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# Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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from typing import List
import numpy as np
from ..base import BaseTextEncoder
from ...helper import batching, as_numpy_array
[docs]class CharEmbeddingEncoder(BaseTextEncoder):
"""A random character embedding model. Only useful for testing"""
is_trained = True
def __init__(self, dim: int = 128, *args, **kwargs):
super().__init__(*args, **kwargs)
self.dim = dim
self.offset = 32
self.unknown_idx = 96
# in total 96 printable chars and 2 special chars = 98
self._char_embedding = np.random.random([97, dim])
[docs] @batching
@as_numpy_array
def encode(self, text: List[str], *args, **kwargs) -> List[np.ndarray]:
# tokenize text
sent_embed = []
for sent in text:
ids = [ord(c) - 32 if 32 <= ord(c) <= 127 else self.unknown_idx for c in sent]
sent_embed.append(np.mean(self._char_embedding[ids], axis=0))
return sent_embed