# Tencent is pleased to support the open source community by making GNES available.
#
# 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List
import numpy as np
from ..base import BaseTextEncoder
from ...helper import batching, pooling_np
[docs]class ElmoEncoder(BaseTextEncoder):
is_trained = True
batch_size = 64
def __init__(self, model_dir: str, pooling_layer: int = -1,
pooling_strategy: str = 'REDUCE_MEAN', *args, **kwargs):
super().__init__(*args, **kwargs)
self.model_dir = model_dir
if pooling_layer > 2:
raise ValueError('pooling_layer = %d is not supported now!' %
pooling_layer)
self.pooling_layer = pooling_layer
self.pooling_strategy = pooling_strategy
[docs] def post_init(self):
from elmoformanylangs import Embedder
from ...helper import Tokenizer
self._elmo = Embedder(model_dir=self.model_dir, batch_size=self.batch_size)
self.cn_tokenizer = Tokenizer()
[docs] @batching
def encode(self, text: List[str], *args, **kwargs) -> np.ndarray:
# tokenize text
batch_tokens = [self.cn_tokenizer.tokenize(sent) for sent in text]
elmo_encodes = self._elmo.sents2elmo(batch_tokens, output_layer=-2)
pooled_data = []
for token_encodes in elmo_encodes:
if self.pooling_layer == -1:
_layer_data = np.average(token_encodes, axis=0)
elif self.pooling_layer >= 0:
_layer_data = token_encodes[self.pooling_layer]
else:
raise ValueError('pooling_layer = %d is not supported now!' %
self.pooling_layer)
_pooled = pooling_np(_layer_data, self.pooling_strategy)
pooled_data.append(_pooled)
return np.array(pooled_data, dtype=np.float32)