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2 repositorios

Awesome GitHub RepositoriesText Normalization

Processes raw text into a standardized format using tokenization and stemming to simplify linguistic analysis.

Distinct from Text Tokenization: Broadens the scope from simple segmentation (tokenization) to include root-form reduction (stemming) and normalization.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Text Normalization. Refine with filters or upvote what's useful.

Awesome Text Normalization GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • axa-group/nlp.jsAvatar de axa-group

    axa-group/nlp.js

    6,574Ver en GitHub↗

    nlp.js is a JavaScript natural language processing library and development framework used to build natural language understanding engines. It provides a toolkit for creating local machine learning models for intent classification and acts as a multilingual text processor that detects languages and normalizes text across various dialects. The framework distinguishes itself by supporting local execution on both servers and mobile devices, enabling chatbot functionality without an internet connection. It features a specialized system for conversational slot filling to collect mandatory informati

    Provides comprehensive text normalization utilities including tokenizers and stemmers to prepare text for natural language understanding.

    JavaScriptbotbotschatbot
    Ver en GitHub↗6,574
  • brightmart/albert_zhAvatar de brightmart

    brightmart/albert_zh

    3,982Ver en GitHub↗

    This project is an implementation of the ALBERT language model architecture, providing a framework for training and evaluating transformer-based text classifiers and similarity models. It specifically includes pre-trained assets and tools optimized for generating semantic embeddings and representations of Chinese text. The framework distinguishes itself through tools for converting heavy language model checkpoints into lightweight formats to enable low-latency inference on mobile devices. It utilizes specific weight reduction techniques, including cross-parameter sharing and factorized embedd

    Provides text normalization to clean raw input data via whitespace removal and Unicode standardization.

    Pythonalbertbertchinese-corpus
    Ver en GitHub↗3,982
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