Open Source Windows Natural Language Processing (NLP) Tools
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Natural Language Processing (NLP) Tools for Windows
View 40 business solutionsBrowse free open source Natural Language Processing (NLP) tools and projects for Windows 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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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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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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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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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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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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Transformers4Rec
Transformers4Rec is a flexible and efficient library
Transformers4Rec is an advanced recommendation system library that leverages Transformer models for sequential and session-based recommendations. The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners. Traditional recommendation algorithms usually ignore the temporal dynamics and the sequence of interactions when trying to model user behavior. Generally, the next user interaction is related to the sequence of the user's previous choices. In some cases, it might be a repeated purchase or song play. User interests can also suffer from interest drift because preferences can change over time. Those challenges are addressed by the sequential recommendation task. -
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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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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. -
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.
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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. -
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diff2html
Pretty diff to html javascript library (diff2html)
Each diff provides a comprehensive visualization of the code changes, helping developers identify problems and better understand the changes. Each diff features a line-by-line and side-by-side preview of your changes. All the code changes are syntax highlighted using highlight.js, providing more readability. Similar lines are paired, allowing for easier change tracking. We work hard to make sure you can have your diffs in a simple and flexible way. The AI community building the future. Build, train and deploy state of the art models powered by the reference open source in natural language processing. Wrapper and helper adding syntax highlight, synchronized scroll, and other nice features. You can use it without syntax highlight or by passing your own implementation with the languages you prefer. Diff2Html can be used in various ways as listed in the distributions section. -
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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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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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Stanford CoreNLP
Stanford CoreNLP, a Java suite of core NLP tools
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. CoreNLP currently supports 6 languages, Arabic, Chinese, English, French, German, and Spanish. The centerpiece of CoreNLP is the pipeline. Pipelines take in raw text, run a series of NLP annotators on the text, and produce a final set of annotations. Pipelines produce CoreDocuments, data objects that contain all of the annotation information, accessible with a simple API, and serializable to a Google Protocol Buffer. CoreNLP generates a variety of linguistic annotations, including parts of speech, named entities, dependency parses, and coreference. -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep integration with the Hugging Face Hub, allowing you to easily load and share a dataset with the wider NLP community. There are currently over 2658 datasets, and more than 34 metrics available. Datasets naturally frees the user from RAM memory limitation, all datasets are memory-mapped using an efficient zero-serialization cost backend (Apache Arrow). Smart caching: never wait for your data to process several times. -
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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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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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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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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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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. -
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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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Arabic Corpus
Text categorization, arabic language processing, language modeling
The Arabic Corpus {compiled by Dr. Mourad Abbas ( http://sites.google.com/site/mouradabbas9/corpora ) The corpus Khaleej-2004 contains 5690 documents. It is divided to 4 topics (categories). The corpus Watan-2004 contains 20291 documents organized in 6 topics (categories). Researchers who use these two corpora would mention the two main references: (1) For Watan-2004 corpus ---------------------- M. Abbas, K. Smaili, D. Berkani, (2011) Evaluation of Topic Identification Methods on Arabic Corpora,JOURNAL OF DIGITAL INFORMATION MANAGEMENT,vol. 9, N. 5, pp.185-192. 2) For Khaleej-2004 corpus --------------------------------- M. Abbas, K. Smaili (2005) Comparison of Topic Identification Methods for Arabic Language, RANLP05 : Recent Advances in Natural Language Processing ,pp. 14-17, 21-23 september 2005, Borovets, Bulgary. More useful references to check: ------------------------------------------- https://sites.google.com/site/mouradabbas9/corpora -
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Adapters
A Unified Library for Parameter-Efficient Learning
Adapters is an add-on library to HuggingFace's Transformers, integrating 10+ adapter methods into 20+ state-of-the-art Transformer models with minimal coding overhead for training and inference. Adapters provide a unified interface for efficient fine-tuning and modular transfer learning, supporting a myriad of features like full-precision or quantized training (e.g. Q-LoRA, Q-Bottleneck Adapters, or Q-PrefixTuning), adapter merging via task arithmetics or the composition of multiple adapters via composition blocks, allowing advanced research in parameter-efficient transfer learning for NLP tasks. -
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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.
