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