戒酒的李白
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
import numpy as np
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from tqdm import tqdm
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from typing import List
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from bertopic.backend import BaseEmbedder
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from gensim.models.keyedvectors import Word2VecKeyedVectors
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class GensimBackend(BaseEmbedder):
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"""Gensim Embedding Model.
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The Gensim embedding model is typically used for word embeddings with
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GloVe, Word2Vec or FastText.
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Arguments:
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embedding_model: A Gensim embedding model
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Examples:
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```python
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from bertopic.backend import GensimBackend
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import gensim.downloader as api
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ft = api.load('fasttext-wiki-news-subwords-300')
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ft_embedder = GensimBackend(ft)
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```
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"""
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def __init__(self, embedding_model: Word2VecKeyedVectors):
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super().__init__()
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if isinstance(embedding_model, Word2VecKeyedVectors):
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self.embedding_model = embedding_model
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else:
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raise ValueError(
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"Please select a correct Gensim model: \n"
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"`import gensim.downloader as api` \n"
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"`ft = api.load('fasttext-wiki-news-subwords-300')`"
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)
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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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vector_shape = self.embedding_model.get_vector(list(self.embedding_model.index_to_key)[0]).shape[0]
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empty_vector = np.zeros(vector_shape)
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# Extract word embeddings and pool to document-level
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embeddings = []
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for doc in tqdm(documents, disable=not verbose, position=0, leave=True):
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embedding = [
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self.embedding_model.get_vector(word)
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for word in doc.split()
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if word in self.embedding_model.key_to_index
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]
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if len(embedding) > 0:
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embeddings.append(np.mean(embedding, axis=0))
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else:
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embeddings.append(empty_vector)
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embeddings = np.array(embeddings)
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return embeddings
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