library for Rust

  •    Rust

Natural language detection for Rust with focus on simplicity and performance.For more details (e.g. how to blacklist some languages) please check the documentation.

python-stop-words - Get list of common stop words in various languages in Python

  •    Python

Get list of common stop words in various languages in Python. Python-stop-words has been originally developed for Python 2, but has been ported and tested for Python 3.






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