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2 repositorios

Awesome GitHub RepositoriesSequence Representation Builders

Utilities for preparing text sequences with special tokens and segment identifiers for encoder processing.

Distinct from Text Sequence Processing: Distinct from general text sequence processing: focuses on the specific preparation of inputs for bidirectional encoders.

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

Awesome Sequence Representation Builders GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • d2l-ai/d2l-enAvatar de d2l-ai

    d2l-ai/d2l-en

    29,001Ver en GitHub↗

    This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

    Provides utilities for preparing text sequences with special tokens and segment identifiers for bidirectional encoder processing.

    Pythonbookcomputer-visiondata-science
    Ver en GitHub↗29,001
  • graykode/nlp-tutorialAvatar de graykode

    graykode/nlp-tutorial

    14,855Ver en GitHub↗

    This repository serves as an educational resource for learning the foundational architectures of natural language processing through concise code implementations. It provides a structured collection of deep learning models designed to process and understand human language, focusing on the core mechanics of neural network sequence modeling and text analysis. The project distinguishes itself by offering direct, hands-on implementations of complex architectures, including Transformers, attention mechanisms, and word embedding generation. By utilizing tensor-based computational graphs and gradien

    Computes weighted sequence representations to focus on relevant data during processing.

    Jupyter Notebookattentionbertnatural-language-processing
    Ver en GitHub↗14,855
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