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Graph4Nlp

Graph4NLP is an open-source Python library for research and development at the intersection of graph deep learning and natural-language processing. It provides implemented models and flexible interfaces for building customized pipelines, including graph-to-sequence and graph-to-tree model APIs.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Graph4Nlp — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use Graph4NLP when an NLP workflow calls for graph-based representations or graph neural network models and you want a Python toolkit with model implementations and customization interfaces.

Who it is for

  • NLP developers integrating graph neural networks into applications or model pipelines.
  • Researchers prototyping graph-based NLP methods.
  • Data scientists exploring implemented graph NLP models.

Documented uses

  • Build NLP applications that use graph neural networks.
  • Prototype graph-based NLP research models.
  • Explore graph-to-sequence approaches for tasks such as machine translation, question answering, or semantic parsing.
  • Customize a graph-based NLP pipeline using the library’s model interfaces.
Before you choose

Requirements & limits

The repository is public and lists an Apache-2.0 license. Installation instructions describe pip and source-code methods.

  • PyTorch 1.6.0 or later is required by the installation guide, and torchtext is needed for embeddings.
  • GPU users need to match the Graph4NLP installation option to their PyTorch/CUDA setup.
  • The README lists Linux, macOS with CPU only, and Windows 10; it recommends source installation for Windows due to compatibility considerations.
  • The project provides documentation, tutorials, and a survey for learning and implementation context.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Graph4NLP from pip on a supported platform

Install Graph4NLP using a pip wheel and a matching PyTorch/CUDA setup.

Before you start

  • Python environment with PyTorch 1.6.0 or later installed.
  • torchtext 0.7.0 or later, installed with attention to the PyTorch version requirements.
  • A supported platform: Linux, macOS (CPU only), or Windows 10 (PyTorch 1.8 or later).

Steps

  1. Check the installed PyTorch version with the command below.

    Documented command
    python -c "import torch; print(torch.__version__)"

  2. For GPU use, check the CUDA version associated with PyTorch using the command below.

    Documented command
    python -c "import torch; print(torch.version.cuda)"

  3. Install torchtext with the command below, observing its PyTorch version requirements.

    Documented command
    pip install torchtext

  4. Install the matching Graph4NLP wheel: use the command below for CPU, or the command below, the command below, the command below, or the command below for the listed CUDA versions.

    Documented command
    pip install graph4nlp
    Documented command
    pip install graph4nlp-cu92
    Documented command
    pip install graph4nlp-cu101
    Documented command
    pip install graph4nlp-cu102
    Documented command
    pip install graph4nlp-cu110

Expected outcome

Graph4NLP is installed from a pip wheel selected for the CPU or listed CUDA configuration.

Guide limitations

  • CUDA 11.1 users are directed to source installation instead.
  • Windows users are strongly advised to use source installation because of compatibility concerns.
  • macOS support is CPU-only.
  • The README specifies PyTorch 1.6.0 or later and cautions that torchtext must match PyTorch requirements.
Documentation 1 ↗
Source-based practical guide

Install Graph4NLP from source

Build and install Graph4NLP from its GitHub source after configuring the CUDA choice.

Before you start

  • PyTorch 1.6.0 or later installed.
  • torchtext installed with attention to PyTorch version requirements.
  • Git available to retrieve the repository.
  • For GPU setups, know the CUDA version associated with PyTorch; the configuration also accepts cpu when no GPU is used.

Steps

  1. Check the installed PyTorch version and, for GPU use, its CUDA version with the Python commands shown in the README.

  2. Install torchtext with the command below, observing its PyTorch version requirements.

    Documented command
    pip install torchtext

  3. Clone the repository and enter its directory with the command below and cd graph4nlp.

    Documented command
    git clone https://github.com/graph4ai/graph4nlp.git

  4. Run ./configure (or ./configure.bat on Windows 10) and specify the CUDA version when prompted, or type cpu if there is no GPU.

  5. Install the package with the command below.

    Documented command
    python setup.py install

Expected outcome

The source package is installed after the configuration script has collected the CUDA or CPU selection.

Guide limitations

  • The README does not provide further build troubleshooting or a post-installation validation procedure.
  • CUDA version configuration is required by the documented source-install path.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
graph4ai/graph4nlp · Official homepage title · retained in its source language
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