Machine learning models for MLonCode trained using the source{d} stack
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README.md

source{d} MLonCode models

bow

Weighted bag-of-words, that is, every bag is a feature extracted from source code and associated with a weight obtained by applying TFIDF.

Example:

from sourced.ml.models import BOW
bow = BOW().load(bow)
print("Number of documents:", len(bow))
print("Number of tokens:", len(bow.tokens))

4 models:

docfreq

Document frequencies of features extracted from source code, that is, how many documents (repositories, files or functions) contain each tokenized feature.

Example:

from sourced.ml.models import DocumentFrequencies
df = DocumentFrequencies().load(docfreq)
print("Number of tokens:", len(df))

2 models:

id2vec

Source code identifier embeddings, that is, every identifier is represented by a dense vector.

Example:

from sourced.ml.models import Id2Vec
id2vec = Id2Vec().load(id2vec)
print("Number of tokens:", len(id2vec))

2 models:

topics

Topic modeling of Git repositories. All tokens are identifiers extracted from repositories and seen as indicators for topics. They are used to infer the topic(s) of repositories.

Example:

from sourced.ml.models import Topics
topics = Topics().load(topics)
print("Number of topics:", len(topics))
print("Number of tokens:", len(topics.tokens))

1 model: