戒酒的李白
56 lines
1.7 KiB
Python
56 lines
1.7 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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class USEBackend(BaseEmbedder):
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"""Universal Sentence Encoder.
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USE encodes text into high-dimensional vectors that
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are used for semantic similarity in BERTopic.
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Arguments:
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embedding_model: An USE embedding model
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Examples:
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```python
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import tensorflow_hub
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from bertopic.backend import USEBackend
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embedding_model = tensorflow_hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
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use_embedder = USEBackend(embedding_model)
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```
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"""
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def __init__(self, embedding_model):
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super().__init__()
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try:
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embedding_model(["test sentence"])
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self.embedding_model = embedding_model
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except TypeError:
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raise ValueError(
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"Please select a correct USE model: \n"
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"`import tensorflow_hub` \n"
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"`embedding_model = tensorflow_hub.load(path_to_model)`"
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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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embeddings = np.array(
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[self.embedding_model([doc]).cpu().numpy()[0] for doc in tqdm(documents, disable=not verbose)]
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)
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return embeddings
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