677 lines
28 KiB
Python
677 lines
28 KiB
Python
import os
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from queue import Queue
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from threading import Thread
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import pandas as pd
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import tensorflow as tf
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import collections
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import args
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import tokenization
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import modeling
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import optimization
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# os.environ['CUDA_VISIBLE_DEVICES'] = '1'
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class InputExample(object):
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"""A single training/test example for simple sequence classification."""
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def __init__(self, guid, text_a, text_b=None, label=None):
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"""Constructs a InputExample.
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Args:
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guid: Unique id for the example.
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text_a: string. The untokenized text of the first sequence. For single
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sequence tasks, only this sequence must be specified.
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text_b: (Optional) string. The untokenized text of the second sequence.
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Only must be specified for sequence pair tasks.
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label: (Optional) string. The label of the example. This should be
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specified for train and dev examples, but not for test examples.
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"""
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self.guid = guid
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self.text_a = text_a
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self.text_b = text_b
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self.label = label
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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, input_ids, input_mask, segment_ids, label_id):
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.label_id = label_id
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class DataProcessor(object):
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"""Base class for data converters for sequence classification data sets."""
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def get_train_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the train set."""
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raise NotImplementedError()
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def get_dev_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the dev set."""
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raise NotImplementedError()
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def get_test_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for prediction."""
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raise NotImplementedError()
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def get_labels(self):
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"""Gets the list of labels for this data set."""
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raise NotImplementedError()
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class SimProcessor(DataProcessor):
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def get_train_examples(self, data_dir):
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file_path = os.path.join(data_dir, 'train.csv')
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train_df = pd.read_csv(file_path, encoding='utf-8')
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train_data = []
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for index, train in enumerate(train_df.values):
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guid = 'train-%d' % index
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text_a = tokenization.convert_to_unicode(str(train[0]))
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text_b = tokenization.convert_to_unicode(str(train[1]))
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label = str(train[2])
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train_data.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return train_data
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def get_dev_examples(self, data_dir):
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file_path = os.path.join(data_dir, 'dev.csv')
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dev_df = pd.read_csv(file_path, encoding='utf-8')
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dev_data = []
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for index, dev in enumerate(dev_df.values):
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guid = 'test-%d' % index
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text_a = tokenization.convert_to_unicode(str(dev[0]))
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text_b = tokenization.convert_to_unicode(str(dev[1]))
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label = str(dev[2])
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dev_data.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return dev_data
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def get_test_examples(self, data_dir):
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file_path = os.path.join(data_dir, 'test.csv')
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test_df = pd.read_csv(file_path, encoding='utf-8')
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test_data = []
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for index, test in enumerate(test_df.values):
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guid = 'test-%d' % index
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text_a = tokenization.convert_to_unicode(str(test[0]))
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text_b = tokenization.convert_to_unicode(str(test[1]))
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label = str(test[2])
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test_data.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return test_data
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def get_sentence_examples(self, questions):
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for index, data in enumerate(questions):
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guid = 'test-%d' % index
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text_a = tokenization.convert_to_unicode(str(data[0]))
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text_b = tokenization.convert_to_unicode(str(data[1]))
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label = str(0)
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yield InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)
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def get_labels(self):
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return ['0', '1']
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class BertSim:
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def __init__(self, batch_size=args.batch_size):
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self.mode = None
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self.max_seq_length = args.max_seq_len
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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 = None
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self.processor = SimProcessor()
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tf.logging.set_verbosity(tf.logging.INFO)
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def set_mode(self, mode):
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self.mode = mode
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self.estimator = self.get_estimator()
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if mode == tf.estimator.ModeKeys.PREDICT:
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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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def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
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labels, num_labels, use_one_hot_embeddings):
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"""Creates a classification model."""
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model = modeling.BertModel(
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config=bert_config,
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is_training=is_training,
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input_ids=input_ids,
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input_mask=input_mask,
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token_type_ids=segment_ids,
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use_one_hot_embeddings=use_one_hot_embeddings)
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# In the demo, we are doing a simple classification task on the entire
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# segment.
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#
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# If you want to use the token-level output, use model.get_sequence_output()
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# instead.
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output_layer = model.get_pooled_output()
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hidden_size = output_layer.shape[-1].value
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output_weights = tf.get_variable(
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"output_weights", [num_labels, hidden_size],
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initializer=tf.truncated_normal_initializer(stddev=0.02))
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output_bias = tf.get_variable(
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"output_bias", [num_labels], initializer=tf.zeros_initializer())
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with tf.variable_scope("loss"):
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if is_training:
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# I.e., 0.1 dropout
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output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
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logits = tf.matmul(output_layer, output_weights, transpose_b=True)
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logits = tf.nn.bias_add(logits, output_bias)
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probabilities = tf.nn.softmax(logits, axis=-1)
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log_probs = tf.nn.log_softmax(logits, axis=-1)
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one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
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per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
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loss = tf.reduce_mean(per_example_loss)
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return (loss, per_example_loss, logits, probabilities)
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def model_fn_builder(self, bert_config, num_labels, init_checkpoint, learning_rate,
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num_train_steps, num_warmup_steps,
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use_one_hot_embeddings):
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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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from tensorflow.python.estimator.model_fn import EstimatorSpec
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tf.logging.info("*** Features ***")
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for name in sorted(features.keys()):
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tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
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input_ids = features["input_ids"]
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input_mask = features["input_mask"]
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segment_ids = features["segment_ids"]
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label_ids = features["label_ids"]
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is_training = (mode == tf.estimator.ModeKeys.TRAIN)
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(total_loss, per_example_loss, logits, probabilities) = BertSim.create_model(
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bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
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num_labels, use_one_hot_embeddings)
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tvars = tf.trainable_variables()
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initialized_variable_names = {}
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if init_checkpoint:
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(assignment_map, initialized_variable_names) \
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= modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
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tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
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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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if mode == tf.estimator.ModeKeys.TRAIN:
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train_op = optimization.create_optimizer(
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total_loss, learning_rate, num_train_steps, num_warmup_steps, False)
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output_spec = EstimatorSpec(
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mode=mode,
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loss=total_loss,
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train_op=train_op)
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elif mode == tf.estimator.ModeKeys.EVAL:
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def metric_fn(per_example_loss, label_ids, logits):
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predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
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accuracy = tf.metrics.accuracy(label_ids, predictions)
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auc = tf.metrics.auc(label_ids, predictions)
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loss = tf.metrics.mean(per_example_loss)
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return {
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"eval_accuracy": accuracy,
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"eval_auc": auc,
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"eval_loss": loss,
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}
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eval_metrics = metric_fn(per_example_loss, label_ids, logits)
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output_spec = EstimatorSpec(
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mode=mode,
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loss=total_loss,
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eval_metric_ops=eval_metrics)
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else:
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output_spec = EstimatorSpec(mode=mode, predictions=probabilities)
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return output_spec
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return model_fn
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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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bert_config = modeling.BertConfig.from_json_file(args.config_name)
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label_list = self.processor.get_labels()
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train_examples = self.processor.get_train_examples(args.data_dir)
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num_train_steps = int(
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len(train_examples) / self.batch_size * args.num_train_epochs)
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num_warmup_steps = int(num_train_steps * 0.1)
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if self.mode == tf.estimator.ModeKeys.TRAIN:
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init_checkpoint = args.ckpt_name
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else:
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init_checkpoint = args.output_dir
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model_fn = self.model_fn_builder(
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bert_config=bert_config,
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num_labels=len(label_list),
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init_checkpoint=init_checkpoint,
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learning_rate=args.learning_rate,
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num_train_steps=num_train_steps,
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num_warmup_steps=num_warmup_steps,
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use_one_hot_embeddings=False)
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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 = args.gpu_memory_fraction
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config.log_device_placement = False
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return Estimator(model_fn=model_fn, config=RunConfig(session_config=config), model_dir=args.output_dir,
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params={'batch_size': self.batch_size})
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def predict_from_queue(self):
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for i in self.estimator.predict(input_fn=self.queue_predict_input_fn, yield_single_examples=False):
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self.output_queue.put(i)
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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={
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'input_ids': tf.int32,
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'input_mask': tf.int32,
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'segment_ids': tf.int32,
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'label_ids': tf.int32},
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output_shapes={
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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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'segment_ids': (None, self.max_seq_length),
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'label_ids': (1,)}).prefetch(10))
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def convert_examples_to_features(self, examples, label_list, max_seq_length, tokenizer):
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"""Convert a set of `InputExample`s to a list of `InputFeatures`."""
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for (ex_index, example) in enumerate(examples):
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label_map = {}
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for (i, label) in enumerate(label_list):
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label_map[label] = i
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tokens_a = tokenizer.tokenize(example.text_a)
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tokens_b = None
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if example.text_b:
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tokens_b = tokenizer.tokenize(example.text_b)
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if tokens_b:
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# Modifies `tokens_a` and `tokens_b` in place so that the total
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# length is less than the specified length.
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# Account for [CLS], [SEP], [SEP] with "- 3"
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self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
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else:
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# Account for [CLS] and [SEP] with "- 2"
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if len(tokens_a) > max_seq_length - 2:
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tokens_a = tokens_a[0:(max_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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segment_ids = []
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tokens.append("[CLS]")
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segment_ids.append(0)
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for token in tokens_a:
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tokens.append(token)
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segment_ids.append(0)
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tokens.append("[SEP]")
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segment_ids.append(0)
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if tokens_b:
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for token in tokens_b:
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tokens.append(token)
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segment_ids.append(1)
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tokens.append("[SEP]")
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segment_ids.append(1)
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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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# Zero-pad up to the sequence length.
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while len(input_ids) < max_seq_length:
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input_ids.append(0)
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input_mask.append(0)
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segment_ids.append(0)
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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label_id = label_map[example.label]
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if ex_index < 5:
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tf.logging.info("*** Example ***")
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tf.logging.info("guid: %s" % (example.guid))
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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("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
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tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
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feature = InputFeatures(
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input_ids=input_ids,
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input_mask=input_mask,
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segment_ids=segment_ids,
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label_id=label_id)
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yield feature
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def generate_from_queue(self):
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while True:
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predict_examples = self.processor.get_sentence_examples(self.input_queue.get())
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features = list(self.convert_examples_to_features(predict_examples, self.processor.get_labels(),
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args.max_seq_len, self.tokenizer))
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yield {
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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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'segment_ids': [f.segment_ids for f in features],
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'label_ids': [f.label_id for f in features]
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}
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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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def convert_single_example(self, ex_index, example, label_list, max_seq_length, tokenizer):
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"""Converts a single `InputExample` into a single `InputFeatures`."""
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label_map = {}
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for (i, label) in enumerate(label_list):
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label_map[label] = i
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tokens_a = tokenizer.tokenize(example.text_a)
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tokens_b = None
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if example.text_b:
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tokens_b = tokenizer.tokenize(example.text_b)
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if tokens_b:
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# Modifies `tokens_a` and `tokens_b` in place so that the total
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# length is less than the specified length.
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# Account for [CLS], [SEP], [SEP] with "- 3"
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self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
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else:
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# Account for [CLS] and [SEP] with "- 2"
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if len(tokens_a) > max_seq_length - 2:
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tokens_a = tokens_a[0:(max_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
|
||
# it easier for the model to learn the concept of sequences.
|
||
#
|
||
# For classification tasks, the first vector (corresponding to [CLS]) is
|
||
# used as as the "sentence vector". Note that this only makes sense because
|
||
# the entire model is fine-tuned.
|
||
tokens = []
|
||
segment_ids = []
|
||
tokens.append("[CLS]")
|
||
segment_ids.append(0)
|
||
for token in tokens_a:
|
||
tokens.append(token)
|
||
segment_ids.append(0)
|
||
tokens.append("[SEP]")
|
||
segment_ids.append(0)
|
||
|
||
if tokens_b:
|
||
for token in tokens_b:
|
||
tokens.append(token)
|
||
segment_ids.append(1)
|
||
tokens.append("[SEP]")
|
||
segment_ids.append(1)
|
||
|
||
input_ids = tokenizer.convert_tokens_to_ids(tokens)
|
||
|
||
# The mask has 1 for real tokens and 0 for padding tokens. Only real
|
||
# tokens are attended to.
|
||
input_mask = [1] * len(input_ids)
|
||
|
||
# Zero-pad up to the sequence length.
|
||
while len(input_ids) < max_seq_length:
|
||
input_ids.append(0)
|
||
input_mask.append(0)
|
||
segment_ids.append(0)
|
||
|
||
assert len(input_ids) == max_seq_length
|
||
assert len(input_mask) == max_seq_length
|
||
assert len(segment_ids) == max_seq_length
|
||
|
||
label_id = label_map[example.label]
|
||
if ex_index < 5:
|
||
tf.logging.info("*** Example ***")
|
||
tf.logging.info("guid: %s" % (example.guid))
|
||
tf.logging.info("tokens: %s" % " ".join(
|
||
[tokenization.printable_text(x) for x in tokens]))
|
||
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
|
||
tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
|
||
tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
|
||
tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
|
||
|
||
feature = InputFeatures(
|
||
input_ids=input_ids,
|
||
input_mask=input_mask,
|
||
segment_ids=segment_ids,
|
||
label_id=label_id)
|
||
return feature
|
||
|
||
def file_based_convert_examples_to_features(self, examples, label_list, max_seq_length, tokenizer, output_file):
|
||
"""Convert a set of `InputExample`s to a TFRecord file."""
|
||
|
||
writer = tf.python_io.TFRecordWriter(output_file)
|
||
|
||
for (ex_index, example) in enumerate(examples):
|
||
if ex_index % 10000 == 0:
|
||
tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
|
||
|
||
feature = self.convert_single_example(ex_index, example, label_list,
|
||
max_seq_length, tokenizer)
|
||
|
||
def create_int_feature(values):
|
||
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
|
||
return f
|
||
|
||
features = collections.OrderedDict()
|
||
features["input_ids"] = create_int_feature(feature.input_ids)
|
||
features["input_mask"] = create_int_feature(feature.input_mask)
|
||
features["segment_ids"] = create_int_feature(feature.segment_ids)
|
||
features["label_ids"] = create_int_feature([feature.label_id])
|
||
|
||
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
|
||
writer.write(tf_example.SerializeToString())
|
||
|
||
def file_based_input_fn_builder(self, input_file, seq_length, is_training, drop_remainder):
|
||
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
|
||
|
||
name_to_features = {
|
||
"input_ids": tf.FixedLenFeature([seq_length], tf.int64),
|
||
"input_mask": tf.FixedLenFeature([seq_length], tf.int64),
|
||
"segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
|
||
"label_ids": tf.FixedLenFeature([], tf.int64),
|
||
}
|
||
|
||
def _decode_record(record, name_to_features):
|
||
"""Decodes a record to a TensorFlow example."""
|
||
example = tf.parse_single_example(record, name_to_features)
|
||
|
||
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
|
||
# So cast all int64 to int32.
|
||
for name in list(example.keys()):
|
||
t = example[name]
|
||
if t.dtype == tf.int64:
|
||
t = tf.to_int32(t)
|
||
example[name] = t
|
||
|
||
return example
|
||
|
||
def input_fn(params):
|
||
"""The actual input function."""
|
||
batch_size = params["batch_size"]
|
||
|
||
# For training, we want a lot of parallel reading and shuffling.
|
||
# For eval, we want no shuffling and parallel reading doesn't matter.
|
||
d = tf.data.TFRecordDataset(input_file)
|
||
if is_training:
|
||
d = d.repeat()
|
||
d = d.shuffle(buffer_size=100)
|
||
|
||
d = d.apply(
|
||
tf.contrib.data.map_and_batch(
|
||
lambda record: _decode_record(record, name_to_features),
|
||
batch_size=batch_size,
|
||
drop_remainder=drop_remainder))
|
||
|
||
return d
|
||
|
||
return input_fn
|
||
|
||
def train(self):
|
||
if self.mode is None:
|
||
raise ValueError("Please set the 'mode' parameter")
|
||
|
||
bert_config = modeling.BertConfig.from_json_file(args.config_name)
|
||
|
||
if args.max_seq_len > bert_config.max_position_embeddings:
|
||
raise ValueError(
|
||
"Cannot use sequence length %d because the BERT model "
|
||
"was only trained up to sequence length %d" %
|
||
(args.max_seq_len, bert_config.max_position_embeddings))
|
||
|
||
tf.gfile.MakeDirs(args.output_dir)
|
||
|
||
label_list = self.processor.get_labels()
|
||
|
||
train_examples = self.processor.get_train_examples(args.data_dir)
|
||
num_train_steps = int(len(train_examples) / args.batch_size * args.num_train_epochs)
|
||
|
||
estimator = self.get_estimator()
|
||
|
||
train_file = os.path.join(args.output_dir, "train.tf_record")
|
||
self.file_based_convert_examples_to_features(train_examples, label_list, args.max_seq_len, self.tokenizer,
|
||
train_file)
|
||
tf.logging.info("***** Running training *****")
|
||
tf.logging.info(" Num examples = %d", len(train_examples))
|
||
tf.logging.info(" Batch size = %d", args.batch_size)
|
||
tf.logging.info(" Num steps = %d", num_train_steps)
|
||
train_input_fn = self.file_based_input_fn_builder(input_file=train_file, seq_length=args.max_seq_len,
|
||
is_training=True,
|
||
drop_remainder=True)
|
||
|
||
# early_stopping = tf.contrib.estimator.stop_if_no_decrease_hook(
|
||
# estimator,
|
||
# metric_name='loss',
|
||
# max_steps_without_decrease=10,
|
||
# min_steps=num_train_steps)
|
||
|
||
# estimator.train(input_fn=train_input_fn, hooks=[early_stopping])
|
||
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
|
||
|
||
def eval(self):
|
||
if self.mode is None:
|
||
raise ValueError("Please set the 'mode' parameter")
|
||
eval_examples = self.processor.get_dev_examples(args.data_dir)
|
||
eval_file = os.path.join(args.output_dir, "eval.tf_record")
|
||
label_list = self.processor.get_labels()
|
||
self.file_based_convert_examples_to_features(
|
||
eval_examples, label_list, args.max_seq_len, self.tokenizer, eval_file)
|
||
|
||
tf.logging.info("***** Running evaluation *****")
|
||
tf.logging.info(" Num examples = %d", len(eval_examples))
|
||
tf.logging.info(" Batch size = %d", self.batch_size)
|
||
|
||
eval_input_fn = self.file_based_input_fn_builder(
|
||
input_file=eval_file,
|
||
seq_length=args.max_seq_len,
|
||
is_training=False,
|
||
drop_remainder=False)
|
||
|
||
estimator = self.get_estimator()
|
||
result = estimator.evaluate(input_fn=eval_input_fn, steps=None)
|
||
|
||
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
|
||
with tf.gfile.GFile(output_eval_file, "w") as writer:
|
||
tf.logging.info("***** Eval results *****")
|
||
for key in sorted(result.keys()):
|
||
tf.logging.info(" %s = %s", key, str(result[key]))
|
||
writer.write("%s = %s\n" % (key, str(result[key])))
|
||
|
||
def predict(self, sentence1, sentence2):
|
||
if self.mode is None:
|
||
raise ValueError("Please set the 'mode' parameter")
|
||
self.input_queue.put([(sentence1, sentence2)])
|
||
prediction = self.output_queue.get()
|
||
return prediction
|
||
|
||
|
||
if __name__ == '__main__':
|
||
sim = BertSim()
|
||
sim.set_mode(tf.estimator.ModeKeys.TRAIN)
|
||
sim.train()
|
||
sim.set_mode(tf.estimator.ModeKeys.EVAL)
|
||
sim.eval()
|
||
# sim.set_mode(tf.estimator.ModeKeys.PREDICT)
|
||
# while True:
|
||
# sentence1 = input('sentence1: ')
|
||
# sentence2 = input('sentence2: ')
|
||
# predict = sim.predict(sentence1, sentence2)
|
||
# print(f'similarity：{predict[0][1]}')
|