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29 个仓库

Awesome GitHub RepositoriesText and NLP Preprocessing

Specialized utilities for cleaning, tokenizing, and formatting text strings specifically for natural language processing or UI presentation.

Explore 29 awesome GitHub repositories matching data & databases · Text and NLP Preprocessing. Refine with filters or upvote what's useful.

Awesome Text and NLP Preprocessing GitHub Repositories

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  • vuejs/vuevuejs 的头像

    vuejs/vue

    209,900在 GitHub 上查看↗

    Vue 是一个渐进式的、基于组件的 JavaScript 框架,旨在构建响应式用户界面和单页应用程序。它以声明式模板系统为中心,将 HTML 转换为高效的渲染函数,允许开发者将复杂的界面组织成自动与应用程序状态同步的隔离、可复用单元。 该框架通过依赖跟踪响应式系统脱颖而出,该系统在渲染期间监控数据访问以触发精确更新。它提供了一个灵活的架构,支持作为轻量级库的增量采用和全规模应用程序开发。开发者可以利用强大的基于插件的扩展模型来注入全局逻辑,同时框架的虚拟 DOM 对账确保通过计算最小突变来实现高效的界面更新。 除了核心渲染能力外,该项目还包括一套全面的工具,用于管理应用程序状态、基于 URL 的路由和服务器端渲染。它为组件组合、内容分发和动画管理提供了广泛支持,并内置了自动内容转义等安全措施以防止常见漏洞。 该框架随附官方类型声明以支持静态分析,并可通过标准包管理器安装,或通过脚本标签直接集成到浏览器环境中。

    Allows defining formatting functions locally within components to override global filters.

    TypeScriptframeworkfrontendjavascript
    在 GitHub 上查看↗209,900
  • avelino/awesome-goavelino 的头像

    avelino/awesome-go

    175,576在 GitHub 上查看↗

    This project serves as a comprehensive language ecosystem index, functioning as a centralized, community-curated directory for the Go programming language. It organizes a vast landscape of software components, libraries, and development tools into a structured, navigable hierarchy, enabling developers to efficiently discover resources tailored to specific functional domains. The repository distinguishes itself through a decentralized contribution model, where community-driven updates ensure the index remains current with the rapidly evolving software landscape. Beyond simple resource listing,

    Offers libraries for parsing, formatting, and manipulating text data.

    Goawesomeawesome-listgo
    在 GitHub 上查看↗175,576
  • d2l-ai/d2l-zhd2l-ai 的头像

    d2l-ai/d2l-zh

    78,493在 GitHub 上查看↗

    This project is an open-source, interactive educational platform designed to teach deep learning through a comprehensive, code-first curriculum. It provides a structured learning path that covers foundational mathematics, modern neural network architectures, and practical optimization techniques, enabling practitioners to master complex artificial intelligence concepts through hands-on experimentation. The platform distinguishes itself by integrating technical explanations with executable Jupyter notebooks. This design allows readers to modify code and hyperparameters in real-time, facilitati

    Demonstrates practical workflows for cleaning, tokenizing, and preparing diverse text data for downstream natural language processing tasks.

    Pythonbookchinesecomputer-vision
    在 GitHub 上查看↗78,493
  • angular/angular.jsangular 的头像

    angular/angular.js

    58,615在 GitHub 上查看↗

    AngularJS is a structural framework for building dynamic web applications by extending standard HTML with custom tags and attributes. It operates as a client-side template engine that transforms declarative markup into interactive components, organizing application logic through a model-view-controller pattern. By utilizing a centralized dependency injection container, the framework manages the lifecycle of services and components to ensure modularity and maintainable architecture. The framework is defined by its two-way data binding mechanism, which automatically synchronizes data models wit

    Modifies text presentation within templates through reusable filters that ensure consistent casing and formatting across the interface.

    JavaScript
    在 GitHub 上查看↗58,615
  • prefecthq/fastmcpPrefectHQ 的头像

    PrefectHQ/fastmcp

    22,994在 GitHub 上查看↗

    FastMCP is a Python framework designed for building servers that expose functions, resources, and prompts to AI models using the Model Context Protocol. It simplifies the development process by automatically deriving tool metadata, input schemas, and documentation directly from Python function signatures and type hints. The framework provides a unified container for managing these components, allowing developers to build modular applications that integrate seamlessly with AI assistants. The project distinguishes itself through its support for interactive, server-defined user interface compone

    Restricts component selection to specific versions using normalized comparisons that ignore prefix formatting and semantic equivalence.

    Pythonagentsfastmcpllms
    在 GitHub 上查看↗22,994
  • livekit/livekitlivekit 的头像

    livekit/livekit

    19,358在 GitHub 上查看↗

    LiveKit is a comprehensive framework for building and orchestrating real-time, multimodal AI agents that interact with users through voice, video, and text. It provides a centralized, event-driven architecture to manage the entire lifecycle of automated participants, from initialization and session state management to graceful shutdown. By utilizing a selective forwarding unit, the platform efficiently routes media streams between participants and agents, ensuring low-latency communication and secure, token-based authentication for all connections. The platform distinguishes itself through it

    Filters or formats text output during the transcription process to clean up punctuation and normalize formatting.

    Gogolangmedia-serversfu
    在 GitHub 上查看↗19,358
  • twpayne/chezmoitwpayne 的头像

    twpayne/chezmoi

    18,075在 GitHub 上查看↗

    chezmoi is a command-line utility designed to manage and synchronize system configuration files across multiple machines. It uses a local Git repository as the single source of truth, allowing users to track, version, and distribute dotfiles while maintaining a consistent state across diverse operating systems and hardware architectures. The project distinguishes itself through a declarative reconciliation model that computes the difference between the current filesystem and the desired state defined in the repository. It features a robust templating engine that processes configuration files

    Applies prefix-based formatting to lines within text blocks to ensure consistent configuration syntax.

    Goconfigurationdotfiledotfile-management
    在 GitHub 上查看↗18,075
  • cjpais/handycjpais 的头像

    cjpais/Handy

    15,515在 GitHub 上查看↗

    Handy is a local speech-to-text automation tool designed to convert spoken audio into text and inject it directly into active desktop applications. By running machine learning models entirely on the host hardware, it provides a private, offline-first environment for dictation and command execution. The system functions as a background service that manages microphone input, transcription state, and text output, enabling hands-free typing across various software environments. The project distinguishes itself through a modular pipeline that integrates local language models for post-transcription

    Automatically appends trailing spaces to facilitate faster consecutive phrase input.

    Rustaccessibilitycross-platformspeech-to-text
    在 GitHub 上查看↗15,515
  • sparanoid/chinese-copywriting-guidelinessparanoid 的头像

    sparanoid/chinese-copywriting-guidelines

    15,218在 GitHub 上查看↗

    This project provides a comprehensive style guide and automated framework for standardizing Chinese typography and technical writing. It establishes a formal set of rules for formatting, spacing, and punctuation, ensuring that mixed-language content maintains professional consistency and visual clarity. The tool distinguishes itself by enforcing specific typographic standards, such as normalizing character widths, managing mixed-language spacing, and standardizing quotation marks and punctuation. It utilizes a deterministic processing pipeline to apply these rules across documentation and sou

    Automates text formatting across source code and documentation to ensure professional presentation.

    chinesechinese-simplifiedchinese-traditional
    在 GitHub 上查看↗15,218
  • textmate/textmatetextmate 的头像

    textmate/textmate

    14,783在 GitHub 上查看↗

    TextMate is a programmable text editor designed for software development and project management. It functions as a highly customizable environment where users can define language-specific behaviors, syntax highlighting rules, and automated workflows to suit their individual development needs. The editor distinguishes itself through a modular, bundle-based extensibility model that allows for deep integration with system shell commands. By piping document buffers through external scripts and command-line tools, users can perform complex text transformations, automate file lifecycle tasks, and b

    Pipes selected text through external shell commands to process, format, or transform content.

    Objective-C++c-plus-pluscocoamacos
    在 GitHub 上查看↗14,783
  • rasbt/python-machine-learning-bookrasbt 的头像

    rasbt/python-machine-learning-book

    12,614在 GitHub 上查看↗

    This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ

    Cleans raw text and performs tokenization to prepare documents for feature extraction.

    Jupyter Notebook
    在 GitHub 上查看↗12,614
  • crowdsecurity/crowdseccrowdsecurity 的头像

    crowdsecurity/crowdsec

    12,574在 GitHub 上查看↗

    CrowdSec is a collaborative, distributed security engine designed for threat detection and infrastructure protection. It functions as an intrusion detection system that parses logs and network traffic to identify malicious patterns, utilizing a bucket-based threshold detection model to aggregate events and trigger alerts. The platform is built on a modular architecture that includes a centralized local API server for managing security signals and a relational database for persistent storage of remediation decisions. What distinguishes the project is its decoupled enforcement model, which offl

    Identifies discrepancies between local security rules and upstream versions to manage configuration drift.

    Goattacks-preventiondetectionids
    在 GitHub 上查看↗12,574
  • karpathy/minbpekarpathy 的头像

    karpathy/minbpe

    10,582在 GitHub 上查看↗

    Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

    Implements regex-based text splitting by category to prevent cross-category BPE merges during tokenization.

    Python
    在 GitHub 上查看↗10,582
  • autogluon/autogluonautogluon 的头像

    autogluon/autogluon

    9,997在 GitHub 上查看↗

    AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

    Tokenizes and concatenates multiple text fields into single sequences for model consumption.

    Pythonautogluonautomated-machine-learningautoml
    在 GitHub 上查看↗9,997
  • livekit/agentslivekit 的头像

    livekit/agents

    9,379在 GitHub 上查看↗

    This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations. The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself throu

    Provides filters for cleaning formatting and accessing timestamps in final agent transcription outputs.

    Pythonagentsaiopenai
    在 GitHub 上查看↗9,379
  • seatgeek/fuzzywuzzyseatgeek 的头像

    seatgeek/fuzzywuzzy

    9,258在 GitHub 上查看↗

    Fuzzywuzzy is a Python library and text processing utility designed to calculate similarity scores between strings. It functions as a text similarity scoring engine and an approximate string matching tool used to identify the closest textual matches within a list of candidate strings. The library provides a suite of tools for measuring the degree of similarity between pieces of text, accounting for typos and formatting differences. These capabilities include extracting the best match from a candidate list and performing fuzzy string matching through various scoring methods. The toolset cover

    Normalizes strings by removing special characters and forcing ASCII encoding to optimize fuzzy comparisons.

    Python
    在 GitHub 上查看↗9,258
  • haifengl/smilehaifengl 的头像

    haifengl/smile

    6,387在 GitHub 上查看↗

    Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of algorithms for classification, regression, and clustering, implemented natively for Java, Scala, and Kotlin. The project also functions as a deep learning framework, a natural language processing library, and an inference engine for large language models. The library distinguishes itself through GPU acceleration via LibTorch bindings and support for the ONNX model interchange format. It includes specialized capabilities for large language model inference, featuring Byte-Pair Encodin

    Extracts meaning from text through sentence splitting, tokenization, stemming, and tagging.

    Java
    在 GitHub 上查看↗6,387
  • mrdbourke/zero-to-mastery-mlmrdbourke 的头像

    mrdbourke/zero-to-mastery-ml

    5,839在 GitHub 上查看↗

    本项目是一个机器学习教育课程和学习平台,通过交互式 Jupyter Notebooks 提供。它作为掌握 Python 数据科学工具包的综合指南,为数值计算、表格数据操作和统计可视化提供结构化教程。 该课程包括 Scikit-Learn 的具体实现指南,以及关于构建、训练和部署神经网络及计算机视觉模型的 TensorFlow 实践课程。它涵盖了构建预测模型的端到端过程,从初始问题定义和任务分类,到通过交互式 Web 界面部署模型。 该项目涵盖了广泛的功能领域,包括多维数组的数值计算、探索性数据分析和数据预处理例程。它为监督和无监督学习、自动化机器学习流水线、超参数优化以及使用分类指标和交叉验证的模型评估提供了详细的工作流。 教育内容组织为一系列 Notebook,将 Python 代码与叙述性解释交织在一起,以记录数据科学工作流。

    Applies string transformations to standardize text formatting across data columns for preprocessing.

    Jupyter Notebookdata-sciencedeep-learningmachine-learning
    在 GitHub 上查看↗5,839
  • lucaong/minisearchlucaong 的头像

    lucaong/minisearch

    5,804在 GitHub 上查看↗

    Accepts user-provided functions for stemming, stop-word removal, or other text preprocessing instead of imposing a built-in locale.

    TypeScriptautocompleteautosuggestionedge-computing
    在 GitHub 上查看↗5,804
  • goto456/stopwordsgoto456 的头像

    goto456/stopwords

    5,539在 GitHub 上查看↗

    该项目提供了一个从学术和行业标准中收集的高频、非信息性中文词汇集合。它作为一个参考数据集和停用词集合,旨在用于自然语言处理任务。 该仓库专注于中文文本预处理,以减少噪声并提高机器学习模型的准确性。它提供了专门用于中文信息检索、情感分析准备和一般数据清洗的过滤数据集。 该项目利用预编译的词典和平面文件存储,以实现高效的中文语料库停用词过滤和词汇聚合。

    Provides a collection of words used to filter noise and improve the accuracy of text analysis and machine learning models.

    在 GitHub 上查看↗5,539
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  2. Data & Databases
  3. Data Processing Pipelines
  4. Data Transformation
  5. Text and NLP Preprocessing

探索子标签

  • NLP Batch PreparationHandles the batching of tokenized text, including sequence length management and validation splitting. **Distinct from Text and NLP Preprocessing:** Distinct from Text and NLP Preprocessing: focuses specifically on the batching and splitting logic for model input.
  • Text Formatting Filters2 个子标签Filters that modify string formatting to ensure consistent text presentation within applications.
  • Text Preprocessing2 个子标签Libraries for parsing, formatting, and manipulating text-based data structures.
  • Vision and NLP PipelinesReady-to-use data loading and preprocessing pipelines for both image classification and text processing tasks. **Distinct from Text and NLP Preprocessing:** Distinct from Text and NLP Preprocessing: covers both vision and NLP pipelines, not just text preprocessing.