修改多sentence的bug
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@ -1,4 +1,6 @@
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/chinese_L-12_H-768_A-12
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tmp/
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__pycache__/
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.idea/
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.idea/
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data/data_merger.py
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data/data.py
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@ -5,6 +5,9 @@ import args
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from queue import Queue
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from threading import Thread
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import tensorflow as tf
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import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '1'
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class InputExample(object):
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@ -43,7 +46,7 @@ class BertVector:
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self.input_queue = Queue(maxsize=1)
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self.output_queue = Queue(maxsize=1)
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self.predict_thread = Thread(target=self.predict_from_queue, daemon=True)
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self.predict_thread.start()
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self.sentence_len = 0
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def get_estimator(self):
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from tensorflow.python.estimator.estimator import Estimator
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@ -80,6 +83,8 @@ class BertVector:
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self.output_queue.put(i)
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def encode(self, sentence):
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self.sentence_len = len(sentence)
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self.predict_thread.start()
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self.input_queue.put(sentence)
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prediction = self.output_queue.get()
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return prediction
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@ -93,7 +98,7 @@ class BertVector:
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'input_mask': tf.int32,
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'input_type_ids': tf.int32},
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output_shapes={
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'unique_ids': (1,),
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'unique_ids': (self.sentence_len,),
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'input_ids': (None, self.max_seq_length),
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'input_mask': (None, self.max_seq_length),
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'input_type_ids': (None, self.max_seq_length)}))
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@ -327,7 +332,7 @@ class BertVector:
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if __name__ == "__main__":
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bert = BertVector()
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while True:
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question = input('question: ')
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vectors = bert.encode([question])
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print(str(vectors))
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# while True:
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# question = input('question: ')
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vectors = bert.encode(['你好', '哈哈'])
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print(str(vectors))
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