ktext/preprocess.py
def transform(self, data: List[str]) -> List[List[int]]:
        """
        Transform List of documents into List[List[int]]
        If transforming a large number of documents consider using the method
        `transform_parallel` instead.

        ex:
        >> pp = processor()
        >> pp.fit(docs)
        >> new_docs = [["The quick brown fox"], ["jumps over the lazy dog"]]
        >> pp.transform(new_docs)
        [[1, 2, 3, 4], [5, 6, 1, 7, 8]]
        """
        tokenized_data = self.process_text(data)
        indexed_data = self.indexer.tokenized_texts_to_sequences(tokenized_data)
        return self.pad(indexed_data)
def transform(self, data: List[str]) -> List[List[int]]:
        """
        Transform List of documents into List[List[int]]
        If transforming a large number of documents consider using the method
        `transform_parallel` instead.

        ex:
        >> pp = processor()
        >> pp.fit(docs)
        >> new_docs = [["The quick brown fox"], ["jumps over the lazy dog"]]
        >> pp.transform(new_docs)
        [[1, 2, 3, 4], [5, 6, 1, 7, 8]]
        """
        tokenized_data = self.process_text(data)
        indexed_data = self.indexer.tokenized_texts_to_sequences(tokenized_data)
        return self.pad(indexed_data)
def transform ( self , data : List [ str ] ) -> List [ List [ int ] ] : tokenized_data = self . process_text ( data ) indexed_data = self . indexer . tokenized_texts_to_sequences ( tokenized_data ) return self . pad ( indexed_data )
Transform List of documents into List[List[int]]
        If transforming a large number of documents consider using the method
        `transform_parallel` instead.

        ex:
        >> pp = processor()
        >> pp.fit(docs)
        >> new_docs = [["The quick brown fox"], ["jumps over the lazy dog"]]
        >> pp.transform(new_docs)
        [[1, 2, 3, 4], [5, 6, 1, 7, 8]]
Transform List of documents into List [ List [ int ]] If transforming a large number of documents consider using the method transform_parallel instead .