Auto-generated documentation for fse.models.sentencevectors module.
class SentenceVectors(utils.SaveLoad):
def __init__(vector_size: int, mapfile_path: str = None):
def __getitem__(entities: int) -> ndarray:
Get vector representation of entities.
entities : {int, list of int} Index or sequence of entities.
numpy.ndarray
Vector representation for entities (1D if entities is int, otherwise - 2D).
def distance(d1: int, d2: int) -> float:
Compute cosine similarity between two sentences from the training set.
d1 : int index of sentence d2 : int index of sentence
float The cosine distance between the vectors of the two sentences.
def get_vector(index: int, use_norm: bool = False) -> ndarray:
Get sentence representations in vector space, as a 1D numpy array.
index : int Input index norm : bool, optional If True - resulting vector will be L2-normalized (unit euclidean length).
numpy.ndarray Vector representation of index.
KeyError If index out of bounds.
def init_sims(replace: bool = False):
Precompute L2-normalized vectors.
replace : bool, optional If True - forget the original vectors and only keep the normalized ones = saves lots of memory!
@classmethod
def load(fname_or_handle, **kwargs):
def most_similar(
positive: Union[int, ndarray] = None,
negative: Union[int, ndarray] = None,
indexable: Union[IndexedList, IndexedLineDocument] = None,
topn: int = 10,
restrict_size: Union[int, Tuple[int, int]] = None,
) -> List[Tuple[int, float]]:
Find the top-N most similar sentences. Positive sentences contribute positively towards the similarity, negative sentences negatively.
This method computes cosine similarity between a simple mean of the projection weight vectors of the given sentences and the vectors for each sentence in the model.
positive : list of int, optional
List of indices that contribute positively.
negative : list of int, optional
List of indices that contribute negatively.
indexable: list, IndexedList, IndexedLineDocument
Provides an indexable object from where the most similar sentences are read
topn : int or None, optional
Number of top-N similar sentences to return, when topn is int. When topn is None,
then similarities for all sentences are returned.
restrict_size : int or Tuple(int,int), optional
Optional integer which limits the range of vectors which
are searched for most-similar values. For example, restrict_vocab=10000 would
only check the first 10000 sentence vectors.
restrict_vocab=(500, 1000) would search the sentence vectors with indices between
500 and 1000.
list of (int, float) or list of (str, int, float)
A sequence of (index, similarity) is returned.
When an indexable is provided, returns (str, index, similarity)
When topn is None, then similarities for all words are returned as a
one-dimensional numpy array with the size of the vocabulary.
def save(*args, **kwargs):
Save object.
fname : str Path to the output file.
:meth:~gensim.models.keyedvectors.Doc2VecKeyedVectors.load
Load object.
def similar_by_sentence(
sentence: List[str],
model,
indexable: Union[IndexedList, IndexedLineDocument] = None,
topn: int = 10,
restrict_size: Union[int, Tuple[int, int]] = None,
) -> List[Tuple[int, float]]:
Find the top-N most similar sentences to a given sentence.
sentence : list of str
Sentence as list of strings
model : class fse.models.base_s2v.BaseSentence2VecModel
This object essentially provides the infer method used to transform .
indexable: list, IndexedList, IndexedLineDocument
Provides an indexable object from where the most similar sentences are read
topn : int or None, optional
Number of top-N similar sentences to return, when topn is int. When topn is None,
then similarities for all sentences are returned.
restrict_size : int or Tuple(int,int), optional
Optional integer which limits the range of vectors which
are searched for most-similar values. For example, restrict_vocab=10000 would
only check the first 10000 sentence vectors.
restrict_vocab=(500, 1000) would search the sentence vectors with indices between
500 and 1000.
list of (int, float) or list of (str, int, float)
A sequence of (index, similarity) is returned.
When an indexable is provided, returns (str, index, similarity)
When topn is None, then similarities for all words are returned as a
one-dimensional numpy array with the size of the vocabulary.
def similar_by_vector(
vector: ndarray,
indexable: Union[IndexedList, IndexedLineDocument] = None,
topn: int = 10,
restrict_size: Union[int, Tuple[int, int]] = None,
) -> List[Tuple[int, float]]:
Find the top-N most similar sentences to a given vector.
vector : ndarray
Vectors
indexable: list, IndexedList, IndexedLineDocument
Provides an indexable object from where the most similar sentences are read
topn : int or None, optional
Number of top-N similar sentences to return, when topn is int. When topn is None,
then similarities for all sentences are returned.
restrict_size : int or Tuple(int,int), optional
Optional integer which limits the range of vectors which
are searched for most-similar values. For example, restrict_vocab=10000 would
only check the first 10000 sentence vectors.
restrict_vocab=(500, 1000) would search the sentence vectors with indices between
500 and 1000.
list of (int, float) or list of (str, int, float)
A sequence of (index, similarity) is returned.
When an indexable is provided, returns (str, index, similarity)
When topn is None, then similarities for all words are returned as a
one-dimensional numpy array with the size of the vocabulary.
def similar_by_word(
word: str,
wv: KeyedVectors,
indexable: Union[IndexedList, IndexedLineDocument] = None,
topn: int = 10,
restrict_size: Union[int, Tuple[int, int]] = None,
) -> List[Tuple[int, float]]:
Find the top-N most similar sentences to a given word.
word : str
Word
wv : class gensim.models.keyedvectors.KeyedVectors
This object essentially contains the mapping between words and embeddings.
indexable: list, IndexedList, IndexedLineDocument
Provides an indexable object from where the most similar sentences are read
topn : int or None, optional
Number of top-N similar sentences to return, when topn is int. When topn is None,
then similarities for all sentences are returned.
restrict_size : int or Tuple(int,int), optional
Optional integer which limits the range of vectors which
are searched for most-similar values. For example, restrict_vocab=10000 would
only check the first 10000 sentence vectors.
restrict_vocab=(500, 1000) would search the sentence vectors with indices between
500 and 1000.
list of (int, float) or list of (str, int, float)
A sequence of (index, similarity) is returned.
When an indexable is provided, returns (str, index, similarity)
When topn is None, then similarities for all words are returned as a
one-dimensional numpy array with the size of the vocabulary.
def similarity(d1: int, d2: int) -> float:
Compute cosine similarity between two sentences from the training set.
d1 : int index of sentence d2 : int index of sentence
float The cosine similarity between the vectors of the two sentences.