Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files" - Page 3
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Showing 118 open source projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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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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Common Resource Grep - crgrep
Common Resource Grep
CRGREP searches for matching text in databases, various document formats, archives and other difficult to access resources. A command line tool for name and content text matching in database tables, plain files, MS Office documents, PDF, archives, MP3 audio, image meta-data, scanned documents, maven dependencies and web resources. CRGREP will search resources within resources of any arbitrary combination or depth, so text within a document within a zip archive, and so on. Here you will find binary downloads and discussion (https://sourceforge.net/p/crgrep/discussion/) . ... -
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AllenNLP
An open-source NLP research library, built on PyTorch
AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional... -
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Emb-GAM
An interpretable and efficient predictor using pre-trained models
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for... -
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BioNLP is an initiative by the University of Colorado Denver Health Sciences Center to create and distribute code, software, and data for applying natural language processing techniques to biomedical texts
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GR4VY: Payment Orchestration PlatformGr4vy’s payment orchestration platform empowers enterprise merchants and platforms to optimize their stack and create bespoke checkout experiences, giving you full control over your payment strategy.
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Graph4NLP
Graph4nlp is the library for the easy use of Graph Neural Networks
...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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MITRE Annotation Toolkit
A toolkit for managing and manipulating text annotations
...It can be customized for specific tasks (e.g., named entity identification, de-identification of medical records). The goal of MAT is not to help you configure your training engine (in the default case, the Carafe CRF system) to achieve the best possible performance on your data. MAT is for "everything else": all the tools you end up wishing you had. -
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Data augmentation
List of useful data augmentation resources
List of useful data augmentation resources. You will find here some links to more or less popular github repos, libraries, papers, and other information. Data augmentation can be simply described as any method that makes our dataset larger. To create more images for example, we could zoom in and save a result, we could change the brightness of the image or rotate it. -
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libpostal
A C library for parsing/normalizing street addresses around the world
A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data. libpostal is a C library for parsing/normalizing street addresses around the world using statistical NLP and open data. The goal of this project is to understand location-based strings in every language, everywhere. Addresses and the locations they represent are essential for any application dealing with maps (place search, transportation, on-demand/delivery services, check-ins, reviews). ... -
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XLM (Cross-lingual Language Model)
PyTorch original implementation of Cross-lingual Language Model
XLM (Cross-lingual Language Model) is a family of multilingual pretraining methods that align representations across languages to enable strong zero-shot transfer. It popularized objectives like Masked Language Modeling (MLM) across many languages and Translation Language Modeling (TLM) that jointly trains on parallel sentence pairs to tighten cross-lingual alignment. Using a shared subword vocabulary, XLM learns language-agnostic features that work well for classification and sequence labeling tasks such as XNLI, NER, and POS without target-language supervision. ... -
Empower Your Contact Center with Human-Like AI ConversationsEnterprise Bot, based in Switzerland, is a pioneer in Conversational AI, Process Automation, and Generative AI. With the trust of esteemed enterprise giants across industries like Generali, SIX, SBB, DHL, and SWICA, Enterprise Bot is revolutionizing both customer and employee experiences. Through its advanced integration with Large Language Models (LLM) such as ChatGPT and Llama 2, and its unique patent-pending DocBrain technology, the company delivers unparalleled personalization, active engagement, and omnichannel solutions across platforms like email, voice, and chat. Furthermore, Enterprise Bot integrates with existing core systems, such as SAP, CRMs, Confluence and more, and with its proprietary middleware, Blitzico, enables the AI to not only respond to queries but also take action to resolve them. This dedication to innovation in four main use case areas, Customer Support, Sales and Marketing, Knowledge Management and Digital Coworker, elevates both CX and employee productivity.
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Duckling
Language, engine, and tooling for testing composable language rules
Duckling is a Haskell library developed by Facebook for parsing and normalizing natural language expressions into structured data. It supports a wide range of entities such as dates, times, durations, distances, temperatures, numbers, and currencies. Designed for use in conversational agents, chatbots, and natural language processing applications, Duckling converts fuzzy user input into a consistent and machine-readable format. It features multi-language support and is widely used in production environments requiring robust entity extraction. -
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Self-Attentive Parser
High-accuracy NLP parser with models for 11 languages
LightAutoML is an automated machine learning (AutoML) framework developed by Sberbank AI Lab, designed to facilitate the development of machine learning models with minimal human intervention. -
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Synonyms
Chinese synonyms, chat robot, intelligent question and answer toolkit
...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. -
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Parsr
Transforms PDF, Documents and Images into Enriched Structured Data
Parsr is an open-source document parsing tool that converts PDFs, scanned images, and other structured documents into structured, machine-readable data formats. -
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NLP Architect
A model library for exploring state-of-the-art deep learning
...The library is designed to be a tool for model development: data pre-processing, build model, train, validate, infer, save or load a model. -
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models (covering tasks such as Chinese word segmentation, named entity recognition, syntactic analysis, text classification, text matching, metaphor resolution, summarization, etc.). ... -
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CC-Net
Tools to download and cleanup Common Crawl data
cc_net provides tools to download, segment, clean, and filter Common Crawl to build large-scale text corpora, including monolingual datasets and the multilingual CC-100 collection introduced in the associated paper. It includes pipelines to fetch snapshots, extract text, de-duplicate, identify language, and apply quality filtering based on heuristics and language models. The outputs are intended for pretraining language models and for creating standardized corpora that can be reproduced or... -
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GluonNLP
NLP made easy
GluonNLP is a toolkit that helps you solve NLP problems. It provides easy-to-use tools that helps you load the text data, process the text data, and train models. To facilitate both the engineers and researchers, we provide command-line-toolkits for downloading and processing the NLP datasets. Gluon NLP makes it easy to evaluate and train word embeddings. Here are examples to evaluate the pre-trained embeddings included in the Gluon NLP toolkit as well as example scripts for training embeddings on custom datasets. ... -
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Delta ML
Deep learning based natural language and speech processing platform
...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. -
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NLP Best Practices
Natural Language Processing Best Practices & Examples
In recent years, natural language processing (NLP) has seen quick growth in quality and usability, and this has helped to drive business adoption of artificial intelligence (AI) solutions. In the last few years, researchers have been applying newer deep learning methods to NLP. Data scientists started moving from traditional methods to state-of-the-art (SOTA) deep neural network (DNN) algorithms which use language models pretrained on large text corpora. This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language. ... -
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cocoNLP
A Chinese information extraction tool
...Its API is intentionally simple, so you can drop it into scripts, ETL jobs, or dashboards without deep ML expertise. Because it aims at utility over complexity, it’s useful for prototyping data products or building lightweight text analytics where large models would be overkill. The repository also includes examples and test snippets to help you understand expected inputs and typical outputs, which shortens the learning curve for newcomers. -
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KSUCCA Corpus
A 50 million tokens corpus of Classical Arabic.
King Saud University Corpus of Classical Arabic (KSUCCA) is a pioneering 50 million tokens annotated corpus of Classical Arabic texts from the period of pre-Islamic era until the fourth Hijri century (equivalent to the period from the seventh until early eleventh century CE), which is the period of pure classical Arabic. The main aim of this corpus is to be used for studying the distributional lexical semantics of The Quran words. However, it can be used for other research purposes, such... -
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VecText
Converting text to a structured representation
...In the command-line interface mode, all options need to be specified using the command line parameters. This way of non-interactive communication enables incorporating the application into a more complicated data mining process integrating several software packages or performing multiple conversions in a batch. -
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Chatito
Dataset generation for AI chatbots, NLP tasks
Chatito is a tool that helps generate datasets for training and validating chatbot models using a simple domain-specific language (DSL). -
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Rasa-UI
Rasa UI is a frontend for the Rasa Framework
Rasa UI is a web application built on top of, and for Rasa. Rasa UI provides a web application to quickly and easily be able to create and manage bots, NLU components (Regex, Examples, Entities, Intents, etc.) and Core components (Stories, Actions, Responses, etc.) through a web interface. It also provides some convenience features for Rasa, like training and loading your models, monitoring usage or viewing logs. -
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OLiA
OWL/DL ontologies for linguistic annotations
MOVED TO https://github.com/acoli-repo/olia. The Ontologies of Linguistic Annotations (OLiA) provide an OWL/DL taxonomy of data categories as a reference for linguistic annotation (OLiA Reference Model), plus OWL/DL models for a large number of annotation schemes (OLiA Annotation Models) and their relationship to reference data categories (OLiA Linking Models). The OLiA Reference Model itself is linked to community-maintained repositories such as GOLD (http://linguistics-ontology.org/) and ISOcat (http://www.isocat.org) The OLiA ontologies were originally developed as part of an infrastructure for the sustainable maintenance of linguistic resources (http://www.sfb441.uni-tuebingen.de/c2/index-engl.html), their fields of application include the formalization of annotation schemes, concept-based querying over heterogeneously annotated corpora, and the development of interoperable NLP pipelines.
