As with most ML tasks, we need to find a set of relevant features in order to predict type annotations. Here, we consider features as type hints. Specifically, we extract three kinds of type hints, namely, identifiers, code context, and visible type hints (VTHs).
To learn from the extracted sequences, first, we apply common NL pre-processing techniques tokenization (by snake case ), stop word removal and lemmatization. Then, we employ the famous Word2Vec model to generate word embeddings of the extracted sequences in order to train the Type4Py model, which is described next.
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