BaseEmbedder

The Base Embedder used for creating embedding models

Parameters:

Name	Type	Description	Default
embedding_model		The main embedding model to be used for extracting document and word embedding	None
word_embedding_model		The embedding model used for extracting word embeddings only. If this model is selected, then the embedding_model is purely used for creating document embeddings.	None

Source code in bertopic\backend\_base.py

class BaseEmbedder:
    """ The Base Embedder used for creating embedding models

    Arguments:
        embedding_model: The main embedding model to be used for extracting
                         document and word embedding
        word_embedding_model: The embedding model used for extracting word
                              embeddings only. If this model is selected,
                              then the `embedding_model` is purely used for
                              creating document embeddings.
    """
    def __init__(self,
                 embedding_model=None,
                 word_embedding_model=None):
        self.embedding_model = embedding_model
        self.word_embedding_model = word_embedding_model

    def embed(self,
              documents: List[str],
              verbose: bool = False) -> np.ndarray:
        """ Embed a list of n documents/words into an n-dimensional
        matrix of embeddings

        Arguments:
            documents: A list of documents or words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document/words embeddings with shape (n, m) with `n` documents/words
            that each have an embeddings size of `m`
        """
        pass

    def embed_words(self,
                    words: List[str],
                    verbose: bool = False) -> np.ndarray:
        """ Embed a list of n words into an n-dimensional
        matrix of embeddings

        Arguments:
            words: A list of words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Word embeddings with shape (n, m) with `n` words
            that each have an embeddings size of `m`

        """
        return self.embed(words, verbose)

    def embed_documents(self,
                        document: List[str],
                        verbose: bool = False) -> np.ndarray:
        """ Embed a list of n words into an n-dimensional
        matrix of embeddings

        Arguments:
            document: A list of documents to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document embeddings with shape (n, m) with `n` documents
            that each have an embeddings size of `m`
        """
        return self.embed(document, verbose)

embed(self, documents, verbose=False)

Embed a list of n documents/words into an n-dimensional matrix of embeddings

Parameters:

Name	Type	Description	Default
documents	List[str]	A list of documents or words to be embedded	required
verbose	bool	Controls the verbosity of the process	False

Returns:

Type	Description
ndarray	Document/words embeddings with shape (n, m) with n documents/words that each have an embeddings size of m

Source code in bertopic\backend\_base.py

def embed(self,
          documents: List[str],
          verbose: bool = False) -> np.ndarray:
    """ Embed a list of n documents/words into an n-dimensional
    matrix of embeddings

    Arguments:
        documents: A list of documents or words to be embedded
        verbose: Controls the verbosity of the process

    Returns:
        Document/words embeddings with shape (n, m) with `n` documents/words
        that each have an embeddings size of `m`
    """
    pass

embed_documents(self, document, verbose=False)

Embed a list of n words into an n-dimensional matrix of embeddings

Parameters:

Name	Type	Description	Default
document	List[str]	A list of documents to be embedded	required
verbose	bool	Controls the verbosity of the process	False

Returns:

Type	Description
ndarray	Document embeddings with shape (n, m) with n documents that each have an embeddings size of m

Source code in bertopic\backend\_base.py

def embed_documents(self,
                    document: List[str],
                    verbose: bool = False) -> np.ndarray:
    """ Embed a list of n words into an n-dimensional
    matrix of embeddings

    Arguments:
        document: A list of documents to be embedded
        verbose: Controls the verbosity of the process

    Returns:
        Document embeddings with shape (n, m) with `n` documents
        that each have an embeddings size of `m`
    """
    return self.embed(document, verbose)

embed_words(self, words, verbose=False)

Embed a list of n words into an n-dimensional matrix of embeddings

Parameters:

Name	Type	Description	Default
words	List[str]	A list of words to be embedded	required
verbose	bool	Controls the verbosity of the process	False

Returns:

Type	Description
ndarray	Word embeddings with shape (n, m) with n words that each have an embeddings size of m

Source code in bertopic\backend\_base.py

def embed_words(self,
                words: List[str],
                verbose: bool = False) -> np.ndarray:
    """ Embed a list of n words into an n-dimensional
    matrix of embeddings

    Arguments:
        words: A list of words to be embedded
        verbose: Controls the verbosity of the process

    Returns:
        Word embeddings with shape (n, m) with `n` words
        that each have an embeddings size of `m`

    """
    return self.embed(words, verbose)