wefe.get_embeddings_from_set(model: wefe.word_embedding_model.WordEmbeddingModel, word_set: Sequence[str], preprocessors: List[Dict[str, Union[str, bool, Callable]]] = [{}], strategy: str = 'first', normalize: bool = False, verbose: bool = False) Tuple[List[str], Dict[str, numpy.ndarray]][source]

Transform a sequence of words into dictionary that maps word - word embedding.

The method discard out words that are not in the model’s vocabulary (according to the rules specified in the preprocessors).


A word embeddding model


A sequence with the words that this function will convert to embeddings.

preprocessorsList[Dict[str, Union[str, bool, Callable]]]

A list with preprocessor options.

A preprocessor is a dictionary that specifies what processing(s) are performed on each word before it is looked up in the model vocabulary. For example, the preprocessor {'lowecase': True, 'strip_accents': True} allows you to lowercase and remove the accent from each word before searching for them in the model vocabulary. Note that an empty dictionary {} indicates that no preprocessing is done.

The possible options for a preprocessor are:

  • lowercase: bool. Indicates that the words are transformed to lowercase.

  • uppercase: bool. Indicates that the words are transformed to uppercase.

  • titlecase: bool. Indicates that the words are transformed to titlecase.

  • strip_accents: bool, {'ascii', 'unicode'}: Specifies that the accents of the words are eliminated. The stripping type can be specified. True uses ‘unicode’ by default.

  • preprocessor: Callable. It receives a function that operates on each word. In the case of specifying a function, it overrides the default preprocessor (i.e., the previous options stop working).

A list of preprocessor options allows you to search for several variants of the words into the model. For example, the preprocessors [{}, {"lowercase": True, "strip_accents": True}] {} allows first to search for the original words in the vocabulary of the model. In case some of them are not found, {"lowercase": True, "strip_accents": True} is executed on these words and then they are searched in the model vocabulary. by default [{}]

strategystr, optional

The strategy indicates how it will use the preprocessed words: ‘first’ will include only the first transformed word found. all’ will include all transformed words found, by default “first”.

normalizebool, optional

True indicates that embeddings will be normalized, by default False

verbosebool, optional

Indicates whether the execution status of this function is printed, by default False

Tuple[List[str], Dict[str, np.ndarray]]

A tuple containing the words that could not be found and a dictionary with the found words and their corresponding embeddings.