Open Source Linux Natural Language Processing (NLP) Tools
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Natural Language Processing (NLP) Tools for Linux
View 26 business solutionsBrowse free open source Natural Language Processing (NLP) tools and projects for Linux below. Use the toggles on the left to filter open source Natural Language Processing (NLP) tools by OS, license, language, programming language, and project status.
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Virastyar
Virastyar is an spell checker for low-resource languages
Virastyar is a free and open-source (FOSS) spell checker. It stands upon the shoulders of many free/libre/open-source (FLOSS) libraries developed for processing low-resource languages, especially Persian and RTL languages Publications: Kashefi, O., Nasri, M., & Kanani, K. (2010). Towards Automatic Persian Spell Checking. SCICT. Kashefi, O., Sharifi, M., & Minaie, B. (2013). A novel string distance metric for ranking Persian respelling suggestions. Natural Language Engineering, 19(2), 259-284. Rasooli, M. S., Kahefi, O., & Minaei-Bidgoli, B. (2011). Effect of adaptive spell checking in Persian. In NLP-KE Contributors: Omid Kashefi Azadeh Zamanifar Masoumeh Mashaiekhi Meisam Pourafzal Reza Refaei Mohammad Hedayati Kamiar Kanani Mehrdad Senobari Sina Iravanin Mohammad Sadegh Rasooli Mohsen Hoseinalizadeh Mitra Nasri Alireza Dehlaghi Fatemeh Ahmadi Neda PourMorteza -
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MeCab is a fast and customizable Japanese morphological analyzer. MeCab is designed for generic purpose and applied to variety of NLP tasks, such as Kana-Kanji conversion. MeCab provides parameter estimation functionalities based on CRFs and HMM
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OpenVINO
OpenVINO™ Toolkit repository
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks. Use models trained with popular frameworks like TensorFlow, PyTorch and more. Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi. -
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ChatGLM.cpp
C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & GLM4(V)
ChatGLM.cpp is a C++ implementation of the ChatGLM-6B model, enabling efficient local inference without requiring a Python environment. It is optimized for running on consumer hardware. -
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DeepSparse
Sparsity-aware deep learning inference runtime for CPUs
A sparsity-aware enterprise inferencing system for AI models on CPUs. Maximize your CPU infrastructure with DeepSparse to run performant computer vision (CV), natural language processing (NLP), and large language models (LLMs). -
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NVIDIA NeMo
Toolkit for conversational AI
NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. NeMo uses PyTorch Lightning for easy and performant multi-GPU/multi-node mixed-precision training. Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC. NGC collection of pre-trained speech processing models. -
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Docspell
Assist in organizing your piles of documents
Docspell is a personal document organizer. Or sometimes called a "Document Management System" (DMS). You'll need a scanner to convert your papers into files. Docspell can then assist in organizing the resulting mess. It can unify your files from scanners, emails, and other sources. It is targeted for home use, i.e. families, households, and also for smaller groups/companies. You can associate tags, set correspondent,s and lots of other predefined and custom metadata. If your documents are associated with such metadata, you can quickly find them later using the search feature. However adding this manually is a tedious task. Docspell can help by suggesting correspondents, guessing tags or finding dates using machine learning. It can learn metadata from existing documents and find things using NLP. This makes adding metadata to your documents a lot easier. For machine learning, it relies on the free (GPL) Stanford Core NLP library. -
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spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with an accuracy within 1% of the best available. It's blazing fast, easy to install and comes with a simple and productive API. -
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HanLP
Han Language Processing
HanLP is a multilingual Natural Language Processing (NLP) library composed of a series of models and algorithms. Built on TensorFlow 2.0, it was designed to advance state-of-the-art deep learning techniques and popularize the application of natural language processing in both academia and industry. HanLP is capable of lexical analysis (Chinese word segmentation, part-of-speech tagging, named entity recognition), syntax analysis, text classification, and sentiment analysis. It comes with pretrained models for numerous languages including Chinese and English. It offers efficient performance, clear structure and customizable features, with plenty more amazing features to look forward to on the roadmap. -
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BotSharp
AI Multi-Agent Framework in .NET
Conversation as a platform (CaaP) is the future, so it's perfect that we're already offering the whole toolkits to our .NET developers using the BotSharp AI BOT Platform Builder to build a CaaP. It opens up as much learning power as possible for your own robots and precisely control every step of the AI processing pipeline. BotSharp is an open source machine learning framework for AI Bot platform builder. This project involves natural language understanding, computer vision and audio processing technologies, and aims to promote the development and application of intelligent robot assistants in information systems. Out-of-the-box machine learning algorithms allow ordinary programmers to develop artificial intelligence applications faster and easier. It's written in C# running on .Net Core that is full cross-platform framework. C# is a enterprise-grade programming language which is widely used to code business logic in information management-related system. -
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Ciphey
Decrypt encryptions without knowing the key or cipher
Fully automated decryption/decoding/cracking tool using natural language processing & artificial intelligence, along with some common sense. You don't know, you just know it's possibly encrypted. Ciphey will figure it out for you. Ciphey can solve most things in 3 seconds or less. Ciphey aims to be a tool to automate a lot of decryptions & decodings such as multiple base encodings, classical ciphers, hashes or more advanced cryptography. If you don't know much about cryptography, or you want to quickly check the ciphertext before working on it yourself, Ciphey is for you. The technical part. Ciphey uses a custom-built artificial intelligence module (AuSearch) with a Cipher Detection Interface to approximate what something is encrypted with. And then a custom-built, customizable natural language processing Language Checker Interface, which can detect when the given text becomes plaintext. -
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franc
Natural language detection
Franc is a lightweight language detection library for JavaScript that supports multiple languages and scripts. It is designed for detecting text language efficiently in various applications. -
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Obsei
Obsei is a low code AI powered automation tool
Obsei is an automated no-code/low-code AI-powered text observation and analysis framework, designed for extracting insights from unstructured text data such as social media, reviews, and logs. -
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gse
Go efficient multilingual NLP and text segmentation
Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others. Gse is implements jieba by golang, and try add NLP support and more feature. Support common, search engine, full mode, precise mode and HMM mode multiple word segmentation modes. Support user and embed dictionary, Part-of-speech/POS tagging, analyze segment info, stop and trim words. Support multilingual: English, Chinese, Japanese and others. Support Traditional Chinese. Support HMM cut text use Viterbi algorithm. Support NLP by TensorFlow (in work). Named Entity Recognition (in work). Supports with elastic search and bleve. run JSON RPC service. -
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Subliminal Blaster 4
Subliminal Blaster Powered 4 - Mude seus Hábitos! Change your habits
Subliminal Blaster is a NLP software that shows text subliminal messages in your computer screen while you use it normaly for your activities. It re-programs your mind in a subconscious level while you exercite your conscious with your activities like browsing, working, watching video and others. Subliminal Blaster é um software de PNL que exibe mensagens subliminares na tela do PC enquanto você utiliza normalmente para suas atividades. Ele reprograma sua mente a nível subconsciente enquanto você exercita seu consciente em suas atividades. WE ARE NOW ON VERSION 4! Please support the project by donating bitcoins 1GRYGnSmpuU1ZuXodn2H9UVEpVRBx5CTL2 Or dogecoins! DBfkGrdLvmpbYQzcRCm9KLUuPk9Zigjjod Would you like to contribute? Go to our Facebook page! https://www.facebook.com/SubliminalBlasterIntl/ -
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OpenNLP provides the organizational structure for coordinating several different projects which approach some aspect of Natural Language Processing. OpenNLP also defines a set of Java interfaces and implements some basic infrastructure for NLP compon
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. Then, the second half of this book focuses on deep learning, including applications to natural language processing and computer vision. -
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Stanza
Stanford NLP Python library for many human languages
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
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Weaviate
Weaviate is a cloud-native, modular, real-time vector search engine
Weaviate in a nutshell: Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance of a cloud-native database, all accessible through GraphQL, REST, and various language clients. -
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kener
Kener is a Modern Self hosted Status Page, batteries included
Kener: Open-source Node.js status page tool, designed to make service monitoring and incident handling a breeze. It offers a sleek and user-friendly interface that simplifies tracking service outages and improves how we communicate during incidents. And the best part? Kener integrates seamlessly with GitHub, making incident management a team effort—making it easier for us to track and fix issues together in a collaborative and friendly environment. -
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spaGO
Self-contained Machine Learning and Natural Language Processing lib
A Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. Spago is self-contained, in that it uses its own lightweight computational graph both for training and inference, easy to understand from start to finish. The core module of Spago relies only on testify for unit testing. In other words, it has "zero dependencies", and we are committed to keeping it that way as much as possible. Spago uses a multi-module workspace to ensure that additional dependencies are downloaded only when specific features (e.g. persistent embeddings) are used. A good place to start is by looking at the implementation of built-in neural models, such as the LSTM. Except for a few linear algebra operations written in assembly for optimal performance (a bit of copying from Gonum), it's straightforward Go code, so you don't have to worry. -
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txtai
Build AI-powered semantic search applications
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Traditional search systems use keywords to find data. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. Backed by state-of-the-art machine learning models, data is transformed into vector representations for search (also known as embeddings). Innovation is happening at a rapid pace, models can understand concepts in documents, audio, images and more. Machine-learning pipelines to run extractive question-answering, zero-shot labeling, transcription, translation, summarization and text extraction. Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes). Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. The following applications are powered by txtai. -
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MARF is a general cross-platform framework with a collection of algorithms for audio (voice, speech, and sound) and natural language text analysis and recognition along with sample applications (identification, NLP, etc.) of its use, implemented in Java.
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Apache OpenNLP
Apache OpenNLP
Apache OpenNLP is a machine learning-based NLP library that provides tools for text-processing tasks such as tokenization, sentence segmentation, and named entity recognition. -
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Bolt NLP
Bolt is a deep learning library with high performance
Bolt is a high-performance deep learning inference framework developed by Huawei Noah's Ark Lab. It is designed to optimize and accelerate the deployment of deep learning models across various hardware platforms. Bolt is a light-weight library for deep learning. Bolt, as a universal deployment tool for all kinds of neural networks, aims to automate the deployment pipeline and achieve extreme acceleration. Bolt has been widely deployed and used in many departments of HUAWEI company, such as 2012 Laboratory, CBG and HUAWEI Product Lines. If you have questions or suggestions, you can submit issue.
