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:
- 1e0deee4-7dc1-400f-acb6-74c0f4aec471
- 1e3da42a-28b6-4b33-94a2-a5671f4102f4
- 694c20a0-9b96-4444-80ae-f2fa5bd1395b
- da8c5dee-b285-4d55-8913-a5209f716564
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:
id_splitter_bilstm
Model that contains source code identifier splitter BiLSTM weights.
Example:
from sourced.ml.models.id_splitter import IdentifierSplitterBiLSTM
id_splitter = IdentifierSplitterBiLSTM().load(id_splitter_bilstm)
id_splitter.split(identifiers)1 model:
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:
typos_correction
Model that suggests fixes to correct typos.
Example:
from lookout.style.typos.corrector import TyposCorrector
corrector = TyposCorrector().load(typos_correction)
print("Corrector configuration:\n", corrector.dump())2 models: