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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.
# 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
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from typing import List, Tuple
import numpy as np
from ..base import BaseTextEncoder
from ...helper import batching, as_numpy_array
[docs]class FlairEncoder(BaseTextEncoder):
is_trained = True
def __init__(self,
word_embedding: str = 'glove',
flair_embeddings: Tuple[str] = ('news-forward', 'news-backward'),
pooling_strategy: str = 'mean', *args, **kwargs):
super().__init__(*args, **kwargs)
self.word_embedding = word_embedding
self.flair_embeddings = flair_embeddings
self.pooling_strategy = pooling_strategy
[docs] def post_init(self):
from flair.embeddings import DocumentPoolEmbeddings, WordEmbeddings, FlairEmbeddings
self._flair = DocumentPoolEmbeddings(
[WordEmbeddings(self.word_embedding),
FlairEmbeddings(self.flair_embeddings[0]),
FlairEmbeddings(self.flair_embeddings[1])],
pooling=self.pooling_strategy)
[docs] @batching
@as_numpy_array
def encode(self, text: List[str], *args, **kwargs) -> np.ndarray:
from flair.data import Sentence
import torch
# tokenize text
batch_tokens = [Sentence(v) for v in text]
self._flair.embed(batch_tokens)
return torch.stack([v.embedding for v in batch_tokens]).detach()