v0.5.5-alpha

• 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.
• Separate the graph topology and graph embedding process.
• Renew all the graph construction functions.
• Module graph_embedding is divided into graph_embedding_initialization and graph_embedding_learning.
• Unify the parameters in Dataset. We remove the ambiguous parameter graph_type and introduce graph_name to indicate the graph construction method and static_or_dynamic to indicate the static or dynamic graph construction type.
• New: The dataset now can automatically choose the default methods (e.g., topology_builder) by only one parameter graph_name.

v0.5.1-alpha

• Lint the codes
• Support testing with users' own data
• Fix the bug: The word embedding size was hard-coded in the 0.4.1 version. Now it is equal to "word_emb_size" parameter.
• Fix the bug: The build_vocab() is called twice in the 0.4.1 version.
• Fix the bug: The two main files of knowledge graph completion example missed the optional parameter "kg_graph" in ranking_and_hits() when resuming training the model.
• Fix the bug: We have fixed the preprocessing path error in KGC readme.
• Fix the bug: We have fixed embedding construction bug when setting emb_strategy to 'w2v'.

v0.4.1-alpha

This is the beta-version of our graph4nlp library, which is the first library for the easy use of GNNs for NLP.