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import numpy as np
from typing import List
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)
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