339 lines
13 KiB
Python
339 lines
13 KiB
Python
import modeling
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import tokenization
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from graph import optimize_graph
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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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def __init__(self, unique_id, text_a, text_b):
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self.unique_id = unique_id
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self.text_a = text_a
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self.text_b = text_b
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class InputFeatures(object):
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"""A single set of features of data."""
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def __init__(self, unique_id, tokens, input_ids, input_mask, input_type_ids):
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self.unique_id = unique_id
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self.tokens = tokens
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.input_type_ids = input_type_ids
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class BertVector:
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def __init__(self, batch_size=32):
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"""
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init BertVector
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:param batch_size: Depending on your memory default is 32
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"""
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self.max_seq_length = args.max_seq_len
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self.layer_indexes = args.layer_indexes
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self.gpu_memory_fraction = 1
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self.graph_path = optimize_graph()
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self.tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=True)
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self.batch_size = batch_size
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self.estimator = self.get_estimator()
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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.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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from tensorflow.python.estimator.run_config import RunConfig
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from tensorflow.python.estimator.model_fn import EstimatorSpec
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def model_fn(features, labels, mode, params):
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with tf.gfile.GFile(self.graph_path, 'rb') as f:
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graph_def = tf.GraphDef()
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graph_def.ParseFromString(f.read())
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input_names = ['input_ids', 'input_mask', 'input_type_ids']
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output = tf.import_graph_def(graph_def,
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input_map={k + ':0': features[k] for k in input_names},
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return_elements=['final_encodes:0'])
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return EstimatorSpec(mode=mode, predictions={
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'encodes': output[0]
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})
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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config.gpu_options.per_process_gpu_memory_fraction = self.gpu_memory_fraction
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config.log_device_placement = False
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config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
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return Estimator(model_fn=model_fn, config=RunConfig(session_config=config),
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params={'batch_size': self.batch_size})
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def predict_from_queue(self):
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prediction = self.estimator.predict(input_fn=self.queue_predict_input_fn, yield_single_examples=False)
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for i in prediction:
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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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def queue_predict_input_fn(self):
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return (tf.data.Dataset.from_generator(
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self.generate_from_queue,
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output_types={'unique_ids': tf.int32,
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'input_ids': tf.int32,
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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': (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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def generate_from_queue(self):
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while True:
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features = list(self.convert_examples_to_features(seq_length=self.max_seq_length, tokenizer=self.tokenizer))
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yield {
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'unique_ids': [f.unique_id for f in features],
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'input_ids': [f.input_ids for f in features],
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'input_mask': [f.input_mask for f in features],
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'input_type_ids': [f.input_type_ids for f in features]
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}
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def input_fn_builder(self, features, seq_length):
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"""Creates an `input_fn` closure to be passed to Estimator."""
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all_unique_ids = []
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all_input_ids = []
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all_input_mask = []
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all_input_type_ids = []
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for feature in features:
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all_unique_ids.append(feature.unique_id)
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all_input_ids.append(feature.input_ids)
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all_input_mask.append(feature.input_mask)
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all_input_type_ids.append(feature.input_type_ids)
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def input_fn(params):
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"""The actual input function."""
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batch_size = params["batch_size"]
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num_examples = len(features)
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# This is for demo purposes and does NOT scale to large data sets. We do
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# not use Dataset.from_generator() because that uses tf.py_func which is
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# not TPU compatible. The right way to load data is with TFRecordReader.
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d = tf.data.Dataset.from_tensor_slices({
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"unique_ids":
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tf.constant(all_unique_ids, shape=[num_examples], dtype=tf.int32),
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"input_ids":
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tf.constant(
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all_input_ids, shape=[num_examples, seq_length],
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dtype=tf.int32),
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"input_mask":
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tf.constant(
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all_input_mask,
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shape=[num_examples, seq_length],
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dtype=tf.int32),
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"input_type_ids":
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tf.constant(
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all_input_type_ids,
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shape=[num_examples, seq_length],
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dtype=tf.int32),
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})
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d = d.batch(batch_size=batch_size, drop_remainder=False)
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return d
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return input_fn
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def model_fn_builder(self, bert_config, init_checkpoint, layer_indexes):
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"""Returns `model_fn` closure for TPUEstimator."""
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def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
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"""The `model_fn` for TPUEstimator."""
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unique_ids = features["unique_ids"]
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input_ids = features["input_ids"]
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input_mask = features["input_mask"]
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input_type_ids = features["input_type_ids"]
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jit_scope = tf.contrib.compiler.jit.experimental_jit_scope
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with jit_scope():
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model = modeling.BertModel(
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config=bert_config,
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is_training=False,
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input_ids=input_ids,
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input_mask=input_mask,
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token_type_ids=input_type_ids)
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if mode != tf.estimator.ModeKeys.PREDICT:
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raise ValueError("Only PREDICT modes are supported: %s" % (mode))
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tvars = tf.trainable_variables()
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(assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars,
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init_checkpoint)
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tf.logging.info("**** Trainable Variables ****")
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for var in tvars:
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init_string = ""
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if var.name in initialized_variable_names:
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init_string = ", *INIT_FROM_CKPT*"
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tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
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init_string)
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all_layers = model.get_all_encoder_layers()
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predictions = {
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"unique_id": unique_ids,
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}
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for (i, layer_index) in enumerate(layer_indexes):
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predictions["layer_output_%d" % i] = all_layers[layer_index]
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from tensorflow.python.estimator.model_fn import EstimatorSpec
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output_spec = EstimatorSpec(mode=mode, predictions=predictions)
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return output_spec
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return model_fn
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def convert_examples_to_features(self, seq_length, tokenizer):
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"""Loads a data file into a list of `InputBatch`s."""
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features = []
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input_masks = []
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examples = self._to_example(self.input_queue.get())
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for (ex_index, example) in enumerate(examples):
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tokens_a = tokenizer.tokenize(example.text_a)
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# if the sentences's length is more than seq_length, only use sentence's left part
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if len(tokens_a) > seq_length - 2:
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tokens_a = tokens_a[0:(seq_length - 2)]
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# The convention in BERT is:
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# (a) For sequence pairs:
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# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
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# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
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# (b) For single sequences:
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# tokens: [CLS] the dog is hairy . [SEP]
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# type_ids: 0 0 0 0 0 0 0
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#
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# Where "type_ids" are used to indicate whether this is the first
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# sequence or the second sequence. The embedding vectors for `type=0` and
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# `type=1` were learned during pre-training and are added to the wordpiece
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# embedding vector (and position vector). This is not *strictly* necessary
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# since the [SEP] token unambiguously separates the sequences, but it makes
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# it easier for the model to learn the concept of sequences.
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#
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# For classification tasks, the first vector (corresponding to [CLS]) is
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# used as as the "sentence vector". Note that this only makes sense because
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# the entire model is fine-tuned.
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tokens = []
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input_type_ids = []
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tokens.append("[CLS]")
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input_type_ids.append(0)
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for token in tokens_a:
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tokens.append(token)
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input_type_ids.append(0)
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tokens.append("[SEP]")
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input_type_ids.append(0)
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# Where "input_ids" are tokens's index in vocabulary
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1] * len(input_ids)
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input_masks.append(input_mask)
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# Zero-pad up to the sequence length.
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while len(input_ids) < seq_length:
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input_ids.append(0)
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input_mask.append(0)
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input_type_ids.append(0)
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assert len(input_ids) == seq_length
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assert len(input_mask) == seq_length
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assert len(input_type_ids) == seq_length
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if ex_index < 5:
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tf.logging.info("*** Example ***")
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tf.logging.info("unique_id: %s" % (example.unique_id))
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tf.logging.info("tokens: %s" % " ".join(
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[tokenization.printable_text(x) for x in tokens]))
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tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
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tf.logging.info(
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"input_type_ids: %s" % " ".join([str(x) for x in input_type_ids]))
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yield InputFeatures(
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unique_id=example.unique_id,
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tokens=tokens,
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input_ids=input_ids,
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input_mask=input_mask,
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input_type_ids=input_type_ids)
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def _truncate_seq_pair(self, tokens_a, tokens_b, max_length):
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"""Truncates a sequence pair in place to the maximum length."""
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# This is a simple heuristic which will always truncate the longer sequence
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# one token at a time. This makes more sense than truncating an equal percent
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# of tokens from each, since if one sequence is very short then each token
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# that's truncated likely contains more information than a longer sequence.
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while True:
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total_length = len(tokens_a) + len(tokens_b)
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if total_length <= max_length:
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break
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if len(tokens_a) > len(tokens_b):
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tokens_a.pop()
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else:
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tokens_b.pop()
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@staticmethod
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def _to_example(sentences):
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import re
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"""
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sentences to InputExample
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:param sentences: list of strings
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:return: list of InputExample
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"""
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unique_id = 0
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for ss in sentences:
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line = tokenization.convert_to_unicode(ss)
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if not line:
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continue
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line = line.strip()
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text_a = None
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text_b = None
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m = re.match(r"^(.*) \|\|\| (.*)$", line)
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if m is None:
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text_a = line
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else:
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text_a = m.group(1)
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text_b = m.group(2)
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yield InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b)
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unique_id += 1
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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(['你好', '哈哈'])
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print(str(vectors))
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