戒酒的李白
63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
import numpy as np
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from typing import List
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class BaseEmbedder:
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"""The Base Embedder used for creating embedding models.
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Arguments:
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embedding_model: The main embedding model to be used for extracting
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document and word embedding
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word_embedding_model: The embedding model used for extracting word
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embeddings only. If this model is selected,
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then the `embedding_model` is purely used for
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creating document embeddings.
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"""
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def __init__(self, embedding_model=None, word_embedding_model=None):
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self.embedding_model = embedding_model
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self.word_embedding_model = word_embedding_model
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def embed(self, documents: List[str], verbose: bool = False) -> np.ndarray:
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"""Embed a list of n documents/words into an n-dimensional
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matrix of embeddings.
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Arguments:
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documents: A list of documents or words to be embedded
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verbose: Controls the verbosity of the process
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Returns:
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Document/words embeddings with shape (n, m) with `n` documents/words
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that each have an embeddings size of `m`
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"""
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pass
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def embed_words(self, words: List[str], verbose: bool = False) -> np.ndarray:
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"""Embed a list of n words into an n-dimensional
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matrix of embeddings.
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Arguments:
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words: A list of words to be embedded
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verbose: Controls the verbosity of the process
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Returns:
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Word embeddings with shape (n, m) with `n` words
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that each have an embeddings size of `m`
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"""
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return self.embed(words, verbose)
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def embed_documents(self, document: List[str], verbose: bool = False) -> np.ndarray:
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"""Embed a list of n words into an n-dimensional
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matrix of embeddings.
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Arguments:
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document: A list of documents to be embedded
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verbose: Controls the verbosity of the process
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Returns:
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Document embeddings with shape (n, m) with `n` documents
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that each have an embeddings size of `m`
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"""
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return self.embed(document, verbose)
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