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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("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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def embed(self, |
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documents: List[str], |
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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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embeddings = [] |
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for doc in tqdm(documents, disable=not verbose, position=0, leave=True): |
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embedding = [self.embedding_model.get_vector(word) for word in doc.split() |
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if word in self.embedding_model.key_to_index] |
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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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