Open Source Natural Language Processing (NLP) Tools
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Natural Language Processing (NLP) Tools
View 189 business solutionsBrowse free open source Natural Language Processing (NLP) tools and projects 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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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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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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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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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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Botpress
Dev tools to reliably understand text and automate conversations
We make building chatbots much easier for developers. We have put together the boilerplate code and infrastructure you need to get a chatbot up and running. We propose you a complete dev-friendly platform that ships with all the tools you need to build, deploy and manage production-grade chatbots in record time. Built-in Natural Language Processing tasks such as intent recognition, spell checking, entity extraction, and slot tagging (and many others). A visual conversation studio to design multi-turn conversations and workflows. An emulator & a debugger to simulate conversations and debug your chatbot. Support for popular messaging channels like Slack, Telegram, MS Teams, Facebook Messenger, and an embeddable web chat. An SDK and code editor to extend the capabilities. Post-deployment tools like analytics dashboards, human handoff and more. -
Crowdtesting That Delivers | TesteumTesteum connects your software, app, or website to a worldwide network of testers, delivering detailed feedback in under 48 hours. Ensure functionality and refine UX on real devices, all at a fraction of traditional costs. Trusted by startups and enterprises alike, our platform streamlines quality assurance with actionable insights.
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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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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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ModelScope
Bring the notion of Model-as-a-Service to life
ModelScope is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. In particular, with rich layers of API abstraction, the ModelScope library offers unified experience to explore state-of-the-art models spanning across domains such as CV, NLP, Speech, Multi-Modality, and Scientific-computation. Model contributors of different areas can integrate models into the ModelScope ecosystem through the layered APIs, allowing easy and unified access to their models. Once integrated, model inference, fine-tuning, and evaluations can be done with only a few lines of code. -
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Botkit
Tool for building chat bots, apps and custom integrations
An open source developer tool for building chat bots, apps and custom integrations for major messaging platforms. Part of the Microsoft Bot Framework. We love bots, and want to make them easy and fun to build! Include Botkit into your Node application and boot up a controller that will define your bot's behaviors. In this case, we're setting up a bot to use with the Bot Framework Emulator. Tell the bot to listen for users saying "hello," and use `bot.reply` to send an immediate response. Start a conversation, then queue up multiple messages to send, including a prompt sent using `convo.ask()` which allows your bot to capture user input and use it. Botkit is just one part of a bigger set of developer tools and SDKs that encompass the Microsoft Bot Framework. The Bot Framework SDK provides the base upon which Botkit is built. It is available in multiple programming languages! -
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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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textlint
The pluggable natural language linter for text and markdown
Textlint is an extensible linting tool for text and markdown files, designed to enforce style guidelines, detect errors, and improve writing quality. -
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AdalFlow
The library to build & auto-optimize LLM applications
AdalFlow is a framework for building AI-powered automation workflows, enabling users to design and execute intelligent automation pipelines with minimal coding. -
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Data-Juicer
Data processing for and with foundation models
Data-Juicer is an open-source data processing and augmentation framework designed to enhance the quality and diversity of datasets for machine learning tasks. It includes a modular pipeline for scalable data transformation. -
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SetFit
Efficient few-shot learning with Sentence Transformers
SetFit is an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers. It achieves high accuracy with little labeled data - for instance, with only 8 labeled examples per class on the Customer Reviews sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples. -
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Spark NLP
State of the Art Natural Language Processing
Experience the power of large language models like never before, unleashing the full potential of Natural Language Processing (NLP) with Spark NLP, the open source library that delivers scalable LLMs. The full code base is open under the Apache 2.0 license, including pre-trained models and pipelines. The only NLP library built natively on Apache Spark. The most widely used NLP library in the enterprise. Spark ML provides a set of machine learning applications that can be built using two main components, estimators and transformers. The estimators have a method that secures and trains a piece of data to such an application. The transformer is generally the result of a fitting process and applies changes to the target dataset. These components have been embedded to be applicable to Spark NLP. Pipelines are a mechanism for combining multiple estimators and transformers in a single workflow. They allow multiple chained transformations along a machine-learning task. -
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DataProfiler
Extract schema, statistics and entities from datasets
DataProfiler is an AI-powered tool for automatic data analysis and profiling, designed to detect patterns, anomalies, and schema inconsistencies in structured and unstructured datasets. The DataProfiler is a Python library designed to make data analysis, monitoring, and sensitive data detection easy. Loading Data with a single command, the library automatically formats & loads files into a DataFrame. Profiling the Data, the library identifies the schema, statistics, entities (PII / NPI), and more. Data Profiles can then be used in downstream applications or reports. -
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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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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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VADER
Lexicon and rule-based sentiment analysis tool
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool designed for analyzing the sentiment of text, particularly in social media and short text formats. It is optimized for quick and accurate analysis of positive, negative, and neutral sentiments. -
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deepdoctection
A Repo For Document AI
DeepDoctection is a document AI framework that applies deep learning techniques to analyze and extract structured data from scanned documents, PDFs, and images. deepdoctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated frameworks for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. -
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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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tidytext
Text mining using tidy tools
tidytext brings tidy data principles to text mining by converting text into a tidy data frame format. It provides tools for tokenization, sentiment analysis, n‑gram creation, and term‑document matrices, enabling interoperability with dplyr, ggplot2, and other tidyverse workflows. -
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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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PaperAI
Semantic search and workflows for medical/scientific papers
PaperAI is an open-source framework for searching and analyzing scientific papers, particularly useful for researchers looking to extract insights from large-scale document collections. -
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PyResParser
A simple resume parser used for extracting information from resumes
PyResParser is a simple resume parser that extracts information from resumes, aiding in the automation of resume-processing tasks. -
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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.
Open Source Natural Language Processing (NLP) Tools Guide
Open source natural language processing (NLP) tools are software applications designed to help users analyze, interpret, and understand text. They are usually developed as an open source project by a community of developers who collaborate together to develop the application.Open source NLP tools often utilize sophisticated algorithms and techniques such as machine learning, deep learning, and natural language understanding to provide insights into text data. These insights can be used for many purposes such as sentiment analysis, topic classification, automatic summarization, entity extraction, and question answering. In addition to being open source projects, these tools are free from cost which is attractive for researchers and business owners who don't have the budget for expensive commercial NLP software solutions. With their flexibility and affordability in mind many businesses have adopted open source NLP tools for data analysis purposes such as customer service chatbot development or social media monitoring projects. Open source NLP tools can be deployed on-premises or in the cloud making them even more versatile when it comes to using them in production systems.
Features of Open Source Natural Language Processing (NLP) Tools
- Tokenization: Process of splitting a sentence into its individual words or phrases, known as tokens.
- Part-Of-Speech Tagging: A process that assigns part-of-speech tags (nouns, verbs, adjectives etc.) to each token in a sentence.
- Named Entity Recognition: A process for detecting and classifying named entities (people, places, organizations etc.) from unstructured text.
- Syntactic Parsing: Process of segmenting text into smaller pieces to determine the meaning and structure of a sentence.
- Semantic Analysis: A process for extracting the underlying meaning behind a set of words by connecting them with relevant context or facts.
- Sentiment Analysis: Process used to identify subjective opinions expressed in text and classify it as either positive or negative.
- Summarization & Text Simplification: Refers to techniques used to produce shorter versions of texts while maintaining the key information contained within them.
- Machine Translation & Language Identification: Natural language processing tools used to detect source language and automatically translate it into another target language.
Different Types of Open Source Natural Language Processing (NLP) Tools
- GATE (General Architecture for Text Engineering): GATE is an open-source platform for performing NLP tasks such as text mining and information extraction. It provides modular components that can be used to build more complex applications.
- Stanford CoreNLP: Stanford CoreNLP is a suite of tools for natural language processing of English, Chinese, French, Spanish and other languages. It includes a set of core Java libraries and command line tools which allow developers to create custom NLP pipelines.
- NLTK (Natural Language ToolKit): NLTK is an open source library used to build Python programs that can analyze natural language. It provides interfaces to more than 50 corpora and lexical resources, along with wrappers for over 50 NLP applications.
- spaCy: SpaCy is a library for advanced NLP in Python designed specifically for production use on large datasets. It allows developers to quickly create systems that can process large volumes of text accurately and efficiently using its efficient algorithms and Pipelines-based architecture.
- OpenNLP: OpenNLP is an Apache-licensed open source toolkit developed by the Apache Software Foundation for the processing of human language data like tokenization, segmentation, categorization, parsing etc., written in Java programming language.
- UIMA (Unstructured Information Management Architecture): UIMA is an open source framework developed by IBM Research specifically designed to enable development of applications which search unstructured content and extract information from it like annotations, relationships etc., through annotators written in Java or C++ programming language.
Open Source Natural Language Processing (NLP) Tools Advantages
- Cost: Using open source NLP tools is often free, or much more cost effective than expensive licensed software. This makes it an ideal choice for businesses who have smaller budgets, as well as individuals and researchers.
- Efficiency: Open source NLP tools are available immediately, with no need to purchase or wait for a license. This makes them great when you need results quickly.
- Flexibility: Open source NLP tools are often very customizable and can be adapted to many different tasks. This provides flexibility in using the tool for a variety of needs.
- Portability: Since they are open source, these tools can be used on any operating system without the need to install additional software. They can also easily be shared and distributed among colleagues or students in a class setting with minimal effort.
- Security & Privacy: Many open source solutions guarantee that your code is not only secure but private too, meaning that no one else will have access to confidential data or research results from your projects unless you choose to share them publicly.
- Community Support & Development: The advantage of having an active community behind their development ensures that these NLP solutions stay up-to-date and keep improving rapidly with the regular updates provided by the community developers addressing bugs and adding new features. Additionally, having so many people contributing allows users of open source tools to get help faster if they face a problem when using the tool set.
What Types of Users Use Open Source Natural Language Processing (NLP) Tools?
- Researchers: Scientists and academics who use open source NLP tools to study language, its meaning, and its context.
- Educators: Those who teach students about the basics of natural language processing as a part of their coursework.
- Data Analysts: Analysts leverage open source NLP tools to extract insights from datasets or text-based sources.
- Application Developers: Software engineers and application developers who use open source NLP libraries for tasks like creating chatbots or building speech recognition software.
- Machine Learning Engineers: Professionals who develop machine learning models that utilize natural language processing techniques.
- Business Analytics Teams: Companies often have analytics teams that apply NLP techniques to their customer data in order to better understand customer behavior and preferences.
- Webmasters: Webmasters can use open source NLP libraries to automatically generate content or monitor webpages for certain key words or phrases.
- Journalists & Content Creators: Journalists, bloggers, copywriters, etc., commonly use open source NLP tools to organize notes, generate content outlines and edit drafts more efficiently than before.
How Much Do Open Source Natural Language Processing (NLP) Tools Cost?
Open source natural language processing (NLP) tools are typically free to use. As open source software, they are developed and maintained by a community of volunteers who donate their time and energy to create quality code that can be used by anyone across the world. This means that you don’t have to pay a cent for creating sophisticated NLP models or applications using open source NLP tools.
With an increasing number of open source resources available today, you can find various kinds of data sets, tools and frameworks for building your own classifiers for sentiment analysis, text summarization or even machine translation systems. Some of these resources include popular libraries like Natural Language Toolkit (NLTK), Python-based TensorFlow library, OpenNLP from Apache Software Foundation and SpaCy – an industrial-strength natural language understanding library in Python.
These libraries come with extensive documentation on how to use them as well as detailed instructions on how to implement particular tasks — such as text classification or information extraction — leveraging the power of machine learning algorithms. With only basic programming knowledge required, one can create complex tools or extend existing ones with just a few lines of code. Thus there is no need for costly licenses related to closed-source software when working with free and open source NLP tools.
What Software Do Open Source Natural Language Processing (NLP) Tools Integrate With?
Open source natural language processing (NLP) tools can be integrated with a variety of software, including chatbot development platforms, analytic and business intelligence platforms, enterprise search solutions, automation and workflow management systems, customer support software, voice recognition technologies, and more. Many of these types of software provide APIs or other integration services that allow developers to quickly connect their NLP tools to other applications. By connecting open source NLP tools to other applications through these interfaces, users can leverage the power of NLP for use cases such as automatically analyzing customer data for sentiment analysis or creating virtual agents using natural language commands.
What Are the Trends Relating to Open Source Natural Language Processing (NLP) Tools?
- Open source NLP tools are becoming increasingly popular due to their flexibility and affordability.
- Developers have access to a wide range of software libraries, from which they can pick the best fit for their projects.
- Deep learning algorithms have been incorporated into many open source NLP tools, resulting in more accurate language processing.
- Open source frameworks such as spaCy, NLTK, and Gensim offer developers the opportunity to customize models and hyperparameters.
- Open source NLP tools make it easier for developers to integrate pre-trained models into their applications.
- These tools are being used more frequently in various applications such as chatbot development, text summarization, sentiment analysis, natural language understanding, etc.
- Many open source libraries also provide support for multiple languages, making them accessible to a wider audience.
- There has been increased focus on open source efforts in the industry, with companies investing resources in developing new NLP tools and services.
- Open source NLP tools are becoming more user-friendly and accessible over time, allowing more developers to benefit from them.
How Users Can Get Started With Open Source Natural Language Processing (NLP) Tools
Getting started with using open source Natural Language Processing (NLP) projects is easier than ever now that there are a wide range of popular and powerful projects available.
The first step in getting up to speed on open source NLP tools is to familiarize yourself with the most popular frameworks, libraries, and packages available. There are dozens of options out there, including spaCy, NLTK, OpenNLP, NLU-Evaluation Framework (NEF), Stanford CoreNLP, Gensim, AllenNLP, and HuggingFace Transformers. Different projects focus on different tasks (e.g., tokenization), so you should consider which project is best suited for your particular needs. Once you’ve chosen a project or framework that fits your requirements best it's time to get started.
Fortunately tutorials for many of these packages are commonly updated as new versions come out or bugs have been fixed. A great place to start if you're new to using open source NLP tools is training courses such as Natural Language Processing with Python from Coursera or Udacity's Intro to Natural Language Processing course. These courses will help you understand the basics of NLP concepts and algorithms as well as provide an overview of the various tools and packages available for use in developing solutions for natural language processing tasks.
Once you've completed any necessary training online or elsewhere it's time to dig deeper into each package and library that interests you most. Each project often has its own official website containing extensive documentation explaining not only how set up the software but also how certain features work exactly under different settings etc.. Github repos can often provide more insights into an algorithm’s capabilities by providing examples written by users who may have already solved a problem similar to yours before. Lastly don't forget about local user groups where passionate people eager to help newcomers meet in person share their experiences while demystifying some technical hurdles along the way.
