Open Source Natural Language Processing (NLP) Tools
Sort By:
Natural Language Processing (NLP) Tools
View 187 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.
-
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
-
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
-
1
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
-
2
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. -
3
Open Interpreter
A natural language interface for computers
Open Interpreter is an open-source tool that provides a natural-language interface for interacting with your computer. It lets large language models (LLMs) run code locally (Python, JavaScript, shell, etc.), enabling you to ask your computer to do tasks like data analysis, file manipulation, browsing, etc. in human terms (“chat with your computer”), with safeguards. Runs locally or via configured remote LLM servers/inference backends, giving flexibility to use models you trust or have locally. It prompts you to approve code before executing, and supports both online LLM models and local inference servers. It seeks to combine convenience (like ChatGPT’s code interpreter) with control and flexibility by running on your own machine. -
4
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
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
5
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 -
6
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. -
7
compromise
Modest natural-language processing
Language is complicated and there's a gazillion words. Compromise is a javascript library that interprets and pre-parses text and makes some reasonable decisions so things are way easier. Compromise tries its best to parse text. it is small, quick, and often good-enough. It is not as smart as you'd think. Conjugate and negate verbs in any tense. Play between plural, singular and possessive forms. Interpret plain-text numbers. Handle implicit terms. Use it on the client-side or as an es-module. compromise is 180kb (minified). It's pretty fast. It can run on keypress. It works mainly by conjugating all forms of a basic word list. Decide how words get interpreted or make heavier changes with a compromise-plugin. Parse text without running POS-tagging. Pre-parse any match statements for faster lookups. It is not the most accurate, or clever nlp library, but found its niche as an easy, small library that can run everywhere. -
8
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. -
9
STORM
An LLM-powered knowledge curation system that researches topics
STORM is an open-source virtual assistant framework developed by Stanford's OVAL lab. It is designed for creating natural language interfaces and assistants that can interact with APIs, databases, and services in a modular way. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
10
AIVA (A.I. Virtual Assistant)
AIVA (A.I. Virtual Assistant): General-purpose virtual assistant
AIVA is a general-purpose virtual assistant designed for developers, enabling the creation of customizable AI assistants for various applications. -
11
Lingua-Py
The most accurate natural language detection library for Python
Its task is simple: It tells you which language some text is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. Language detection is often done as part of large machine learning frameworks or natural language processing applications. In cases where you don't need the full-fledged functionality of those systems or don't want to learn the ropes of those, a small flexible library comes in handy. -
12
AminePlatform
Amine is a Multi-Layer Platform for the dev. of Intelligent Systems
Amine is an Artificial Intelligence Multi-Layer Java Open Source Platform dedicated to the development of various kinds of Intelligent Systems and Agents (Knowledge-Based, Ontology-Based, Conceptual Graph -CG- Based, NLP, Reasoning and Learning, Natural Language Processing, etc.). Ontology, KB can be created and manipulated with various processes. CG theory is used as the main knowledge representation language. Amine provides two languages: PROLOG+CG which extends PROLOG with CG and Amine modules, and SYNERGY which is a visual activation/propagation based language. CGs are considered by SYNERGY as activable/executable graphs. See for more detail: //amine-platform.sourceforge.net/ -
13
Haystack
Haystack is an open source NLP framework to interact with your data
Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning, not just keywords! Make use of and compare the latest pre-trained transformer-based languages models like OpenAI’s GPT-3, BERT, RoBERTa, DPR, and more. Pick any Transformer model from Hugging Face's Model Hub, experiment, find the one that works. Use Haystack NLP components on top of Elasticsearch, OpenSearch, or plain SQL. Boost search performance with Pinecone, Milvus, FAISS, or Weaviate vector databases, and dense passage retrieval. -
14
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. -
15
Super comprehensive deep learning notes
Super Comprehensive Deep Learning Notes
Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural network designs like ResNet, transformers, and object detection algorithms. It’s not just a dry code repository; it includes theoretical explanations alongside hands-on examples, loss function explorations, optimization routines, and full end-to-end experiments on real datasets, making it highly suitable for both self-study and classroom use. -
16
Chonkie
The no-nonsense RAG chunking library
Chonkie is an AI-powered framework designed for building conversational agents and chatbots with natural language understanding and multi-turn conversation support. -
17
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. -
18
Colossal-AI
Making large AI models cheaper, faster and more accessible
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing. Together with better performance come larger model sizes. This imposes challenges to the memory wall of the current accelerator hardware such as GPU. It is never ideal to train large models such as Vision Transformer, BERT, and GPT on a single GPU or a single machine. There is an urgent demand to train models in a distributed environment. However, distributed training, especially model parallelism, often requires domain expertise in computer systems and architecture. It remains a challenge for AI researchers to implement complex distributed training solutions for their models. Colossal-AI provides a collection of parallel components for you. We aim to support you to write your distributed deep learning models just like how you write your model on your laptop. -
19
Delta ML
Deep learning based natural language and speech processing platform
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. DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users. It helps you to train, develop, and deploy NLP and/or speech models. Use configuration files to easily tune parameters and network structures. What you see in training is what you get in serving: all data processing and features extraction are integrated into a model graph. Text classification, named entity recognition, question and answering, text summarization, etc. Uniform I/O interfaces and no changes for new models. -
20
KoGPT
KakaoBrain KoGPT (Korean Generative Pre-trained Transformer)
KoGPT is a Korean language model based on OpenAI’s GPT architecture, designed for various natural language processing (NLP) tasks such as text generation, summarization, and dialogue systems. -
21
Synonyms
Chinese synonyms, chat robot, intelligent question and answer toolkit
Chinese Synonyms for natural language processing and understanding. Better Chinese synonyms, chatbot, intelligent question and answer toolkit. synonymsCan be used for many tasks in natural language understanding, text alignment, recommendation algorithms, similarity calculation, semantic shifting, keyword extraction, concept extraction, automatic summarization, search engines, etc. Print synonyms in a friendly way for easy debugging. "Synonyms Cilin" was compiled by Mei Jiaju and others in 1983, and now widely used is "Synonyms Cilin Extended Edition" maintained by the Social Computing and Information Retrieval Research Center of Harbin Institute of Technology. Classes and subclasses, sort out the relationship between words, the extended version of the synonym word forest contains more than 70,000 words, of which more than 30,000 words are shared in the form of open data. -
22
Text Generation Inference
Large Language Model Text Generation Inference
Text Generation Inference is a high-performance inference server for text generation models, optimized for Hugging Face's Transformers. It is designed to serve large language models efficiently with optimizations for performance and scalability. -
23
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. -
24
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. -
25
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.
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.
