wefe
.get_embeddings_from_set¶
- 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).
- Parameters
- modelWordEmbeddingModel
A word embeddding model
- word_setSequence[str]
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, thepreprocessor
{'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
- Returns
- 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.