Open Source Natural Language Processing (NLP) Tools
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Natural Language Processing (NLP) Tools
View 188 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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Outbound sales softwareAdversus is an outbound dialing solution that helps you streamline your call strategies, automate manual processes, and provide valuable insights to improve your outbound workflows and efficiency.
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MicroStation by Bentley Systems is the trusted computer-aided design (CAD) software built specifically for infrastructure design.MicroStation is the only computer-aided design software for infrastructure design, helping architects and engineers like you bring their vision to life, present their designs to their clients, and deliver their projects to the community.
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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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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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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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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 -
Iris Powered By Generali - Iris puts your customer in control of their identity.Iris Identity Protection API sends identity monitoring and alerts data into your existing digital environment – an ideal solution for businesses that are looking to offer their customers identity protection services without having to build a new product or app from scratch.
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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. -
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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. -
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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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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. -
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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. -
Get full visibility and control over your tasks and projects with Wrike.Wrike offers world-class features that empower cross-functional, distributed, or growing teams take their projects from the initial request stage all the way to tracking work progress and reporting results.
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Search-Index
A persistent, network resilient, full text search library
Search-Index is a lightweight and fast JavaScript-based search engine that enables full-text search indexing and retrieval for web applications. -
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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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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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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. -
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AutoGPTQ
An easy-to-use LLMs quantization package with user-friendly apis
AutoGPTQ is an implementation of GPTQ (Quantized GPT) that optimizes large language models (LLMs) for faster inference by reducing their computational footprint while maintaining accuracy. -
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BEIR
A Heterogeneous Benchmark for Information Retrieval
BEIR is a benchmark framework for evaluating information retrieval models across various datasets and tasks, including document ranking and question answering. -
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DOLMA
Data and tools for generating and inspecting OLMo pre-training data
DOLMA (Data Optimization and Learning for Model Alignment) is a framework designed to manage large-scale datasets for training and fine-tuning language models efficiently. -
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NLG-Eval
Evaluation code for various unsupervised automated metrics
NLG-Eval is a toolkit for evaluating the quality of natural language generation (NLG) outputs using multiple automated metrics such as BLEU, METEOR, and ROUGE. -
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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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Botonic
Build chatbots and conversational experiences using React
Botonic is a full-stack Javascript framework to create chatbots and modern conversational apps that work on multiple platforms, web, mobile and messaging apps (Messenger, Whatsapp, Telegram, etc). Building modern applications on top of messaging apps like Whatsapp or Messenger is much more than creating simple text-based chatbots. Botonic is a full-stack serverless framework that combines the power of React and Tensorflow.js to create amazing experiences at the intersection of text and graphical interfaces. With Botonic you can focus on creating the best conversational experience for your users instead of dealing with different messaging APIs, AI/NLP complexity or managing and scaling infrastructure. It also comes with a battery of plugins so you can easily integrate popular services into your project. -
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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. -
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Diffgram
Training data (data labeling, annotation, workflow) for all data types
From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality. Training Data is the art of supervising machines through data. This includes the activities of annotation, which produces structured data; ready to be consumed by a machine learning model. Annotation is required because raw media is considered to be unstructured and not usable without it. That’s why training data is required for many modern machine learning use cases including computer vision, natural language processing and speech recognition. -
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Ecco
Explain, analyze, and visualize NLP language models
Ecco is an interpretability tool for transformers that helps visualize and analyze how language models generate text, making model behavior more transparent. -
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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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VnCoreNLP
A Vietnamese natural language processing toolkit
VnCoreNLP is a Java-based natural language processing toolkit tailored for Vietnamese. It offers a fast and accurate pipeline for essential NLP tasks, facilitating research and application development in Vietnamese language processing. -
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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.
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.
