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import numpy as np
from typing import List
from bertopic.backend._base import BaseEmbedder
from bertopic.backend._utils import select_backend
class WordDocEmbedder(BaseEmbedder):
""" Combine a document- and word-level embedder
"""
def __init__(self,
embedding_model,
word_embedding_model):
super().__init__()
self.embedding_model = select_backend(embedding_model)
self.word_embedding_model = select_backend(word_embedding_model)
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.word_embedding_model.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.embedding_model.embed(document, verbose)
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