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Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP). It provides both full implementations of state-of-the-art models for data scientists and also flexible interfaces to build customized models for researchers and developers with whole-pipeline support. Built upon highly-optimized runtime libraries including DGL , Graph4NLP has both high running efficiency and great extensibility. The architecture of Graph4NLP is shown in the following figure, where boxes with dashed lines represent the features under development. 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).
Features
- Support model.predict API by introducing wrapper functions
- Introduce Three new inference_wrapper functions: classifier_inference_wrapper, generator_inference_wrapper, generator_inference_wrapper_for_tree
- Add the inference and inference_advance examples in each application
- Renew all the graph construction functions
- Separate the graph topology and graph embedding process
- Support testing with users' own data
Project Samples
