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Showing 42 open source projects for "two"
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Spark NLP
State of the Art Natural Language Processing
...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. ... -
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natural
General natural language facilities for node
"Natural" is a general natural language facility for nodejs. It offers a broad range of functionalities for natural language processing. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported. It’s still in the early stages, so we’re very interested in bug reports, contributions and the like. Note that many algorithms from Rob Ellis’s node-nltools are being merged into this project and will be maintained from here... -
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LexiFinder
AI-powered semantic indexing: automating the creation of book indexes
...Given one or more source documents and a set of keywords, it extracts all nouns, compares them semantically to the keywords using a pretrained NLP model, and produces a structured, hierarchical index ready to be included in a book or manuscript. LexiFinder works in two ways: as a command-line tool for scripting, automation, and batch processing, and as a graphical application for a guided, point-and-click experience. Both interfaces share the same underlying engine and support the same features. Supported input formats are PDF, DOCX, and ODT. The index can be exported as plain text, JSON, CSV, or HTML. -
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CapyBro
Rewrite, fix, or translate text in any Windows app via a hotkey
...Select text in any Windows app — Word, Outlook, a browser field, VS Code, Telegram — press a global hotkey (Ctrl+Shift+E), and AI rewrites, fixes grammar, translates, or paraphrases it in place. Two backends: **OpenRouter** (cloud — GPT-4o, Claude, Gemini, DeepSeek, your own key) or **Ollama** (fully local/offline — text never leaves the machine). Native .NET 8 / WPF, ~48 MB, no Electron, zero telemetry. Unlimited custom prompts with per-prompt models, diff preview, history. UI in English, Ukrainian, Russian. Free and open-source core (MIT). ... -
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Chinese-LLaMA-Alpaca 2
Chinese LLaMA-2 & Alpaca-2 Large Model Phase II Project
This project is developed based on the commercially available large model Llama-2 released by Meta. It is the second phase of the Chinese LLaMA&Alpaca large model project. The Chinese LLaMA-2 base model and the Alpaca-2 instruction fine-tuning large model are open-sourced. These models expand and optimize the Chinese vocabulary on the basis of the original Llama-2, use large-scale Chinese data for incremental pre-training, and further improve the basic semantics and command understanding of Chinese. ... -
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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/ -
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Transformers-Interpret
Model explainability that works seamlessly with Hugging Face
Transformers-Interpret is an interpretability tool for Transformer-based NLP models, providing insights into attention mechanisms and feature importance. -
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Graph4NLP
Graph4nlp is the library for the easy use of Graph Neural Networks
...The architecture of Graph4NLP is shown in the following figure, where boxes with dashed lines represent the features under development. Graph4NLP consists of four different layers: 1) Data Layer, 2) Module Layer, 3) Model Layer, and 4) Application Layer. Graph4nlp aims to make it incredibly easy to use GNNs in NLP tasks (check out Graph4NLP Documentation). -
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masmt
A frame work for Multi agent system development
...Rzevski, Multi-agent solution for managing complexity in English to Sinhala Machine Translation, International Journal of Design & Nature and Ecodynamics, Volume 11, Issue 2, 2016, 88 – 96. B. Hettige, A. S. Karunananda, G. Rzevski, ” MaSMT: A Multi-agent System Development Framework for English-Sinhala Machine Translation”, International Journal of Computational Linguistics and Natural Language Processing (IJCLNLP), Volume 2 Issue 7 July 2013. -
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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Texar
Toolkit for Machine Learning, Natural Language Processing
...Texar was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. A mirror of this repository is maintained by Petuum Open Source. Two Versions, (Mostly) Same Interfaces. Texar-TensorFlow (this repo) and Texar-PyTorch have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Rich Pre-trained Models, Rich Usage with Uniform Interfaces. BERT, GPT2, XLNet, etc, for encoding, classification, generation, and composing complex models with other Texar components! -
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Safe Harbor Deidentification
Safe Harbor Deidentification for medical documents
Phalanx - Deidentify Safe Harbor Deidentification Mode of Phalanx is an abridged pipeline of NLP annotators culminating in NER annotators which write output of text offsets. It uses the Safe Harbor deidentification method. -
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Arabic Corpus
Text categorization, arabic language processing, language modeling
...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. ... -
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CRP - Chemical Reaction Prediction
Predicting Organic Reactions using Neural Networks.
...This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule. Beam search is used in Version 2, to generate top 5 predictions. Maximum input length for the model is 15 (excluding spaces). -
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popt4jlib
Parallel Optimization Library for Java
popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known... -
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Seq2seq Chatbot for Keras
This repository contains a new generative model of chatbot
This repository contains a new generative model of chatbot based on seq2seq modeling. The trained model available here used a small dataset composed of ~8K pairs of context (the last two utterances of the dialogue up to the current point) and respective response. The data were collected from dialogues of English courses online. This trained model can be fine-tuned using a closed-domain dataset to real-world applications. The canonical seq2seq model became popular in neural machine translation, a task that has different prior probability distributions for the words belonging to the input and output sequences since the input and output utterances are written in different languages. ... -
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...The project also includes CYMRIE an adapted version for Welsh of the GATE - ANNIE Named Entity Recognition (NER) application for a range of entities such as Persons, Organisations, Locations, and date and time expressions. Version 2.x The CYMRIE pipeline is accessible via a API, standalone GUI and CLI. The CymrIE pipeline has also been adapted for Twitter.
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Ansj Chinese word segmentation
Ansj word segmentation
...Chinese word segmentation, name recognition, part-of-speech tagging, user-defined dictionary. This is a java implementation of Chinese word segmentation based on n-Gram+CRF+HMM. The word segmentation speed reaches about 2 million words per second (tested under mac air), and the accuracy rate can reach more than 96%. At present, it has realized the functions of Chinese word segmentation, Chinese name recognition, user-defined dictionary, keyword extraction, automatic summarization, and keyword tagging. It can be applied to natural language processing and other aspects, and is suitable for various projects that require high word segmentation effects. -
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Virastyar
Virastyar is an spell checker for low-resource languages
...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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...Original: No pre-processing step is applied. 2. FPS-5: The first five characters of terms are selected as stem and stop-words elimination is performed. 3. FPS-7: The first seven characters of terms are selected as stem and stop-words elimination is performed. 4. Zemberek-Stemmed: Zemberek NLP toolkit is utilized for stemming and stop-words elimination is perfo
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Natural Language Analysis with Ngrams
NLP tool for statistical analysis of words, sentences, documents
...Furthermore, there will be a github version for anyone who wishes to contribute. In the future versions, user will be able to convert a single word to numerical data, to be able to compare two words and get the comparison data, and to be able to do the same for the sentences, paragraphs and documents. I will JAR-it once I decide that it can be called a final release. This project was made by creating a corpus from the Google Ngrams data for English Language, version 20120701. EOWL list of English words was used to filter-out the words from Ngrams data. ... -
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Free and open source grammar checker. Currently capable of identifying errors: 1) Incomplete sentence (fragment) 2) Subject Verb Plurality Agreement Incomplete Sentence Example: "John a very man." Subject Verb Plurality Agreement Example: "They walks into a classroom." This software utilizes parts-of-speech tagging software that was developed and published by the Natural Language Processing Group at Stanford University. Many thanks!!!
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training with NLP
Making training programs work with chunking strategies
Strategies make up everything we do. We have thinking strategies that are effective. We have thinking strategies that are less so! It has been proven possible to secure elements of successful strategies and apply these to our training of all kinds of skills. Looking at the practices of successful people, and breaking down the parts of their effective behaviours we can teach these in order to improve particular areas of effectiveness. This is the basis of this learning and teaching strategy! -
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Stella
Elementary Arithmetic Word Problem Solver
Basic natural language processor capable of solving elementary math word problems such as: 1) John has 7 pencils. He buys 10 more pencils. How many pencils does he have in all? 2) There are 6 boys and 4 girls in a library. How many kids are there altogether? 3) Bob ran 8 miles and Stella ran 2 miles. How many miles did they run altogether? 4) What is the sum of 7 and 2? 5) What is 8 divided by 2? 6) What is the 10 subtracted by 2? 7) What is 2 subtracted from 10? Also shows users how answer was achieved. ... -
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KALIMAT Multipurpose Arabic Corpus
A corpus that could be of help for researchers working on Arabic NLP
KALIMAT a Multipurpose Arabic Corpus We are pleased to announce the immediate availability of KALIMAT 1.0, KALIMAT is an Arabic natural language resource that consists of: 1) 20,291 Arabic articles collected from the Omani newspaper Alwatan by (Abbas et al. 2011). 2) 20,291 Extractive Single-document system summaries. 3) 2,057 Extractive Multi-document system summaries. 4) 20,291 Named Entity Recognised articles. 5) 20,291 Part of Speech Tagged articles. 6) 20,291 Morphologically Analyse articles. The data collection articles fall into six categories: culture, economy, local-news, international-news, religion, and sports. ... -
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...It uses Natural Language Processing techniques and Machine learning algorithms to generate specific trigger questions. If you use this software in a publication, please cite the paper 2. 1.Ming Liu and Rafael A. Calvo (2012) “Using Information Extraction to Generate Trigger Question for Academic Writing Support”, 11th International Conference on Intelligent Tutoring Systems, Crete, Springer LNCS 7315, p.360-369. 2.Ming Liu, Rafael A. Calvo, Anindito Aditomo and Luiz Augusto Pizzato (2012), “Using Wikipedia and Conceptual Graph Structures to Generate Questions for Academic Writing Support”, IEEE Transactions on Learning Technologies, vol. 5, no. 3, pp. 251-263.
