In [1]:
from google.colab import drive
drive.mount('/content/gdrive')
import os
os.chdir('/content/gdrive/My Drive/finch/tensorflow2/text_matching/joint/main')
Mounted at /content/gdrive
In [2]:
%tensorflow_version 2.x
!pip install tensorflow-addons
Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.6/dist-packages (0.8.3)
Requirement already satisfied: typeguard in /usr/local/lib/python3.6/dist-packages (from tensorflow-addons) (2.7.1)
In [3]:
from sklearn.metrics import classification_report

import tensorflow as tf
import tensorflow_addons as tfa
import numpy as np
import pprint
import logging
import time
import math
import json
import csv

print("TensorFlow Version", tf.__version__)
print('GPU Enabled:', tf.test.is_gpu_available())
TensorFlow Version 2.3.0
WARNING:tensorflow:From <ipython-input-3-efe7d2d6e6b5>:14: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.list_physical_devices('GPU')` instead.
GPU Enabled: True
In [4]:
def get_vocab(f_path):
  k2v = {}
  with open(f_path) as f:
    for i, line in enumerate(f):
      line = line.rstrip()
      k2v[line] = i
  return k2v
In [5]:
def data_gen_cs(char2idx):
  f_path = '../data/test.csv'
  with open(f_path) as f:
    print('Reading', f_path)
    for i, line in enumerate(csv.reader(f, delimiter=',')):
      if i == 0:
        continue
      text1, text2, label = line
      text1 = [char2idx.get(c, len(char2idx)) for c in list(text1)]
      text2 = [char2idx.get(c, len(char2idx)) for c in list(text2)]
      if len(text1) > params['max_len']:
        text1 = text1[:params['max_len']]
      if len(text2) > params['max_len']:
        text2 = text2[:params['max_len']]
      yield ((text1, text2), int(label))


def data_gen_js(char2idx):
  f_path = '../data/dev.json'
  with open(f_path) as f:
    print('Reading', f_path)
    for line in f:
      line = json.loads(line.rstrip())
      text1, text2, label = line['sentence1'], line['sentence2'], line['label']
      text1 = [char2idx.get(c, len(char2idx)) for c in list(text1)]
      text2 = [char2idx.get(c, len(char2idx)) for c in list(text2)]
      yield ((text1, text2), int(label))


def joint_data_gen(char2idx):
  f_path = '../data/train.csv'
  with open(f_path) as f:
    print('Reading', f_path)
    for i, line in enumerate(csv.reader(f, delimiter=',')):
      if i == 0:
        continue
      text1, text2, label = line
      text1 = [char2idx.get(c, len(char2idx)) for c in list(text1)]
      text2 = [char2idx.get(c, len(char2idx)) for c in list(text2)]
      if len(text1) > params['max_len']:
        text1 = text1[:params['max_len']]
      if len(text2) > params['max_len']:
        text2 = text2[:params['max_len']]
      yield ((text1, text2), int(label))
  f_path = '../data/train.json'
  with open(f_path) as f:
    print('Reading', f_path)
    for line in f:
      line = json.loads(line.rstrip())
      text1, text2, label = line['sentence1'], line['sentence2'], line['label']
      text1 = [char2idx.get(c, len(char2idx)) for c in list(text1)]
      text2 = [char2idx.get(c, len(char2idx)) for c in list(text2)]
      yield ((text1, text2), int(label))
In [6]:
def get_datasets(params):
  _shapes = (([None], [None]), ())
  _types = ((tf.int32, tf.int32), tf.int32)
  _pads = ((0, 0), -1)
  
  ds_train = tf.data.Dataset.from_generator(
    lambda: joint_data_gen(params['char2idx']),
    output_shapes = _shapes,
    output_types = _types,)
  ds_train = ds_train.shuffle(params['buffer_size'])
  ds_train = ds_train.padded_batch(params['batch_size'], _shapes, _pads)
  ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)

  ds_test_js = tf.data.Dataset.from_generator(
    lambda: data_gen_js(params['char2idx']),
    output_shapes = _shapes,
    output_types = _types,)
  ds_test_js = ds_test_js.padded_batch(params['batch_size'], _shapes, _pads)
  ds_test_js = ds_test_js.prefetch(tf.data.experimental.AUTOTUNE)

  ds_test_cs = tf.data.Dataset.from_generator(
    lambda: data_gen_cs(params['char2idx']),
    output_shapes = _shapes,
    output_types = _types,)
  ds_test_cs = ds_test_cs.padded_batch(params['batch_size'], _shapes, _pads)
  ds_test_cs = ds_test_cs.prefetch(tf.data.experimental.AUTOTUNE)
  
  return ds_train, ds_test_js, ds_test_cs
In [7]:
class FFNBlock(tf.keras.Model):
  def __init__(self, params, name):
    super().__init__(name = name)
    self.dropout1 = tf.keras.layers.Dropout(params['dropout_rate'])
    self.fc1 = tf.keras.layers.Dense(params['hidden_units'], params['activation'])
    self.dropout2 = tf.keras.layers.Dropout(params['dropout_rate'])
    self.fc2 = tf.keras.layers.Dense(params['hidden_units'], params['activation'])
  
  def call(self, inputs, training=False):
    x = inputs
    x = self.dropout1(x, training=training)
    x = self.fc1(x)
    x = self.dropout2(x, training=training)
    x = self.fc2(x)
    return x
In [8]:
class Pyramid(tf.keras.Model):
  def __init__(self, params: dict):
    super().__init__()
    self.embedding = tf.Variable(np.load(params['embedding_path']), name='pretrained_embedding', dtype=tf.float32)
    
    self.inp_dropout = tf.keras.layers.Dropout(params['dropout_rate'])
    
    self.encoder = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
      params['hidden_units'], return_sequences=True, zero_output_for_mask=True), name='encoder')
    
    self.W_0 = tf.keras.layers.Dense(2*params['hidden_units'], use_bias=False)
    
    self.W_1_1 = tf.keras.layers.Dense(params['hidden_units'], use_bias=False)
    
    self.W_1_2 = tf.keras.layers.Dense(params['hidden_units'], use_bias=False)
    
    self.v_1 = tf.keras.layers.Dense(1, use_bias=False)
    
    self.W_2 = tf.keras.layers.Dense(params['hidden_units'], use_bias=False)
    
    self.v_2 = tf.keras.layers.Dense(1, use_bias=False)
    
    self.W_3 = tf.keras.layers.Dense(params['hidden_units'], use_bias=False)
    
    self.v_3 = tf.keras.layers.Dense(1, use_bias=False)
    
    self.img_dropout = tf.keras.layers.Dropout(params['dropout_rate']/2)

    self.conv_1 = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=params['activation'], padding='same')
    
    self.conv_2 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation=params['activation'], padding='same')
    
    self.conv_3 = tf.keras.layers.Conv2D(filters=128, kernel_size=3, activation=params['activation'], padding='same')

    self.flatten = tf.keras.layers.Flatten()
    
    self.out_hidden = FFNBlock(params, name='out_hidden')
    
    self.out_linear = tf.keras.layers.Dense(1, name='out_linear')

  
  def get_stride(self, x, fixed_len):
    batch_sz = tf.shape(x)[0]
    len = x.shape[1]
    stride = len // fixed_len
    if len // stride != fixed_len:
      remin = (stride + 1) * fixed_len - len
      zeros = tf.zeros([batch_sz, remin], tf.int32)
      x = tf.concat([x, zeros], 1)
      len = x.shape[1]
      stride = len // fixed_len
    return x, stride
  
  
  def call(self, inputs, training=False):
    x1, x2 = inputs
    
    if x1.dtype != tf.int32:
      x1 = tf.cast(x1, tf.int32)
    if x2.dtype != tf.int32:
      x2 = tf.cast(x2, tf.int32)
    
    x1, stride1 = self.get_stride(x1, params['fixed_len1'])
    x2, stride2 = self.get_stride(x2, params['fixed_len2'])
    
    mask1 = tf.sign(x1)
    mask2 = tf.sign(x2)
    
    x1 = tf.nn.embedding_lookup(self.embedding, x1)
    x2 = tf.nn.embedding_lookup(self.embedding, x2)
    
    x1 = self.inp_dropout(x1, training=training)
    x2 = self.inp_dropout(x2, training=training)
    
    x1 = self.encoder(x1, mask=tf.cast(mask1, tf.bool))
    x2 = self.encoder(x2, mask=tf.cast(mask2, tf.bool))

    x = []

    # attention 1 (bilinear)
    a = tf.matmul(x1, self.W_0(x2), transpose_b=True)
    x.append(tf.expand_dims(a, -1))
    
    # attention 2 (add)
    a1 = tf.expand_dims(self.W_1_1(x1), 2)
    a2 = tf.expand_dims(self.W_1_2(x2), 1)
    x.append(self.v_1(tf.tanh(a1 + a2)))
    
    # attention 3 (minus)
    a1 = tf.expand_dims(x1, 2)
    a2 = tf.expand_dims(x2, 1)
    x.append(self.v_2(tf.tanh(self.W_2(tf.abs(a1 - a2)))))
    
    # attention 4 (dot)
    a1 = tf.expand_dims(x1, 2)
    a2 = tf.expand_dims(x2, 1)
    x.append(self.v_3(tf.tanh(self.W_3(a1 * a2))))
    
    x = tf.concat(x, -1)
    x = self.img_dropout(x, training=training)

    x = self.conv_1(x)
    x = tf.nn.max_pool(x, [1, stride1, stride2, 1], [1, stride1, stride2, 1], 'VALID')
    x = self.conv_2(x)
    x = tf.nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
    x = self.conv_3(x)
    x = tf.nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
    
    x = self.flatten(x)
    x = self.out_hidden(x, training=training)
    x = self.out_linear(x)
    x = tf.squeeze(x, 1)
    
    return x
In [9]:
params = {
  'train_path': '../data/train.json',
  'test_path': '../data/dev.json',
  'vocab_path': '../vocab/char.txt',
  'embedding_path': '../vocab/char.npy',
  'batch_size': 32,
  'max_len': 50,
  'buffer_size': 34334 + 100000,
  'dropout_rate': .2,
  'hidden_units': 300,
  'fixed_len1': 12,
  'fixed_len2': 12,
  'activation': tf.nn.swish,
  'init_lr': 1e-4,
  'max_lr': 8e-4,
  'label_smooth': .2,
  'clip_norm': .1,
  'num_patience': 10,
}
In [10]:
def label_smoothing(label, smooth):
  if smooth > 0.:
    return label * (1 - smooth) + 0.5 * smooth
  else:
    return label

params['char2idx'] = get_vocab(params['vocab_path'])
params['vocab_size'] = len(params['char2idx']) + 1
In [ ]:
model = Pyramid(params)
model.build([[None, 13], [None, 13]])
pprint.pprint([(v.name, v.shape) for v in model.trainable_variables])

decay_lr = tfa.optimizers.Triangular2CyclicalLearningRate(
  initial_learning_rate = params['init_lr'],
  maximal_learning_rate = params['max_lr'],
  step_size = 4 * params['buffer_size'] // params['batch_size'],)
optim = tf.optimizers.Adam(params['init_lr'])
global_step = 0

best_acc, best_acc1, best_acc2 = .0, .0, .0
count = 0

t0 = time.time()
logger = logging.getLogger('tensorflow')
logger.setLevel(logging.INFO)

while True:
  ds_train, ds_test_js, ds_test_cs = get_datasets(params)

  # TRAINING
  for ((text1, text2), labels) in ds_train:
    with tf.GradientTape() as tape:
      logits = model((text1, text2), training=True)
      labels = tf.cast(labels, tf.float32)
      num_neg = tf.reduce_sum(tf.cast(tf.equal(labels, 0.), tf.float32)).numpy()
      num_pos = tf.reduce_sum(labels).numpy()
      if num_pos == 0.:
        pos_weight = 1.
      else:
        pos_weight = num_neg / num_pos
      loss = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(
        labels = label_smoothing(labels, params['label_smooth']),
        logits = logits,
        pos_weight = pos_weight))
    
    optim.lr.assign(decay_lr(global_step))
    grads = tape.gradient(loss, model.trainable_variables)
    grads, _ = tf.clip_by_global_norm(grads, params['clip_norm'])
    optim.apply_gradients(zip(grads, model.trainable_variables))
    
    if global_step % 100 == 0:
      logger.info("Step {} | Loss: {:.4f} | Spent: {:.1f} secs | LR: {:.6f}".format(
        global_step, loss.numpy().item(), time.time()-t0, optim.lr.numpy().item()))
      t0 = time.time()
    global_step += 1
  
  # EVALUATION 1
  m = tf.keras.metrics.Accuracy()
  intent_true = []
  intent_pred = []

  for ((text1, text2), labels) in ds_test_cs:
    logits = tf.sigmoid(model((text1, text2), training=False))
    y_pred = tf.cast(tf.math.greater_equal(logits, .5), tf.int32)
    m.update_state(y_true=labels, y_pred=y_pred)
    intent_true += labels.numpy().flatten().tolist()
    intent_pred += y_pred.numpy().flatten().tolist()

  acc_1 = m.result().numpy()
  logger.info('测试集:微众银行智能客服')
  logger.info("Evaluation: Testing Accuracy: {:.3f}".format(acc_1))
  logger.info('\n'+classification_report(y_true = intent_true,
                                         y_pred = intent_pred,
                                         labels = [0, 1],
                                         target_names = ['Not Matched', 'Matched'],
                                         digits = 3))

  # EVALUATION 2
  m = tf.keras.metrics.Accuracy()
  intent_true = []
  intent_pred = []

  for ((text1, text2), labels) in ds_test_js:
    logits = tf.sigmoid(model((text1, text2), training=False))
    y_pred = tf.cast(tf.math.greater_equal(logits, .5), tf.int32)
    m.update_state(y_true=labels, y_pred=y_pred)
    intent_true += labels.numpy().flatten().tolist()
    intent_pred += y_pred.numpy().flatten().tolist()

  acc_2 = m.result().numpy()
  logger.info('测试集:蚂蚁金融语义相似度')
  logger.info("Evaluation: Testing Accuracy: {:.3f}".format(acc_2))
  logger.info('\n'+classification_report(y_true = intent_true,
                                         y_pred = intent_pred,
                                         labels = [0, 1],
                                         target_names = ['Not Matched', 'Matched'],
                                         digits = 3))

  # Define Where To Save Model and Stop Training
  acc = (acc_1 + acc_2) / 2.
  if acc > best_acc:
    best_acc = acc
    best_acc1 = acc_1
    best_acc2 = acc_2
    # you can save model here
    count = 0
  else:
    count += 1
  logger.info("Best | Accuracy 1: {:.3f} | Accuracy 2: {:.3f}".format(best_acc1, best_acc2))

  if count == params['num_patience']:
    print(params['num_patience'], "times not improve the best result, therefore stop training")
    break
[('encoder/forward_lstm/lstm_cell_1/kernel:0', TensorShape([300, 1200])),
 ('encoder/forward_lstm/lstm_cell_1/recurrent_kernel:0',
  TensorShape([300, 1200])),
 ('encoder/forward_lstm/lstm_cell_1/bias:0', TensorShape([1200])),
 ('encoder/backward_lstm/lstm_cell_2/kernel:0', TensorShape([300, 1200])),
 ('encoder/backward_lstm/lstm_cell_2/recurrent_kernel:0',
  TensorShape([300, 1200])),
 ('encoder/backward_lstm/lstm_cell_2/bias:0', TensorShape([1200])),
 ('dense/kernel:0', TensorShape([600, 600])),
 ('dense_1/kernel:0', TensorShape([600, 300])),
 ('dense_2/kernel:0', TensorShape([600, 300])),
 ('dense_3/kernel:0', TensorShape([300, 1])),
 ('dense_4/kernel:0', TensorShape([600, 300])),
 ('dense_5/kernel:0', TensorShape([300, 1])),
 ('dense_6/kernel:0', TensorShape([600, 300])),
 ('dense_7/kernel:0', TensorShape([300, 1])),
 ('conv2d/kernel:0', TensorShape([3, 3, 4, 32])),
 ('conv2d/bias:0', TensorShape([32])),
 ('conv2d_1/kernel:0', TensorShape([3, 3, 32, 64])),
 ('conv2d_1/bias:0', TensorShape([64])),
 ('conv2d_2/kernel:0', TensorShape([3, 3, 64, 128])),
 ('conv2d_2/bias:0', TensorShape([128])),
 ('out_hidden/dense_8/kernel:0', TensorShape([1152, 300])),
 ('out_hidden/dense_8/bias:0', TensorShape([300])),
 ('out_hidden/dense_9/kernel:0', TensorShape([300, 300])),
 ('out_hidden/dense_9/bias:0', TensorShape([300])),
 ('out_linear/kernel:0', TensorShape([300, 1])),
 ('out_linear/bias:0', TensorShape([1])),
 ('pretrained_embedding:0', TensorShape([1646, 300]))]
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 0 | Loss: 0.6503 | Spent: 30.4 secs | LR: 0.000100
INFO:tensorflow:Step 100 | Loss: 0.7659 | Spent: 9.0 secs | LR: 0.000104
INFO:tensorflow:Step 200 | Loss: 0.8170 | Spent: 8.5 secs | LR: 0.000108
INFO:tensorflow:Step 300 | Loss: 0.7759 | Spent: 8.5 secs | LR: 0.000113
INFO:tensorflow:Step 400 | Loss: 0.6943 | Spent: 8.7 secs | LR: 0.000117
INFO:tensorflow:Step 500 | Loss: 0.8000 | Spent: 8.3 secs | LR: 0.000121
INFO:tensorflow:Step 600 | Loss: 0.7650 | Spent: 8.1 secs | LR: 0.000125
INFO:tensorflow:Step 700 | Loss: 0.7743 | Spent: 8.2 secs | LR: 0.000129
INFO:tensorflow:Step 800 | Loss: 0.7336 | Spent: 8.7 secs | LR: 0.000133
INFO:tensorflow:Step 900 | Loss: 0.6859 | Spent: 8.9 secs | LR: 0.000138
INFO:tensorflow:Step 1000 | Loss: 0.7478 | Spent: 8.9 secs | LR: 0.000142
INFO:tensorflow:Step 1100 | Loss: 0.8519 | Spent: 8.4 secs | LR: 0.000146
INFO:tensorflow:Step 1200 | Loss: 0.8347 | Spent: 8.2 secs | LR: 0.000150
INFO:tensorflow:Step 1300 | Loss: 0.6363 | Spent: 8.5 secs | LR: 0.000154
INFO:tensorflow:Step 1400 | Loss: 0.7723 | Spent: 8.7 secs | LR: 0.000158
INFO:tensorflow:Step 1500 | Loss: 0.9882 | Spent: 8.5 secs | LR: 0.000163
INFO:tensorflow:Step 1600 | Loss: 0.5410 | Spent: 8.3 secs | LR: 0.000167
INFO:tensorflow:Step 1700 | Loss: 0.5686 | Spent: 8.3 secs | LR: 0.000171
INFO:tensorflow:Step 1800 | Loss: 0.7259 | Spent: 8.5 secs | LR: 0.000175
INFO:tensorflow:Step 1900 | Loss: 0.6737 | Spent: 8.3 secs | LR: 0.000179
INFO:tensorflow:Step 2000 | Loss: 0.6422 | Spent: 8.5 secs | LR: 0.000183
INFO:tensorflow:Step 2100 | Loss: 0.7076 | Spent: 8.4 secs | LR: 0.000188
INFO:tensorflow:Step 2200 | Loss: 0.5964 | Spent: 8.2 secs | LR: 0.000192
INFO:tensorflow:Step 2300 | Loss: 0.7417 | Spent: 8.3 secs | LR: 0.000196
INFO:tensorflow:Step 2400 | Loss: 0.6189 | Spent: 8.6 secs | LR: 0.000200
INFO:tensorflow:Step 2500 | Loss: 0.5904 | Spent: 8.2 secs | LR: 0.000204
INFO:tensorflow:Step 2600 | Loss: 0.6248 | Spent: 8.5 secs | LR: 0.000208
INFO:tensorflow:Step 2700 | Loss: 0.7177 | Spent: 8.2 secs | LR: 0.000213
INFO:tensorflow:Step 2800 | Loss: 0.7828 | Spent: 8.3 secs | LR: 0.000217
INFO:tensorflow:Step 2900 | Loss: 0.8619 | Spent: 8.6 secs | LR: 0.000221
INFO:tensorflow:Step 3000 | Loss: 0.5027 | Spent: 8.1 secs | LR: 0.000225
INFO:tensorflow:Step 3100 | Loss: 0.4694 | Spent: 8.3 secs | LR: 0.000229
INFO:tensorflow:Step 3200 | Loss: 0.6534 | Spent: 8.2 secs | LR: 0.000233
INFO:tensorflow:Step 3300 | Loss: 0.6593 | Spent: 8.4 secs | LR: 0.000238
INFO:tensorflow:Step 3400 | Loss: 0.5380 | Spent: 8.1 secs | LR: 0.000242
INFO:tensorflow:Step 3500 | Loss: 0.4883 | Spent: 8.5 secs | LR: 0.000246
INFO:tensorflow:Step 3600 | Loss: 0.6518 | Spent: 8.6 secs | LR: 0.000250
INFO:tensorflow:Step 3700 | Loss: 0.5598 | Spent: 8.1 secs | LR: 0.000254
INFO:tensorflow:Step 3800 | Loss: 0.6648 | Spent: 8.3 secs | LR: 0.000258
INFO:tensorflow:Step 3900 | Loss: 0.4274 | Spent: 8.3 secs | LR: 0.000263
INFO:tensorflow:Step 4000 | Loss: 0.6435 | Spent: 8.2 secs | LR: 0.000267
INFO:tensorflow:Step 4100 | Loss: 0.8271 | Spent: 8.6 secs | LR: 0.000271
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.780
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.784     0.773     0.779      5000
     Matched      0.776     0.787     0.782      5000

    accuracy                          0.780     10000
   macro avg      0.780     0.780     0.780     10000
weighted avg      0.780     0.780     0.780     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.646
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.748     0.734     0.741      2978
     Matched      0.431     0.448     0.440      1338

    accuracy                          0.646      4316
   macro avg      0.590     0.591     0.590      4316
weighted avg      0.650     0.646     0.648      4316

INFO:tensorflow:Best | Accuracy 1: 0.780 | Accuracy 2: 0.646
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 4200 | Loss: 0.5550 | Spent: 48.3 secs | LR: 0.000275
INFO:tensorflow:Step 4300 | Loss: 0.5439 | Spent: 8.4 secs | LR: 0.000279
INFO:tensorflow:Step 4400 | Loss: 0.5572 | Spent: 8.3 secs | LR: 0.000283
INFO:tensorflow:Step 4500 | Loss: 1.0410 | Spent: 8.4 secs | LR: 0.000288
INFO:tensorflow:Step 4600 | Loss: 0.5223 | Spent: 8.3 secs | LR: 0.000292
INFO:tensorflow:Step 4700 | Loss: 1.0905 | Spent: 8.2 secs | LR: 0.000296
INFO:tensorflow:Step 4800 | Loss: 0.5872 | Spent: 8.1 secs | LR: 0.000300
INFO:tensorflow:Step 4900 | Loss: 0.5609 | Spent: 8.1 secs | LR: 0.000304
INFO:tensorflow:Step 5000 | Loss: 0.4964 | Spent: 8.5 secs | LR: 0.000308
INFO:tensorflow:Step 5100 | Loss: 0.7382 | Spent: 7.9 secs | LR: 0.000313
INFO:tensorflow:Step 5200 | Loss: 0.5061 | Spent: 8.3 secs | LR: 0.000317
INFO:tensorflow:Step 5300 | Loss: 0.6043 | Spent: 8.6 secs | LR: 0.000321
INFO:tensorflow:Step 5400 | Loss: 0.7125 | Spent: 8.5 secs | LR: 0.000325
INFO:tensorflow:Step 5500 | Loss: 0.7409 | Spent: 8.5 secs | LR: 0.000329
INFO:tensorflow:Step 5600 | Loss: 0.5861 | Spent: 8.1 secs | LR: 0.000333
INFO:tensorflow:Step 5700 | Loss: 0.7855 | Spent: 8.4 secs | LR: 0.000338
INFO:tensorflow:Step 5800 | Loss: 0.6032 | Spent: 8.6 secs | LR: 0.000342
INFO:tensorflow:Step 5900 | Loss: 1.0858 | Spent: 8.5 secs | LR: 0.000346
INFO:tensorflow:Step 6000 | Loss: 0.4404 | Spent: 8.1 secs | LR: 0.000350
INFO:tensorflow:Step 6100 | Loss: 0.5031 | Spent: 8.4 secs | LR: 0.000354
INFO:tensorflow:Step 6200 | Loss: 0.6237 | Spent: 8.9 secs | LR: 0.000358
INFO:tensorflow:Step 6300 | Loss: 0.9813 | Spent: 8.1 secs | LR: 0.000363
INFO:tensorflow:Step 6400 | Loss: 0.6793 | Spent: 8.7 secs | LR: 0.000367
INFO:tensorflow:Step 6500 | Loss: 0.6542 | Spent: 8.3 secs | LR: 0.000371
INFO:tensorflow:Step 6600 | Loss: 0.5738 | Spent: 8.2 secs | LR: 0.000375
INFO:tensorflow:Step 6700 | Loss: 0.4105 | Spent: 8.6 secs | LR: 0.000379
INFO:tensorflow:Step 6800 | Loss: 0.6714 | Spent: 8.2 secs | LR: 0.000383
INFO:tensorflow:Step 6900 | Loss: 0.6458 | Spent: 8.3 secs | LR: 0.000388
INFO:tensorflow:Step 7000 | Loss: 0.5768 | Spent: 8.6 secs | LR: 0.000392
INFO:tensorflow:Step 7100 | Loss: 0.6716 | Spent: 8.3 secs | LR: 0.000396
INFO:tensorflow:Step 7200 | Loss: 0.6743 | Spent: 8.4 secs | LR: 0.000400
INFO:tensorflow:Step 7300 | Loss: 0.5433 | Spent: 8.2 secs | LR: 0.000404
INFO:tensorflow:Step 7400 | Loss: 0.4056 | Spent: 8.6 secs | LR: 0.000408
INFO:tensorflow:Step 7500 | Loss: 0.5671 | Spent: 8.4 secs | LR: 0.000413
INFO:tensorflow:Step 7600 | Loss: 0.5116 | Spent: 8.4 secs | LR: 0.000417
INFO:tensorflow:Step 7700 | Loss: 0.8336 | Spent: 8.5 secs | LR: 0.000421
INFO:tensorflow:Step 7800 | Loss: 0.5184 | Spent: 8.5 secs | LR: 0.000425
INFO:tensorflow:Step 7900 | Loss: 0.7729 | Spent: 8.9 secs | LR: 0.000429
INFO:tensorflow:Step 8000 | Loss: 0.5395 | Spent: 8.6 secs | LR: 0.000434
INFO:tensorflow:Step 8100 | Loss: 0.6266 | Spent: 8.3 secs | LR: 0.000438
INFO:tensorflow:Step 8200 | Loss: 0.5390 | Spent: 8.4 secs | LR: 0.000442
INFO:tensorflow:Step 8300 | Loss: 0.5570 | Spent: 8.3 secs | LR: 0.000446
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.797
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.852     0.720     0.780      5000
     Matched      0.757     0.875     0.812      5000

    accuracy                          0.797     10000
   macro avg      0.805     0.797     0.796     10000
weighted avg      0.805     0.797     0.796     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.668
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.753     0.773     0.763      2978
     Matched      0.462     0.435     0.448      1338

    accuracy                          0.668      4316
   macro avg      0.607     0.604     0.605      4316
weighted avg      0.663     0.668     0.665      4316

INFO:tensorflow:Best | Accuracy 1: 0.797 | Accuracy 2: 0.668
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 8400 | Loss: 0.5821 | Spent: 48.0 secs | LR: 0.000450
INFO:tensorflow:Step 8500 | Loss: 0.4491 | Spent: 8.2 secs | LR: 0.000454
INFO:tensorflow:Step 8600 | Loss: 0.7757 | Spent: 8.1 secs | LR: 0.000459
INFO:tensorflow:Step 8700 | Loss: 0.6243 | Spent: 8.4 secs | LR: 0.000463
INFO:tensorflow:Step 8800 | Loss: 0.4444 | Spent: 7.9 secs | LR: 0.000467
INFO:tensorflow:Step 8900 | Loss: 0.5876 | Spent: 8.4 secs | LR: 0.000471
INFO:tensorflow:Step 9000 | Loss: 0.4446 | Spent: 8.9 secs | LR: 0.000475
INFO:tensorflow:Step 9100 | Loss: 0.6946 | Spent: 8.4 secs | LR: 0.000479
INFO:tensorflow:Step 9200 | Loss: 0.5270 | Spent: 8.5 secs | LR: 0.000484
INFO:tensorflow:Step 9300 | Loss: 0.8508 | Spent: 8.4 secs | LR: 0.000488
INFO:tensorflow:Step 9400 | Loss: 0.5154 | Spent: 8.7 secs | LR: 0.000492
INFO:tensorflow:Step 9500 | Loss: 0.8187 | Spent: 8.2 secs | LR: 0.000496
INFO:tensorflow:Step 9600 | Loss: 0.5784 | Spent: 8.3 secs | LR: 0.000500
INFO:tensorflow:Step 9700 | Loss: 0.8458 | Spent: 8.5 secs | LR: 0.000504
INFO:tensorflow:Step 9800 | Loss: 0.8509 | Spent: 8.5 secs | LR: 0.000509
INFO:tensorflow:Step 9900 | Loss: 0.6479 | Spent: 8.2 secs | LR: 0.000513
INFO:tensorflow:Step 10000 | Loss: 0.4801 | Spent: 8.5 secs | LR: 0.000517
INFO:tensorflow:Step 10100 | Loss: 0.5577 | Spent: 8.6 secs | LR: 0.000521
INFO:tensorflow:Step 10200 | Loss: 0.4429 | Spent: 8.5 secs | LR: 0.000525
INFO:tensorflow:Step 10300 | Loss: 0.8826 | Spent: 8.5 secs | LR: 0.000529
INFO:tensorflow:Step 10400 | Loss: 0.4716 | Spent: 8.5 secs | LR: 0.000534
INFO:tensorflow:Step 10500 | Loss: 0.4390 | Spent: 8.6 secs | LR: 0.000538
INFO:tensorflow:Step 10600 | Loss: 0.5482 | Spent: 8.5 secs | LR: 0.000542
INFO:tensorflow:Step 10700 | Loss: 0.6516 | Spent: 8.2 secs | LR: 0.000546
INFO:tensorflow:Step 10800 | Loss: 0.6740 | Spent: 8.2 secs | LR: 0.000550
INFO:tensorflow:Step 10900 | Loss: 0.6174 | Spent: 8.5 secs | LR: 0.000554
INFO:tensorflow:Step 11000 | Loss: 0.5534 | Spent: 8.6 secs | LR: 0.000559
INFO:tensorflow:Step 11100 | Loss: 0.6344 | Spent: 8.5 secs | LR: 0.000563
INFO:tensorflow:Step 11200 | Loss: 0.6817 | Spent: 8.6 secs | LR: 0.000567
INFO:tensorflow:Step 11300 | Loss: 0.4909 | Spent: 8.4 secs | LR: 0.000571
INFO:tensorflow:Step 11400 | Loss: 0.5592 | Spent: 8.8 secs | LR: 0.000575
INFO:tensorflow:Step 11500 | Loss: 0.8342 | Spent: 8.8 secs | LR: 0.000579
INFO:tensorflow:Step 11600 | Loss: 0.4312 | Spent: 9.0 secs | LR: 0.000584
INFO:tensorflow:Step 11700 | Loss: 0.7580 | Spent: 8.4 secs | LR: 0.000588
INFO:tensorflow:Step 11800 | Loss: 0.5068 | Spent: 8.4 secs | LR: 0.000592
INFO:tensorflow:Step 11900 | Loss: 0.4851 | Spent: 8.5 secs | LR: 0.000596
INFO:tensorflow:Step 12000 | Loss: 0.4424 | Spent: 8.7 secs | LR: 0.000600
INFO:tensorflow:Step 12100 | Loss: 0.4811 | Spent: 8.2 secs | LR: 0.000604
INFO:tensorflow:Step 12200 | Loss: 0.6085 | Spent: 8.5 secs | LR: 0.000609
INFO:tensorflow:Step 12300 | Loss: 0.5551 | Spent: 8.3 secs | LR: 0.000613
INFO:tensorflow:Step 12400 | Loss: 0.5383 | Spent: 8.5 secs | LR: 0.000617
INFO:tensorflow:Step 12500 | Loss: 0.8271 | Spent: 8.8 secs | LR: 0.000621
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.806
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.833     0.765     0.798      5000
     Matched      0.783     0.847     0.814      5000

    accuracy                          0.806     10000
   macro avg      0.808     0.806     0.806     10000
weighted avg      0.808     0.806     0.806     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.693
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.742     0.850     0.792      2978
     Matched      0.506     0.343     0.409      1338

    accuracy                          0.693      4316
   macro avg      0.624     0.596     0.601      4316
weighted avg      0.669     0.693     0.673      4316

INFO:tensorflow:Best | Accuracy 1: 0.806 | Accuracy 2: 0.693
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 12600 | Loss: 0.4964 | Spent: 49.5 secs | LR: 0.000625
INFO:tensorflow:Step 12700 | Loss: 0.5281 | Spent: 8.4 secs | LR: 0.000629
INFO:tensorflow:Step 12800 | Loss: 0.4769 | Spent: 8.3 secs | LR: 0.000634
INFO:tensorflow:Step 12900 | Loss: 0.4572 | Spent: 8.4 secs | LR: 0.000638
INFO:tensorflow:Step 13000 | Loss: 0.6888 | Spent: 8.9 secs | LR: 0.000642
INFO:tensorflow:Step 13100 | Loss: 0.4948 | Spent: 8.4 secs | LR: 0.000646
INFO:tensorflow:Step 13200 | Loss: 0.4233 | Spent: 8.5 secs | LR: 0.000650
INFO:tensorflow:Step 13300 | Loss: 0.6218 | Spent: 8.8 secs | LR: 0.000654
INFO:tensorflow:Step 13400 | Loss: 0.8746 | Spent: 8.4 secs | LR: 0.000659
INFO:tensorflow:Step 13500 | Loss: 0.3612 | Spent: 8.5 secs | LR: 0.000663
INFO:tensorflow:Step 13600 | Loss: 0.5114 | Spent: 8.5 secs | LR: 0.000667
INFO:tensorflow:Step 13700 | Loss: 0.5737 | Spent: 8.7 secs | LR: 0.000671
INFO:tensorflow:Step 13800 | Loss: 0.4241 | Spent: 8.3 secs | LR: 0.000675
INFO:tensorflow:Step 13900 | Loss: 0.6984 | Spent: 8.6 secs | LR: 0.000679
INFO:tensorflow:Step 14000 | Loss: 0.5676 | Spent: 8.6 secs | LR: 0.000684
INFO:tensorflow:Step 14100 | Loss: 0.4659 | Spent: 8.5 secs | LR: 0.000688
INFO:tensorflow:Step 14200 | Loss: 0.5353 | Spent: 8.4 secs | LR: 0.000692
INFO:tensorflow:Step 14300 | Loss: 0.4702 | Spent: 8.3 secs | LR: 0.000696
INFO:tensorflow:Step 14400 | Loss: 1.2186 | Spent: 8.4 secs | LR: 0.000700
INFO:tensorflow:Step 14500 | Loss: 0.7813 | Spent: 8.6 secs | LR: 0.000704
INFO:tensorflow:Step 14600 | Loss: 0.4771 | Spent: 8.5 secs | LR: 0.000709
INFO:tensorflow:Step 14700 | Loss: 0.4869 | Spent: 8.7 secs | LR: 0.000713
INFO:tensorflow:Step 14800 | Loss: 0.4729 | Spent: 8.6 secs | LR: 0.000717
INFO:tensorflow:Step 14900 | Loss: 0.5821 | Spent: 8.7 secs | LR: 0.000721
INFO:tensorflow:Step 15000 | Loss: 0.4624 | Spent: 8.5 secs | LR: 0.000725
INFO:tensorflow:Step 15100 | Loss: 0.4880 | Spent: 8.5 secs | LR: 0.000730
INFO:tensorflow:Step 15200 | Loss: 0.4912 | Spent: 8.6 secs | LR: 0.000734
INFO:tensorflow:Step 15300 | Loss: 0.7401 | Spent: 8.5 secs | LR: 0.000738
INFO:tensorflow:Step 15400 | Loss: 0.8177 | Spent: 8.9 secs | LR: 0.000742
INFO:tensorflow:Step 15500 | Loss: 0.4946 | Spent: 8.5 secs | LR: 0.000746
INFO:tensorflow:Step 15600 | Loss: 0.7308 | Spent: 8.5 secs | LR: 0.000750
INFO:tensorflow:Step 15700 | Loss: 0.9006 | Spent: 8.4 secs | LR: 0.000755
INFO:tensorflow:Step 15800 | Loss: 0.4279 | Spent: 8.8 secs | LR: 0.000759
INFO:tensorflow:Step 15900 | Loss: 0.5409 | Spent: 8.2 secs | LR: 0.000763
INFO:tensorflow:Step 16000 | Loss: 0.8008 | Spent: 8.5 secs | LR: 0.000767
INFO:tensorflow:Step 16100 | Loss: 0.6328 | Spent: 8.4 secs | LR: 0.000771
INFO:tensorflow:Step 16200 | Loss: 0.5268 | Spent: 9.1 secs | LR: 0.000775
INFO:tensorflow:Step 16300 | Loss: 0.4615 | Spent: 8.8 secs | LR: 0.000780
INFO:tensorflow:Step 16400 | Loss: 0.4915 | Spent: 8.6 secs | LR: 0.000784
INFO:tensorflow:Step 16500 | Loss: 0.5860 | Spent: 8.4 secs | LR: 0.000788
INFO:tensorflow:Step 16600 | Loss: 0.5130 | Spent: 8.6 secs | LR: 0.000792
INFO:tensorflow:Step 16700 | Loss: 0.3926 | Spent: 8.6 secs | LR: 0.000796
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.809
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.853     0.746     0.796      5000
     Matched      0.774     0.872     0.820      5000

    accuracy                          0.809     10000
   macro avg      0.814     0.809     0.808     10000
weighted avg      0.814     0.809     0.808     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.680
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.774     0.757     0.765      2978
     Matched      0.484     0.507     0.495      1338

    accuracy                          0.680      4316
   macro avg      0.629     0.632     0.630      4316
weighted avg      0.684     0.680     0.682      4316

INFO:tensorflow:Best | Accuracy 1: 0.806 | Accuracy 2: 0.693
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 16800 | Loss: 0.5521 | Spent: 50.0 secs | LR: 0.000800
INFO:tensorflow:Step 16900 | Loss: 0.6193 | Spent: 8.4 secs | LR: 0.000795
INFO:tensorflow:Step 17000 | Loss: 0.4926 | Spent: 8.9 secs | LR: 0.000791
INFO:tensorflow:Step 17100 | Loss: 0.4834 | Spent: 8.7 secs | LR: 0.000787
INFO:tensorflow:Step 17200 | Loss: 0.6975 | Spent: 9.0 secs | LR: 0.000783
INFO:tensorflow:Step 17300 | Loss: 0.8526 | Spent: 8.8 secs | LR: 0.000779
INFO:tensorflow:Step 17400 | Loss: 0.6464 | Spent: 9.0 secs | LR: 0.000775
INFO:tensorflow:Step 17500 | Loss: 0.5199 | Spent: 8.0 secs | LR: 0.000770
INFO:tensorflow:Step 17600 | Loss: 0.5340 | Spent: 8.6 secs | LR: 0.000766
INFO:tensorflow:Step 17700 | Loss: 0.5278 | Spent: 8.4 secs | LR: 0.000762
INFO:tensorflow:Step 17800 | Loss: 0.3931 | Spent: 8.7 secs | LR: 0.000758
INFO:tensorflow:Step 17900 | Loss: 0.3995 | Spent: 8.3 secs | LR: 0.000754
INFO:tensorflow:Step 18000 | Loss: 0.6934 | Spent: 8.8 secs | LR: 0.000750
INFO:tensorflow:Step 18100 | Loss: 0.5486 | Spent: 8.5 secs | LR: 0.000745
INFO:tensorflow:Step 18200 | Loss: 0.4788 | Spent: 8.5 secs | LR: 0.000741
INFO:tensorflow:Step 18300 | Loss: 0.7981 | Spent: 8.6 secs | LR: 0.000737
INFO:tensorflow:Step 18400 | Loss: 0.5893 | Spent: 8.5 secs | LR: 0.000733
INFO:tensorflow:Step 18500 | Loss: 0.9413 | Spent: 9.0 secs | LR: 0.000729
INFO:tensorflow:Step 18600 | Loss: 0.5449 | Spent: 8.5 secs | LR: 0.000725
INFO:tensorflow:Step 18700 | Loss: 0.4888 | Spent: 8.5 secs | LR: 0.000720
INFO:tensorflow:Step 18800 | Loss: 0.6496 | Spent: 8.5 secs | LR: 0.000716
INFO:tensorflow:Step 18900 | Loss: 0.6167 | Spent: 8.9 secs | LR: 0.000712
INFO:tensorflow:Step 19000 | Loss: 0.5427 | Spent: 8.5 secs | LR: 0.000708
INFO:tensorflow:Step 19100 | Loss: 0.5156 | Spent: 8.7 secs | LR: 0.000704
INFO:tensorflow:Step 19200 | Loss: 0.5730 | Spent: 8.6 secs | LR: 0.000700
INFO:tensorflow:Step 19300 | Loss: 0.4511 | Spent: 8.5 secs | LR: 0.000695
INFO:tensorflow:Step 19400 | Loss: 0.5007 | Spent: 8.5 secs | LR: 0.000691
INFO:tensorflow:Step 19500 | Loss: 0.4582 | Spent: 8.9 secs | LR: 0.000687
INFO:tensorflow:Step 19600 | Loss: 0.3753 | Spent: 8.6 secs | LR: 0.000683
INFO:tensorflow:Step 19700 | Loss: 0.5493 | Spent: 8.8 secs | LR: 0.000679
INFO:tensorflow:Step 19800 | Loss: 0.5315 | Spent: 8.7 secs | LR: 0.000675
INFO:tensorflow:Step 19900 | Loss: 0.6275 | Spent: 8.4 secs | LR: 0.000670
INFO:tensorflow:Step 20000 | Loss: 0.7017 | Spent: 8.6 secs | LR: 0.000666
INFO:tensorflow:Step 20100 | Loss: 0.5960 | Spent: 8.9 secs | LR: 0.000662
INFO:tensorflow:Step 20200 | Loss: 0.5757 | Spent: 8.6 secs | LR: 0.000658
INFO:tensorflow:Step 20300 | Loss: 0.5971 | Spent: 8.5 secs | LR: 0.000654
INFO:tensorflow:Step 20400 | Loss: 0.5601 | Spent: 8.6 secs | LR: 0.000650
INFO:tensorflow:Step 20500 | Loss: 0.9194 | Spent: 8.6 secs | LR: 0.000645
INFO:tensorflow:Step 20600 | Loss: 0.4323 | Spent: 8.6 secs | LR: 0.000641
INFO:tensorflow:Step 20700 | Loss: 0.5755 | Spent: 8.7 secs | LR: 0.000637
INFO:tensorflow:Step 20800 | Loss: 0.4813 | Spent: 8.5 secs | LR: 0.000633
INFO:tensorflow:Step 20900 | Loss: 0.4599 | Spent: 8.7 secs | LR: 0.000629
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.821
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.811     0.838     0.824      5000
     Matched      0.832     0.805     0.818      5000

    accuracy                          0.821     10000
   macro avg      0.822     0.821     0.821     10000
weighted avg      0.822     0.821     0.821     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.687
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.774     0.773     0.773      2978
     Matched      0.496     0.497     0.496      1338

    accuracy                          0.687      4316
   macro avg      0.635     0.635     0.635      4316
weighted avg      0.688     0.687     0.688      4316

INFO:tensorflow:Best | Accuracy 1: 0.821 | Accuracy 2: 0.687
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 21000 | Loss: 0.4533 | Spent: 50.9 secs | LR: 0.000625
INFO:tensorflow:Step 21100 | Loss: 0.5140 | Spent: 8.4 secs | LR: 0.000620
INFO:tensorflow:Step 21200 | Loss: 0.5355 | Spent: 8.5 secs | LR: 0.000616
INFO:tensorflow:Step 21300 | Loss: 0.4148 | Spent: 8.7 secs | LR: 0.000612
INFO:tensorflow:Step 21400 | Loss: 0.6374 | Spent: 8.8 secs | LR: 0.000608
INFO:tensorflow:Step 21500 | Loss: 0.6356 | Spent: 8.5 secs | LR: 0.000604
INFO:tensorflow:Step 21600 | Loss: 0.4477 | Spent: 8.8 secs | LR: 0.000600
INFO:tensorflow:Step 21700 | Loss: 0.6764 | Spent: 8.8 secs | LR: 0.000595
INFO:tensorflow:Step 21800 | Loss: 0.6371 | Spent: 9.1 secs | LR: 0.000591
INFO:tensorflow:Step 21900 | Loss: 0.4062 | Spent: 8.5 secs | LR: 0.000587
INFO:tensorflow:Step 22000 | Loss: 0.5105 | Spent: 8.7 secs | LR: 0.000583
INFO:tensorflow:Step 22100 | Loss: 0.6192 | Spent: 8.4 secs | LR: 0.000579
INFO:tensorflow:Step 22200 | Loss: 0.5143 | Spent: 8.8 secs | LR: 0.000575
INFO:tensorflow:Step 22300 | Loss: 0.4663 | Spent: 8.6 secs | LR: 0.000570
INFO:tensorflow:Step 22400 | Loss: 0.6020 | Spent: 8.6 secs | LR: 0.000566
INFO:tensorflow:Step 22500 | Loss: 0.4552 | Spent: 8.7 secs | LR: 0.000562
INFO:tensorflow:Step 22600 | Loss: 0.4307 | Spent: 8.9 secs | LR: 0.000558
INFO:tensorflow:Step 22700 | Loss: 0.5867 | Spent: 8.5 secs | LR: 0.000554
INFO:tensorflow:Step 22800 | Loss: 0.3825 | Spent: 8.5 secs | LR: 0.000549
INFO:tensorflow:Step 22900 | Loss: 0.5729 | Spent: 8.7 secs | LR: 0.000545
INFO:tensorflow:Step 23000 | Loss: 0.4622 | Spent: 8.9 secs | LR: 0.000541
INFO:tensorflow:Step 23100 | Loss: 0.4876 | Spent: 8.5 secs | LR: 0.000537
INFO:tensorflow:Step 23200 | Loss: 0.5470 | Spent: 8.6 secs | LR: 0.000533
INFO:tensorflow:Step 23300 | Loss: 0.5460 | Spent: 8.5 secs | LR: 0.000529
INFO:tensorflow:Step 23400 | Loss: 0.6640 | Spent: 8.8 secs | LR: 0.000524
INFO:tensorflow:Step 23500 | Loss: 1.0004 | Spent: 8.2 secs | LR: 0.000520
INFO:tensorflow:Step 23600 | Loss: 0.5658 | Spent: 8.6 secs | LR: 0.000516
INFO:tensorflow:Step 23700 | Loss: 0.4877 | Spent: 8.7 secs | LR: 0.000512
INFO:tensorflow:Step 23800 | Loss: 0.5123 | Spent: 8.5 secs | LR: 0.000508
INFO:tensorflow:Step 23900 | Loss: 0.3970 | Spent: 8.6 secs | LR: 0.000504
INFO:tensorflow:Step 24000 | Loss: 0.5035 | Spent: 8.6 secs | LR: 0.000499
INFO:tensorflow:Step 24100 | Loss: 0.4869 | Spent: 8.6 secs | LR: 0.000495
INFO:tensorflow:Step 24200 | Loss: 0.5434 | Spent: 8.6 secs | LR: 0.000491
INFO:tensorflow:Step 24300 | Loss: 0.3469 | Spent: 8.5 secs | LR: 0.000487
INFO:tensorflow:Step 24400 | Loss: 0.5437 | Spent: 8.5 secs | LR: 0.000483
INFO:tensorflow:Step 24500 | Loss: 0.3637 | Spent: 8.6 secs | LR: 0.000479
INFO:tensorflow:Step 24600 | Loss: 0.4871 | Spent: 8.5 secs | LR: 0.000474
INFO:tensorflow:Step 24700 | Loss: 0.4881 | Spent: 8.8 secs | LR: 0.000470
INFO:tensorflow:Step 24800 | Loss: 0.4929 | Spent: 8.9 secs | LR: 0.000466
INFO:tensorflow:Step 24900 | Loss: 0.5231 | Spent: 8.8 secs | LR: 0.000462
INFO:tensorflow:Step 25000 | Loss: 0.4425 | Spent: 8.6 secs | LR: 0.000458
INFO:tensorflow:Step 25100 | Loss: 0.5524 | Spent: 8.5 secs | LR: 0.000454
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.817
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.799     0.848     0.823      5000
     Matched      0.838     0.787     0.812      5000

    accuracy                          0.818     10000
   macro avg      0.819     0.818     0.817     10000
weighted avg      0.819     0.818     0.817     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.703
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.740     0.878     0.803      2978
     Matched      0.535     0.312     0.395      1338

    accuracy                          0.703      4316
   macro avg      0.637     0.595     0.599      4316
weighted avg      0.676     0.703     0.676      4316

INFO:tensorflow:Best | Accuracy 1: 0.817 | Accuracy 2: 0.703
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 25200 | Loss: 0.4562 | Spent: 50.5 secs | LR: 0.000449
INFO:tensorflow:Step 25300 | Loss: 0.4302 | Spent: 8.6 secs | LR: 0.000445
INFO:tensorflow:Step 25400 | Loss: 0.5459 | Spent: 8.8 secs | LR: 0.000441
INFO:tensorflow:Step 25500 | Loss: 0.5643 | Spent: 8.9 secs | LR: 0.000437
INFO:tensorflow:Step 25600 | Loss: 0.6048 | Spent: 9.0 secs | LR: 0.000433
INFO:tensorflow:Step 25700 | Loss: 0.5624 | Spent: 8.5 secs | LR: 0.000429
INFO:tensorflow:Step 25800 | Loss: 0.6431 | Spent: 8.5 secs | LR: 0.000424
INFO:tensorflow:Step 25900 | Loss: 0.4955 | Spent: 8.7 secs | LR: 0.000420
INFO:tensorflow:Step 26000 | Loss: 0.3925 | Spent: 8.5 secs | LR: 0.000416
INFO:tensorflow:Step 26100 | Loss: 0.6872 | Spent: 8.7 secs | LR: 0.000412
INFO:tensorflow:Step 26200 | Loss: 0.4162 | Spent: 8.1 secs | LR: 0.000408
INFO:tensorflow:Step 26300 | Loss: 0.6045 | Spent: 8.6 secs | LR: 0.000404
INFO:tensorflow:Step 26400 | Loss: 0.5733 | Spent: 9.0 secs | LR: 0.000399
INFO:tensorflow:Step 26500 | Loss: 0.5837 | Spent: 8.8 secs | LR: 0.000395
INFO:tensorflow:Step 26600 | Loss: 0.4557 | Spent: 8.4 secs | LR: 0.000391
INFO:tensorflow:Step 26700 | Loss: 0.4978 | Spent: 8.4 secs | LR: 0.000387
INFO:tensorflow:Step 26800 | Loss: 0.4387 | Spent: 8.6 secs | LR: 0.000383
INFO:tensorflow:Step 26900 | Loss: 0.4350 | Spent: 8.3 secs | LR: 0.000379
INFO:tensorflow:Step 27000 | Loss: 0.6335 | Spent: 8.5 secs | LR: 0.000374
INFO:tensorflow:Step 27100 | Loss: 0.3209 | Spent: 8.6 secs | LR: 0.000370
INFO:tensorflow:Step 27200 | Loss: 0.6310 | Spent: 8.6 secs | LR: 0.000366
INFO:tensorflow:Step 27300 | Loss: 0.5522 | Spent: 8.9 secs | LR: 0.000362
INFO:tensorflow:Step 27400 | Loss: 0.5984 | Spent: 8.6 secs | LR: 0.000358
INFO:tensorflow:Step 27500 | Loss: 0.5218 | Spent: 8.7 secs | LR: 0.000354
INFO:tensorflow:Step 27600 | Loss: 0.4807 | Spent: 8.8 secs | LR: 0.000349
INFO:tensorflow:Step 27700 | Loss: 0.3072 | Spent: 8.5 secs | LR: 0.000345
INFO:tensorflow:Step 27800 | Loss: 0.4290 | Spent: 8.4 secs | LR: 0.000341
INFO:tensorflow:Step 27900 | Loss: 0.3859 | Spent: 8.5 secs | LR: 0.000337
INFO:tensorflow:Step 28000 | Loss: 0.5753 | Spent: 8.8 secs | LR: 0.000333
INFO:tensorflow:Step 28100 | Loss: 1.3822 | Spent: 8.5 secs | LR: 0.000329
INFO:tensorflow:Step 28200 | Loss: 0.4405 | Spent: 8.4 secs | LR: 0.000324
INFO:tensorflow:Step 28300 | Loss: 0.3807 | Spent: 8.5 secs | LR: 0.000320
INFO:tensorflow:Step 28400 | Loss: 0.4015 | Spent: 8.7 secs | LR: 0.000316
INFO:tensorflow:Step 28500 | Loss: 0.6123 | Spent: 8.8 secs | LR: 0.000312
INFO:tensorflow:Step 28600 | Loss: 0.5833 | Spent: 8.8 secs | LR: 0.000308
INFO:tensorflow:Step 28700 | Loss: 0.4950 | Spent: 8.7 secs | LR: 0.000304
INFO:tensorflow:Step 28800 | Loss: 0.5128 | Spent: 8.7 secs | LR: 0.000299
INFO:tensorflow:Step 28900 | Loss: 0.5040 | Spent: 8.7 secs | LR: 0.000295
INFO:tensorflow:Step 29000 | Loss: 0.4869 | Spent: 8.8 secs | LR: 0.000291
INFO:tensorflow:Step 29100 | Loss: 0.5216 | Spent: 8.6 secs | LR: 0.000287
INFO:tensorflow:Step 29200 | Loss: 0.4227 | Spent: 8.9 secs | LR: 0.000283
INFO:tensorflow:Step 29300 | Loss: 0.4471 | Spent: 9.2 secs | LR: 0.000279
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.823
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.818     0.831     0.825      5000
     Matched      0.828     0.815     0.822      5000

    accuracy                          0.823     10000
   macro avg      0.823     0.823     0.823     10000
weighted avg      0.823     0.823     0.823     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.698
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.786     0.772     0.779      2978
     Matched      0.512     0.534     0.523      1338

    accuracy                          0.698      4316
   macro avg      0.649     0.653     0.651      4316
weighted avg      0.701     0.698     0.700      4316

INFO:tensorflow:Best | Accuracy 1: 0.823 | Accuracy 2: 0.698
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 29400 | Loss: 0.4269 | Spent: 50.2 secs | LR: 0.000274
INFO:tensorflow:Step 29500 | Loss: 0.5302 | Spent: 8.7 secs | LR: 0.000270
INFO:tensorflow:Step 29600 | Loss: 0.3816 | Spent: 8.7 secs | LR: 0.000266
INFO:tensorflow:Step 29700 | Loss: 0.3814 | Spent: 8.8 secs | LR: 0.000262
INFO:tensorflow:Step 29800 | Loss: 0.3858 | Spent: 8.5 secs | LR: 0.000258
INFO:tensorflow:Step 29900 | Loss: 0.5786 | Spent: 8.9 secs | LR: 0.000253
INFO:tensorflow:Step 30000 | Loss: 0.5247 | Spent: 8.6 secs | LR: 0.000249
INFO:tensorflow:Step 30100 | Loss: 0.5366 | Spent: 8.9 secs | LR: 0.000245
INFO:tensorflow:Step 30200 | Loss: 0.4456 | Spent: 8.5 secs | LR: 0.000241
INFO:tensorflow:Step 30300 | Loss: 0.5381 | Spent: 8.6 secs | LR: 0.000237
INFO:tensorflow:Step 30400 | Loss: 0.3564 | Spent: 8.7 secs | LR: 0.000233
INFO:tensorflow:Step 30500 | Loss: 0.4514 | Spent: 8.5 secs | LR: 0.000228
INFO:tensorflow:Step 30600 | Loss: 0.5661 | Spent: 8.7 secs | LR: 0.000224
INFO:tensorflow:Step 30700 | Loss: 0.5209 | Spent: 8.6 secs | LR: 0.000220
INFO:tensorflow:Step 30800 | Loss: 0.6425 | Spent: 8.7 secs | LR: 0.000216
INFO:tensorflow:Step 30900 | Loss: 0.4099 | Spent: 8.6 secs | LR: 0.000212
INFO:tensorflow:Step 31000 | Loss: 0.3196 | Spent: 8.7 secs | LR: 0.000208
INFO:tensorflow:Step 31100 | Loss: 0.5016 | Spent: 8.7 secs | LR: 0.000203
INFO:tensorflow:Step 31200 | Loss: 0.4488 | Spent: 8.5 secs | LR: 0.000199
INFO:tensorflow:Step 31300 | Loss: 0.5327 | Spent: 8.4 secs | LR: 0.000195
INFO:tensorflow:Step 31400 | Loss: 1.1267 | Spent: 8.5 secs | LR: 0.000191
INFO:tensorflow:Step 31500 | Loss: 0.4407 | Spent: 8.6 secs | LR: 0.000187
INFO:tensorflow:Step 31600 | Loss: 0.3859 | Spent: 9.0 secs | LR: 0.000183
INFO:tensorflow:Step 31700 | Loss: 0.3573 | Spent: 8.6 secs | LR: 0.000178
INFO:tensorflow:Step 31800 | Loss: 0.5018 | Spent: 8.4 secs | LR: 0.000174
INFO:tensorflow:Step 31900 | Loss: 0.4486 | Spent: 8.9 secs | LR: 0.000170
INFO:tensorflow:Step 32000 | Loss: 0.4985 | Spent: 8.6 secs | LR: 0.000166
INFO:tensorflow:Step 32100 | Loss: 0.5127 | Spent: 8.7 secs | LR: 0.000162
INFO:tensorflow:Step 32200 | Loss: 0.4654 | Spent: 9.0 secs | LR: 0.000158
INFO:tensorflow:Step 32300 | Loss: 0.6102 | Spent: 8.9 secs | LR: 0.000153
INFO:tensorflow:Step 32400 | Loss: 0.5437 | Spent: 8.3 secs | LR: 0.000149
INFO:tensorflow:Step 32500 | Loss: 0.4606 | Spent: 8.4 secs | LR: 0.000145
INFO:tensorflow:Step 32600 | Loss: 0.3588 | Spent: 8.4 secs | LR: 0.000141
INFO:tensorflow:Step 32700 | Loss: 0.5235 | Spent: 8.6 secs | LR: 0.000137
INFO:tensorflow:Step 32800 | Loss: 0.3684 | Spent: 8.4 secs | LR: 0.000133
INFO:tensorflow:Step 32900 | Loss: 0.3551 | Spent: 8.4 secs | LR: 0.000128
INFO:tensorflow:Step 33000 | Loss: 0.3726 | Spent: 8.9 secs | LR: 0.000124
INFO:tensorflow:Step 33100 | Loss: 0.4766 | Spent: 8.6 secs | LR: 0.000120
INFO:tensorflow:Step 33200 | Loss: 0.4378 | Spent: 8.9 secs | LR: 0.000116
INFO:tensorflow:Step 33300 | Loss: 0.6233 | Spent: 8.6 secs | LR: 0.000112
INFO:tensorflow:Step 33400 | Loss: 0.3814 | Spent: 8.7 secs | LR: 0.000108
INFO:tensorflow:Step 33500 | Loss: 0.4950 | Spent: 9.0 secs | LR: 0.000103
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.818
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.796     0.855     0.824      5000
     Matched      0.843     0.780     0.811      5000

    accuracy                          0.818     10000
   macro avg      0.819     0.818     0.817     10000
weighted avg      0.819     0.818     0.817     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.699
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.769     0.806     0.787      2978
     Matched      0.516     0.462     0.488      1338

    accuracy                          0.699      4316
   macro avg      0.643     0.634     0.637      4316
weighted avg      0.691     0.699     0.694      4316

INFO:tensorflow:Best | Accuracy 1: 0.823 | Accuracy 2: 0.698
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 33600 | Loss: 0.4200 | Spent: 50.2 secs | LR: 0.000100
INFO:tensorflow:Step 33700 | Loss: 0.6104 | Spent: 8.4 secs | LR: 0.000102
INFO:tensorflow:Step 33800 | Loss: 0.4386 | Spent: 8.6 secs | LR: 0.000105
INFO:tensorflow:Step 33900 | Loss: 0.4232 | Spent: 8.5 secs | LR: 0.000107
INFO:tensorflow:Step 34000 | Loss: 0.4759 | Spent: 8.7 secs | LR: 0.000109
INFO:tensorflow:Step 34100 | Loss: 0.3219 | Spent: 8.4 secs | LR: 0.000111
INFO:tensorflow:Step 34200 | Loss: 0.4269 | Spent: 8.3 secs | LR: 0.000113
INFO:tensorflow:Step 34300 | Loss: 0.4596 | Spent: 8.4 secs | LR: 0.000115
INFO:tensorflow:Step 34400 | Loss: 0.5563 | Spent: 8.5 secs | LR: 0.000117
INFO:tensorflow:Step 34500 | Loss: 0.5728 | Spent: 8.3 secs | LR: 0.000119
INFO:tensorflow:Step 34600 | Loss: 0.4722 | Spent: 8.7 secs | LR: 0.000121
INFO:tensorflow:Step 34700 | Loss: 0.3898 | Spent: 8.5 secs | LR: 0.000123
INFO:tensorflow:Step 34800 | Loss: 0.5282 | Spent: 8.3 secs | LR: 0.000125
INFO:tensorflow:Step 34900 | Loss: 0.5131 | Spent: 8.6 secs | LR: 0.000127
INFO:tensorflow:Step 35000 | Loss: 0.4699 | Spent: 8.4 secs | LR: 0.000130
INFO:tensorflow:Step 35100 | Loss: 0.3205 | Spent: 8.6 secs | LR: 0.000132
INFO:tensorflow:Step 35200 | Loss: 0.5778 | Spent: 8.5 secs | LR: 0.000134
INFO:tensorflow:Step 35300 | Loss: 0.4659 | Spent: 8.5 secs | LR: 0.000136
INFO:tensorflow:Step 35400 | Loss: 0.6073 | Spent: 8.9 secs | LR: 0.000138
INFO:tensorflow:Step 35500 | Loss: 0.5513 | Spent: 8.4 secs | LR: 0.000140
INFO:tensorflow:Step 35600 | Loss: 0.4644 | Spent: 8.3 secs | LR: 0.000142
INFO:tensorflow:Step 35700 | Loss: 0.4571 | Spent: 8.8 secs | LR: 0.000144
INFO:tensorflow:Step 35800 | Loss: 0.2726 | Spent: 8.3 secs | LR: 0.000146
INFO:tensorflow:Step 35900 | Loss: 0.4707 | Spent: 8.6 secs | LR: 0.000148
INFO:tensorflow:Step 36000 | Loss: 0.4029 | Spent: 8.0 secs | LR: 0.000150
INFO:tensorflow:Step 36100 | Loss: 0.3224 | Spent: 8.6 secs | LR: 0.000152
INFO:tensorflow:Step 36200 | Loss: 0.4164 | Spent: 8.5 secs | LR: 0.000155
INFO:tensorflow:Step 36300 | Loss: 0.4903 | Spent: 8.6 secs | LR: 0.000157
INFO:tensorflow:Step 36400 | Loss: 0.4443 | Spent: 8.2 secs | LR: 0.000159
INFO:tensorflow:Step 36500 | Loss: 0.5571 | Spent: 8.0 secs | LR: 0.000161
INFO:tensorflow:Step 36600 | Loss: 0.6001 | Spent: 8.6 secs | LR: 0.000163
INFO:tensorflow:Step 36700 | Loss: 0.4590 | Spent: 8.4 secs | LR: 0.000165
INFO:tensorflow:Step 36800 | Loss: 0.4851 | Spent: 8.7 secs | LR: 0.000167
INFO:tensorflow:Step 36900 | Loss: 0.4668 | Spent: 8.6 secs | LR: 0.000169
INFO:tensorflow:Step 37000 | Loss: 0.4484 | Spent: 8.7 secs | LR: 0.000171
INFO:tensorflow:Step 37100 | Loss: 0.4883 | Spent: 8.6 secs | LR: 0.000173
INFO:tensorflow:Step 37200 | Loss: 0.4186 | Spent: 8.2 secs | LR: 0.000175
INFO:tensorflow:Step 37300 | Loss: 0.5603 | Spent: 8.6 secs | LR: 0.000177
INFO:tensorflow:Step 37400 | Loss: 0.4982 | Spent: 8.3 secs | LR: 0.000180
INFO:tensorflow:Step 37500 | Loss: 0.6114 | Spent: 8.2 secs | LR: 0.000182
INFO:tensorflow:Step 37600 | Loss: 0.4383 | Spent: 8.1 secs | LR: 0.000184
INFO:tensorflow:Step 37700 | Loss: 0.4758 | Spent: 8.5 secs | LR: 0.000186
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.816
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.799     0.846     0.822      5000
     Matched      0.836     0.787     0.811      5000

    accuracy                          0.816     10000
   macro avg      0.818     0.816     0.816     10000
weighted avg      0.818     0.816     0.816     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.678
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.777     0.749     0.763      2978
     Matched      0.483     0.521     0.501      1338

    accuracy                          0.678      4316
   macro avg      0.630     0.635     0.632      4316
weighted avg      0.686     0.678     0.682      4316

INFO:tensorflow:Best | Accuracy 1: 0.823 | Accuracy 2: 0.698
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 37800 | Loss: 0.4744 | Spent: 47.1 secs | LR: 0.000188
INFO:tensorflow:Step 37900 | Loss: 0.4141 | Spent: 8.7 secs | LR: 0.000190
INFO:tensorflow:Step 38000 | Loss: 0.3880 | Spent: 8.3 secs | LR: 0.000192
INFO:tensorflow:Step 38100 | Loss: 0.5787 | Spent: 8.6 secs | LR: 0.000194
INFO:tensorflow:Step 38200 | Loss: 0.3213 | Spent: 8.2 secs | LR: 0.000196
INFO:tensorflow:Step 38300 | Loss: 0.4137 | Spent: 8.6 secs | LR: 0.000198
INFO:tensorflow:Step 38400 | Loss: 0.3781 | Spent: 8.8 secs | LR: 0.000200
INFO:tensorflow:Step 38500 | Loss: 0.5816 | Spent: 8.3 secs | LR: 0.000203
INFO:tensorflow:Step 38600 | Loss: 0.4276 | Spent: 8.4 secs | LR: 0.000205
INFO:tensorflow:Step 38700 | Loss: 0.4412 | Spent: 8.4 secs | LR: 0.000207
INFO:tensorflow:Step 38800 | Loss: 0.3526 | Spent: 8.4 secs | LR: 0.000209
INFO:tensorflow:Step 38900 | Loss: 0.4117 | Spent: 8.6 secs | LR: 0.000211
INFO:tensorflow:Step 39000 | Loss: 0.7513 | Spent: 8.3 secs | LR: 0.000213
INFO:tensorflow:Step 39100 | Loss: 0.6835 | Spent: 8.2 secs | LR: 0.000215
INFO:tensorflow:Step 39200 | Loss: 0.4336 | Spent: 8.7 secs | LR: 0.000217
INFO:tensorflow:Step 39300 | Loss: 0.3738 | Spent: 8.8 secs | LR: 0.000219
INFO:tensorflow:Step 39400 | Loss: 0.5534 | Spent: 8.3 secs | LR: 0.000221
INFO:tensorflow:Step 39500 | Loss: 0.4069 | Spent: 8.3 secs | LR: 0.000223
INFO:tensorflow:Step 39600 | Loss: 0.4401 | Spent: 8.6 secs | LR: 0.000225
INFO:tensorflow:Step 39700 | Loss: 0.5379 | Spent: 8.1 secs | LR: 0.000228
INFO:tensorflow:Step 39800 | Loss: 0.3919 | Spent: 8.9 secs | LR: 0.000230
INFO:tensorflow:Step 39900 | Loss: 0.4026 | Spent: 8.4 secs | LR: 0.000232
INFO:tensorflow:Step 40000 | Loss: 0.6353 | Spent: 8.4 secs | LR: 0.000234
INFO:tensorflow:Step 40100 | Loss: 0.7259 | Spent: 8.4 secs | LR: 0.000236
INFO:tensorflow:Step 40200 | Loss: 0.5964 | Spent: 8.2 secs | LR: 0.000238
INFO:tensorflow:Step 40300 | Loss: 0.3601 | Spent: 8.4 secs | LR: 0.000240
INFO:tensorflow:Step 40400 | Loss: 0.3671 | Spent: 8.1 secs | LR: 0.000242
INFO:tensorflow:Step 40500 | Loss: 0.3708 | Spent: 8.6 secs | LR: 0.000244
INFO:tensorflow:Step 40600 | Loss: 0.3974 | Spent: 8.5 secs | LR: 0.000246
INFO:tensorflow:Step 40700 | Loss: 0.3890 | Spent: 8.3 secs | LR: 0.000248
INFO:tensorflow:Step 40800 | Loss: 0.4118 | Spent: 8.3 secs | LR: 0.000250
INFO:tensorflow:Step 40900 | Loss: 0.4585 | Spent: 8.3 secs | LR: 0.000253
INFO:tensorflow:Step 41000 | Loss: 0.4745 | Spent: 8.3 secs | LR: 0.000255
INFO:tensorflow:Step 41100 | Loss: 0.4533 | Spent: 7.8 secs | LR: 0.000257
INFO:tensorflow:Step 41200 | Loss: 0.6346 | Spent: 8.2 secs | LR: 0.000259
INFO:tensorflow:Step 41300 | Loss: 0.4103 | Spent: 8.3 secs | LR: 0.000261
INFO:tensorflow:Step 41400 | Loss: 0.6399 | Spent: 8.6 secs | LR: 0.000263
INFO:tensorflow:Step 41500 | Loss: 0.5086 | Spent: 8.1 secs | LR: 0.000265
INFO:tensorflow:Step 41600 | Loss: 0.5183 | Spent: 8.5 secs | LR: 0.000267
INFO:tensorflow:Step 41700 | Loss: 0.8442 | Spent: 8.3 secs | LR: 0.000269
INFO:tensorflow:Step 41800 | Loss: 0.4354 | Spent: 8.2 secs | LR: 0.000271
INFO:tensorflow:Step 41900 | Loss: 0.7936 | Spent: 7.9 secs | LR: 0.000273
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.819
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.807     0.839     0.823      5000
     Matched      0.832     0.800     0.816      5000

    accuracy                          0.819     10000
   macro avg      0.820     0.819     0.819     10000
weighted avg      0.820     0.819     0.819     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.701
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.772     0.803     0.787      2978
     Matched      0.519     0.473     0.495      1338

    accuracy                          0.701      4316
   macro avg      0.646     0.638     0.641      4316
weighted avg      0.694     0.701     0.697      4316

INFO:tensorflow:Best | Accuracy 1: 0.823 | Accuracy 2: 0.698
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 42000 | Loss: 0.3489 | Spent: 47.6 secs | LR: 0.000275
INFO:tensorflow:Step 42100 | Loss: 0.4328 | Spent: 8.4 secs | LR: 0.000278
INFO:tensorflow:Step 42200 | Loss: 0.4104 | Spent: 8.4 secs | LR: 0.000280
INFO:tensorflow:Step 42300 | Loss: 0.4312 | Spent: 8.3 secs | LR: 0.000282
INFO:tensorflow:Step 42400 | Loss: 0.3972 | Spent: 8.3 secs | LR: 0.000284
INFO:tensorflow:Step 42500 | Loss: 0.3411 | Spent: 8.4 secs | LR: 0.000286
INFO:tensorflow:Step 42600 | Loss: 0.4045 | Spent: 8.4 secs | LR: 0.000288
INFO:tensorflow:Step 42700 | Loss: 0.3360 | Spent: 7.9 secs | LR: 0.000290
INFO:tensorflow:Step 42800 | Loss: 0.5377 | Spent: 8.5 secs | LR: 0.000292
INFO:tensorflow:Step 42900 | Loss: 0.4361 | Spent: 8.1 secs | LR: 0.000294
INFO:tensorflow:Step 43000 | Loss: 0.5287 | Spent: 8.5 secs | LR: 0.000296
INFO:tensorflow:Step 43100 | Loss: 0.7215 | Spent: 8.6 secs | LR: 0.000298
INFO:tensorflow:Step 43200 | Loss: 0.5643 | Spent: 8.6 secs | LR: 0.000300
INFO:tensorflow:Step 43300 | Loss: 0.4214 | Spent: 8.8 secs | LR: 0.000303
INFO:tensorflow:Step 43400 | Loss: 0.3332 | Spent: 8.5 secs | LR: 0.000305
INFO:tensorflow:Step 43500 | Loss: 0.3956 | Spent: 8.7 secs | LR: 0.000307
INFO:tensorflow:Step 43600 | Loss: 0.4896 | Spent: 8.6 secs | LR: 0.000309
INFO:tensorflow:Step 43700 | Loss: 0.5168 | Spent: 8.1 secs | LR: 0.000311
INFO:tensorflow:Step 43800 | Loss: 0.6279 | Spent: 8.3 secs | LR: 0.000313
INFO:tensorflow:Step 43900 | Loss: 0.3987 | Spent: 8.3 secs | LR: 0.000315
INFO:tensorflow:Step 44000 | Loss: 0.3859 | Spent: 8.1 secs | LR: 0.000317
INFO:tensorflow:Step 44100 | Loss: 0.5035 | Spent: 8.5 secs | LR: 0.000319
INFO:tensorflow:Step 44200 | Loss: 0.5744 | Spent: 8.4 secs | LR: 0.000321
INFO:tensorflow:Step 44300 | Loss: 0.5053 | Spent: 8.1 secs | LR: 0.000323
INFO:tensorflow:Step 44400 | Loss: 0.6393 | Spent: 8.5 secs | LR: 0.000325
INFO:tensorflow:Step 44500 | Loss: 0.3736 | Spent: 8.3 secs | LR: 0.000328
INFO:tensorflow:Step 44600 | Loss: 0.4209 | Spent: 8.4 secs | LR: 0.000330
INFO:tensorflow:Step 44700 | Loss: 0.3606 | Spent: 8.2 secs | LR: 0.000332
INFO:tensorflow:Step 44800 | Loss: 0.4214 | Spent: 8.3 secs | LR: 0.000334
INFO:tensorflow:Step 44900 | Loss: 0.4388 | Spent: 8.2 secs | LR: 0.000336
INFO:tensorflow:Step 45000 | Loss: 0.4624 | Spent: 8.2 secs | LR: 0.000338
INFO:tensorflow:Step 45100 | Loss: 0.6021 | Spent: 8.5 secs | LR: 0.000340
INFO:tensorflow:Step 45200 | Loss: 0.4183 | Spent: 8.3 secs | LR: 0.000342
INFO:tensorflow:Step 45300 | Loss: 0.4041 | Spent: 8.2 secs | LR: 0.000344
INFO:tensorflow:Step 45400 | Loss: 0.4480 | Spent: 8.7 secs | LR: 0.000346
INFO:tensorflow:Step 45500 | Loss: 0.5583 | Spent: 8.7 secs | LR: 0.000348
INFO:tensorflow:Step 45600 | Loss: 0.3979 | Spent: 8.6 secs | LR: 0.000351
INFO:tensorflow:Step 45700 | Loss: 0.3562 | Spent: 8.0 secs | LR: 0.000353
INFO:tensorflow:Step 45800 | Loss: 0.5243 | Spent: 8.3 secs | LR: 0.000355
INFO:tensorflow:Step 45900 | Loss: 0.4542 | Spent: 8.2 secs | LR: 0.000357
INFO:tensorflow:Step 46000 | Loss: 0.4438 | Spent: 8.3 secs | LR: 0.000359
INFO:tensorflow:Step 46100 | Loss: 0.4678 | Spent: 8.7 secs | LR: 0.000361
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.819
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.830     0.803     0.816      5000
     Matched      0.809     0.835     0.822      5000

    accuracy                          0.819     10000
   macro avg      0.819     0.819     0.819     10000
weighted avg      0.819     0.819     0.819     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.673
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.781     0.732     0.756      2978
     Matched      0.476     0.542     0.507      1338

    accuracy                          0.673      4316
   macro avg      0.628     0.637     0.631      4316
weighted avg      0.686     0.673     0.679      4316

INFO:tensorflow:Best | Accuracy 1: 0.823 | Accuracy 2: 0.698
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 46200 | Loss: 0.3523 | Spent: 47.1 secs | LR: 0.000363
INFO:tensorflow:Step 46300 | Loss: 0.3594 | Spent: 8.5 secs | LR: 0.000365
INFO:tensorflow:Step 46400 | Loss: 0.5707 | Spent: 8.4 secs | LR: 0.000367
INFO:tensorflow:Step 46500 | Loss: 0.5463 | Spent: 8.1 secs | LR: 0.000369
INFO:tensorflow:Step 46600 | Loss: 0.3581 | Spent: 8.7 secs | LR: 0.000371
INFO:tensorflow:Step 46700 | Loss: 0.5334 | Spent: 8.4 secs | LR: 0.000373
INFO:tensorflow:Step 46800 | Loss: 0.3977 | Spent: 8.2 secs | LR: 0.000376
INFO:tensorflow:Step 46900 | Loss: 0.4246 | Spent: 8.1 secs | LR: 0.000378
INFO:tensorflow:Step 47000 | Loss: 0.5694 | Spent: 8.6 secs | LR: 0.000380
INFO:tensorflow:Step 47100 | Loss: 0.3639 | Spent: 8.1 secs | LR: 0.000382
INFO:tensorflow:Step 47200 | Loss: 0.6337 | Spent: 8.1 secs | LR: 0.000384
INFO:tensorflow:Step 47300 | Loss: 0.2905 | Spent: 8.4 secs | LR: 0.000386
INFO:tensorflow:Step 47400 | Loss: 0.4840 | Spent: 8.4 secs | LR: 0.000388
INFO:tensorflow:Step 47500 | Loss: 0.3929 | Spent: 8.4 secs | LR: 0.000390
INFO:tensorflow:Step 47600 | Loss: 0.3448 | Spent: 8.3 secs | LR: 0.000392
INFO:tensorflow:Step 47700 | Loss: 0.2928 | Spent: 8.7 secs | LR: 0.000394
INFO:tensorflow:Step 47800 | Loss: 0.3953 | Spent: 8.3 secs | LR: 0.000396
INFO:tensorflow:Step 47900 | Loss: 0.4829 | Spent: 8.3 secs | LR: 0.000398
INFO:tensorflow:Step 48000 | Loss: 0.4749 | Spent: 8.4 secs | LR: 0.000401
INFO:tensorflow:Step 48100 | Loss: 0.5452 | Spent: 8.3 secs | LR: 0.000403
INFO:tensorflow:Step 48200 | Loss: 0.3550 | Spent: 8.3 secs | LR: 0.000405
INFO:tensorflow:Step 48300 | Loss: 0.5806 | Spent: 8.2 secs | LR: 0.000407
INFO:tensorflow:Step 48400 | Loss: 0.4019 | Spent: 8.0 secs | LR: 0.000409
INFO:tensorflow:Step 48500 | Loss: 0.6523 | Spent: 8.4 secs | LR: 0.000411
INFO:tensorflow:Step 48600 | Loss: 0.3850 | Spent: 8.4 secs | LR: 0.000413
INFO:tensorflow:Step 48700 | Loss: 0.5486 | Spent: 8.1 secs | LR: 0.000415
INFO:tensorflow:Step 48800 | Loss: 0.5001 | Spent: 8.3 secs | LR: 0.000417
INFO:tensorflow:Step 48900 | Loss: 0.3443 | Spent: 8.3 secs | LR: 0.000419
INFO:tensorflow:Step 49000 | Loss: 0.4504 | Spent: 8.0 secs | LR: 0.000421
INFO:tensorflow:Step 49100 | Loss: 0.4066 | Spent: 8.6 secs | LR: 0.000423
INFO:tensorflow:Step 49200 | Loss: 0.4077 | Spent: 8.1 secs | LR: 0.000426
INFO:tensorflow:Step 49300 | Loss: 0.3595 | Spent: 8.3 secs | LR: 0.000428
INFO:tensorflow:Step 49400 | Loss: 0.4581 | Spent: 8.2 secs | LR: 0.000430
INFO:tensorflow:Step 49500 | Loss: 0.4348 | Spent: 8.6 secs | LR: 0.000432
INFO:tensorflow:Step 49600 | Loss: 0.3990 | Spent: 8.4 secs | LR: 0.000434
INFO:tensorflow:Step 49700 | Loss: 0.4356 | Spent: 8.0 secs | LR: 0.000436
INFO:tensorflow:Step 49800 | Loss: 0.3269 | Spent: 8.5 secs | LR: 0.000438
INFO:tensorflow:Step 49900 | Loss: 0.5976 | Spent: 8.6 secs | LR: 0.000440
INFO:tensorflow:Step 50000 | Loss: 0.3448 | Spent: 8.4 secs | LR: 0.000442
INFO:tensorflow:Step 50100 | Loss: 0.3477 | Spent: 8.5 secs | LR: 0.000444
INFO:tensorflow:Step 50200 | Loss: 0.3352 | Spent: 8.5 secs | LR: 0.000446
INFO:tensorflow:Step 50300 | Loss: 0.4085 | Spent: 8.2 secs | LR: 0.000448
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.820
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.818     0.822     0.820      5000
     Matched      0.821     0.817     0.819      5000

    accuracy                          0.820     10000
   macro avg      0.820     0.820     0.820     10000
weighted avg      0.820     0.820     0.820     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.675
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.779     0.739     0.759      2978
     Matched      0.479     0.534     0.505      1338

    accuracy                          0.675      4316
   macro avg      0.629     0.636     0.632      4316
weighted avg      0.686     0.675     0.680      4316

INFO:tensorflow:Best | Accuracy 1: 0.823 | Accuracy 2: 0.698
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 50400 | Loss: 0.4338 | Spent: 47.3 secs | LR: 0.000449
INFO:tensorflow:Step 50500 | Loss: 0.3440 | Spent: 8.4 secs | LR: 0.000447
INFO:tensorflow:Step 50600 | Loss: 0.4424 | Spent: 8.4 secs | LR: 0.000445
INFO:tensorflow:Step 50700 | Loss: 0.3269 | Spent: 8.7 secs | LR: 0.000443
INFO:tensorflow:Step 50800 | Loss: 0.4747 | Spent: 8.4 secs | LR: 0.000441
INFO:tensorflow:Step 50900 | Loss: 0.5139 | Spent: 8.4 secs | LR: 0.000439
INFO:tensorflow:Step 51000 | Loss: 0.3400 | Spent: 8.6 secs | LR: 0.000437
INFO:tensorflow:Step 51100 | Loss: 0.4905 | Spent: 8.4 secs | LR: 0.000435
INFO:tensorflow:Step 51200 | Loss: 0.4028 | Spent: 8.2 secs | LR: 0.000433
INFO:tensorflow:Step 51300 | Loss: 0.4766 | Spent: 8.2 secs | LR: 0.000431
INFO:tensorflow:Step 51400 | Loss: 0.4144 | Spent: 8.1 secs | LR: 0.000429
INFO:tensorflow:Step 51500 | Loss: 0.4620 | Spent: 8.4 secs | LR: 0.000427
INFO:tensorflow:Step 51600 | Loss: 0.3860 | Spent: 8.3 secs | LR: 0.000424
INFO:tensorflow:Step 51700 | Loss: 0.4002 | Spent: 8.8 secs | LR: 0.000422
INFO:tensorflow:Step 51800 | Loss: 0.4380 | Spent: 8.4 secs | LR: 0.000420
INFO:tensorflow:Step 51900 | Loss: 0.2921 | Spent: 8.7 secs | LR: 0.000418
INFO:tensorflow:Step 52000 | Loss: 0.3357 | Spent: 8.1 secs | LR: 0.000416
INFO:tensorflow:Step 52100 | Loss: 0.3541 | Spent: 8.3 secs | LR: 0.000414
INFO:tensorflow:Step 52200 | Loss: 0.5994 | Spent: 8.1 secs | LR: 0.000412
INFO:tensorflow:Step 52300 | Loss: 0.5939 | Spent: 8.5 secs | LR: 0.000410
INFO:tensorflow:Step 52400 | Loss: 0.5354 | Spent: 8.3 secs | LR: 0.000408
INFO:tensorflow:Step 52500 | Loss: 0.5404 | Spent: 8.6 secs | LR: 0.000406
INFO:tensorflow:Step 52600 | Loss: 0.4580 | Spent: 8.2 secs | LR: 0.000404
INFO:tensorflow:Step 52700 | Loss: 0.6106 | Spent: 8.2 secs | LR: 0.000401
INFO:tensorflow:Step 52800 | Loss: 0.3396 | Spent: 8.2 secs | LR: 0.000399
INFO:tensorflow:Step 52900 | Loss: 0.4266 | Spent: 7.9 secs | LR: 0.000397
INFO:tensorflow:Step 53000 | Loss: 0.4310 | Spent: 8.2 secs | LR: 0.000395
INFO:tensorflow:Step 53100 | Loss: 0.4730 | Spent: 8.5 secs | LR: 0.000393
INFO:tensorflow:Step 53200 | Loss: 0.5349 | Spent: 8.6 secs | LR: 0.000391
INFO:tensorflow:Step 53300 | Loss: 0.3929 | Spent: 8.2 secs | LR: 0.000389
INFO:tensorflow:Step 53400 | Loss: 0.4883 | Spent: 8.3 secs | LR: 0.000387
INFO:tensorflow:Step 53500 | Loss: 0.3343 | Spent: 8.3 secs | LR: 0.000385
INFO:tensorflow:Step 53600 | Loss: 0.5062 | Spent: 8.3 secs | LR: 0.000383
INFO:tensorflow:Step 53700 | Loss: 0.3390 | Spent: 8.5 secs | LR: 0.000381
INFO:tensorflow:Step 53800 | Loss: 0.4597 | Spent: 8.7 secs | LR: 0.000379
INFO:tensorflow:Step 53900 | Loss: 0.3208 | Spent: 8.6 secs | LR: 0.000376
INFO:tensorflow:Step 54000 | Loss: 0.3489 | Spent: 8.2 secs | LR: 0.000374
INFO:tensorflow:Step 54100 | Loss: 0.4357 | Spent: 8.6 secs | LR: 0.000372
INFO:tensorflow:Step 54200 | Loss: 0.3470 | Spent: 8.2 secs | LR: 0.000370
INFO:tensorflow:Step 54300 | Loss: 0.3981 | Spent: 8.2 secs | LR: 0.000368
INFO:tensorflow:Step 54400 | Loss: 0.4083 | Spent: 8.4 secs | LR: 0.000366
INFO:tensorflow:Step 54500 | Loss: 0.3635 | Spent: 8.1 secs | LR: 0.000364
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.811
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.798     0.833     0.815      5000
     Matched      0.826     0.789     0.807      5000

    accuracy                          0.811     10000
   macro avg      0.812     0.811     0.811     10000
weighted avg      0.812     0.811     0.811     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.683
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.770     0.769     0.770      2978
     Matched      0.488     0.490     0.489      1338

    accuracy                          0.683      4316
   macro avg      0.629     0.629     0.629      4316
weighted avg      0.683     0.683     0.683      4316

INFO:tensorflow:Best | Accuracy 1: 0.823 | Accuracy 2: 0.698
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 54600 | Loss: 0.3642 | Spent: 47.4 secs | LR: 0.000362
INFO:tensorflow:Step 54700 | Loss: 0.3472 | Spent: 8.4 secs | LR: 0.000360
INFO:tensorflow:Step 54800 | Loss: 0.4681 | Spent: 8.2 secs | LR: 0.000358
INFO:tensorflow:Step 54900 | Loss: 0.6148 | Spent: 8.6 secs | LR: 0.000356
INFO:tensorflow:Step 55000 | Loss: 0.4256 | Spent: 8.7 secs | LR: 0.000354
INFO:tensorflow:Step 55100 | Loss: 0.4056 | Spent: 8.2 secs | LR: 0.000351
INFO:tensorflow:Step 55200 | Loss: 0.3984 | Spent: 8.5 secs | LR: 0.000349
INFO:tensorflow:Step 55300 | Loss: 0.4088 | Spent: 8.7 secs | LR: 0.000347
INFO:tensorflow:Step 55400 | Loss: 0.3965 | Spent: 8.6 secs | LR: 0.000345
INFO:tensorflow:Step 55500 | Loss: 0.3856 | Spent: 8.4 secs | LR: 0.000343
INFO:tensorflow:Step 55600 | Loss: 0.3284 | Spent: 8.0 secs | LR: 0.000341
INFO:tensorflow:Step 55700 | Loss: 0.4415 | Spent: 8.2 secs | LR: 0.000339
INFO:tensorflow:Step 55800 | Loss: 0.4253 | Spent: 8.2 secs | LR: 0.000337
INFO:tensorflow:Step 55900 | Loss: 0.4753 | Spent: 8.5 secs | LR: 0.000335
INFO:tensorflow:Step 56000 | Loss: 0.3171 | Spent: 8.8 secs | LR: 0.000333
INFO:tensorflow:Step 56100 | Loss: 0.3240 | Spent: 8.3 secs | LR: 0.000331
INFO:tensorflow:Step 56200 | Loss: 0.3305 | Spent: 8.2 secs | LR: 0.000329
INFO:tensorflow:Step 56300 | Loss: 0.3237 | Spent: 7.9 secs | LR: 0.000326
INFO:tensorflow:Step 56400 | Loss: 0.3694 | Spent: 8.2 secs | LR: 0.000324
INFO:tensorflow:Step 56500 | Loss: 0.3436 | Spent: 8.5 secs | LR: 0.000322
INFO:tensorflow:Step 56600 | Loss: 0.4986 | Spent: 8.3 secs | LR: 0.000320
INFO:tensorflow:Step 56700 | Loss: 0.4526 | Spent: 8.0 secs | LR: 0.000318
INFO:tensorflow:Step 56800 | Loss: 0.4124 | Spent: 8.8 secs | LR: 0.000316
INFO:tensorflow:Step 56900 | Loss: 0.4816 | Spent: 8.4 secs | LR: 0.000314
INFO:tensorflow:Step 57000 | Loss: 0.3756 | Spent: 8.7 secs | LR: 0.000312
INFO:tensorflow:Step 57100 | Loss: 0.5194 | Spent: 8.1 secs | LR: 0.000310
INFO:tensorflow:Step 57200 | Loss: 0.4742 | Spent: 8.1 secs | LR: 0.000308
INFO:tensorflow:Step 57300 | Loss: 0.4079 | Spent: 8.6 secs | LR: 0.000306
INFO:tensorflow:Step 57400 | Loss: 0.3931 | Spent: 8.3 secs | LR: 0.000304
INFO:tensorflow:Step 57500 | Loss: 0.5572 | Spent: 8.3 secs | LR: 0.000301
INFO:tensorflow:Step 57600 | Loss: 0.3950 | Spent: 8.3 secs | LR: 0.000299
INFO:tensorflow:Step 57700 | Loss: 0.5449 | Spent: 8.5 secs | LR: 0.000297
INFO:tensorflow:Step 57800 | Loss: 0.3567 | Spent: 8.3 secs | LR: 0.000295
INFO:tensorflow:Step 57900 | Loss: 0.3306 | Spent: 8.3 secs | LR: 0.000293
INFO:tensorflow:Step 58000 | Loss: 0.5263 | Spent: 8.4 secs | LR: 0.000291
INFO:tensorflow:Step 58100 | Loss: 0.3548 | Spent: 8.3 secs | LR: 0.000289
INFO:tensorflow:Step 58200 | Loss: 0.4250 | Spent: 8.5 secs | LR: 0.000287
INFO:tensorflow:Step 58300 | Loss: 0.3431 | Spent: 8.2 secs | LR: 0.000285
INFO:tensorflow:Step 58400 | Loss: 0.4072 | Spent: 8.3 secs | LR: 0.000283
INFO:tensorflow:Step 58500 | Loss: 0.4032 | Spent: 8.2 secs | LR: 0.000281
INFO:tensorflow:Step 58600 | Loss: 0.3291 | Spent: 8.4 secs | LR: 0.000279
INFO:tensorflow:Step 58700 | Loss: 0.4186 | Spent: 8.4 secs | LR: 0.000276
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.814
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.811     0.819     0.815      5000
     Matched      0.817     0.809     0.813      5000

    accuracy                          0.814     10000
   macro avg      0.814     0.814     0.814     10000
weighted avg      0.814     0.814     0.814     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.674
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.781     0.734     0.757      2978
     Matched      0.478     0.542     0.508      1338

    accuracy                          0.674      4316
   macro avg      0.629     0.638     0.632      4316
weighted avg      0.687     0.674     0.680      4316

INFO:tensorflow:Best | Accuracy 1: 0.823 | Accuracy 2: 0.698
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 58800 | Loss: 0.4770 | Spent: 47.9 secs | LR: 0.000274
INFO:tensorflow:Step 58900 | Loss: 0.4974 | Spent: 8.6 secs | LR: 0.000272
INFO:tensorflow:Step 59000 | Loss: 0.4646 | Spent: 8.2 secs | LR: 0.000270
INFO:tensorflow:Step 59100 | Loss: 0.3490 | Spent: 8.2 secs | LR: 0.000268
INFO:tensorflow:Step 59200 | Loss: 0.3617 | Spent: 8.3 secs | LR: 0.000266
INFO:tensorflow:Step 59300 | Loss: 0.5723 | Spent: 8.3 secs | LR: 0.000264
INFO:tensorflow:Step 59400 | Loss: 0.4841 | Spent: 8.2 secs | LR: 0.000262
INFO:tensorflow:Step 59500 | Loss: 0.3625 | Spent: 8.0 secs | LR: 0.000260
INFO:tensorflow:Step 59600 | Loss: 0.4037 | Spent: 8.2 secs | LR: 0.000258
INFO:tensorflow:Step 59700 | Loss: 0.4434 | Spent: 8.3 secs | LR: 0.000256
INFO:tensorflow:Step 59800 | Loss: 0.4365 | Spent: 8.7 secs | LR: 0.000253
INFO:tensorflow:Step 59900 | Loss: 0.3835 | Spent: 8.6 secs | LR: 0.000251
INFO:tensorflow:Step 60000 | Loss: 0.3959 | Spent: 8.4 secs | LR: 0.000249
INFO:tensorflow:Step 60100 | Loss: 0.3226 | Spent: 8.3 secs | LR: 0.000247
INFO:tensorflow:Step 60200 | Loss: 0.4362 | Spent: 8.1 secs | LR: 0.000245
INFO:tensorflow:Step 60300 | Loss: 0.4424 | Spent: 8.6 secs | LR: 0.000243
INFO:tensorflow:Step 60400 | Loss: 0.3904 | Spent: 7.9 secs | LR: 0.000241
INFO:tensorflow:Step 60500 | Loss: 0.4535 | Spent: 8.2 secs | LR: 0.000239
INFO:tensorflow:Step 60600 | Loss: 0.3359 | Spent: 8.4 secs | LR: 0.000237
INFO:tensorflow:Step 60700 | Loss: 0.3409 | Spent: 7.9 secs | LR: 0.000235
INFO:tensorflow:Step 60800 | Loss: 0.3661 | Spent: 8.4 secs | LR: 0.000233
INFO:tensorflow:Step 60900 | Loss: 0.4140 | Spent: 8.0 secs | LR: 0.000231
INFO:tensorflow:Step 61000 | Loss: 0.4821 | Spent: 8.6 secs | LR: 0.000228
INFO:tensorflow:Step 61100 | Loss: 0.3443 | Spent: 8.2 secs | LR: 0.000226
INFO:tensorflow:Step 61200 | Loss: 0.4650 | Spent: 8.3 secs | LR: 0.000224
INFO:tensorflow:Step 61300 | Loss: 0.6546 | Spent: 8.5 secs | LR: 0.000222
INFO:tensorflow:Step 61400 | Loss: 0.4604 | Spent: 8.7 secs | LR: 0.000220
INFO:tensorflow:Step 61500 | Loss: 0.4381 | Spent: 8.3 secs | LR: 0.000218
INFO:tensorflow:Step 61600 | Loss: 0.4472 | Spent: 8.5 secs | LR: 0.000216
INFO:tensorflow:Step 61700 | Loss: 0.2894 | Spent: 8.3 secs | LR: 0.000214
INFO:tensorflow:Step 61800 | Loss: 0.3854 | Spent: 8.4 secs | LR: 0.000212
INFO:tensorflow:Step 61900 | Loss: 0.5023 | Spent: 8.3 secs | LR: 0.000210
INFO:tensorflow:Step 62000 | Loss: 0.4235 | Spent: 8.4 secs | LR: 0.000208
INFO:tensorflow:Step 62100 | Loss: 0.3347 | Spent: 8.3 secs | LR: 0.000206
INFO:tensorflow:Step 62200 | Loss: 0.7406 | Spent: 8.4 secs | LR: 0.000203
INFO:tensorflow:Step 62300 | Loss: 0.3981 | Spent: 8.8 secs | LR: 0.000201
INFO:tensorflow:Step 62400 | Loss: 0.3751 | Spent: 8.3 secs | LR: 0.000199
INFO:tensorflow:Step 62500 | Loss: 0.2951 | Spent: 8.3 secs | LR: 0.000197
INFO:tensorflow:Step 62600 | Loss: 0.3840 | Spent: 8.3 secs | LR: 0.000195
INFO:tensorflow:Step 62700 | Loss: 0.4345 | Spent: 8.4 secs | LR: 0.000193
INFO:tensorflow:Step 62800 | Loss: 0.4782 | Spent: 8.3 secs | LR: 0.000191
INFO:tensorflow:Step 62900 | Loss: 0.4593 | Spent: 8.3 secs | LR: 0.000189
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.812
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.785     0.860     0.821      5000
     Matched      0.845     0.764     0.803      5000

    accuracy                          0.812     10000
   macro avg      0.815     0.812     0.812     10000
weighted avg      0.815     0.812     0.812     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.691
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.746     0.837     0.789      2978
     Matched      0.503     0.366     0.424      1338

    accuracy                          0.691      4316
   macro avg      0.625     0.602     0.607      4316
weighted avg      0.671     0.691     0.676      4316

INFO:tensorflow:Best | Accuracy 1: 0.823 | Accuracy 2: 0.698
Reading ../data/train.csv
Reading ../data/train.json
INFO:tensorflow:Step 63000 | Loss: 0.3327 | Spent: 46.4 secs | LR: 0.000187
INFO:tensorflow:Step 63100 | Loss: 0.4724 | Spent: 8.1 secs | LR: 0.000185
INFO:tensorflow:Step 63200 | Loss: 0.4724 | Spent: 8.2 secs | LR: 0.000183
INFO:tensorflow:Step 63300 | Loss: 0.4823 | Spent: 8.5 secs | LR: 0.000181
INFO:tensorflow:Step 63400 | Loss: 0.3313 | Spent: 8.6 secs | LR: 0.000178
INFO:tensorflow:Step 63500 | Loss: 0.5162 | Spent: 8.5 secs | LR: 0.000176
INFO:tensorflow:Step 63600 | Loss: 0.3872 | Spent: 8.2 secs | LR: 0.000174
INFO:tensorflow:Step 63700 | Loss: 0.3016 | Spent: 8.2 secs | LR: 0.000172
INFO:tensorflow:Step 63800 | Loss: 0.2889 | Spent: 8.3 secs | LR: 0.000170
INFO:tensorflow:Step 63900 | Loss: 0.3540 | Spent: 8.4 secs | LR: 0.000168
INFO:tensorflow:Step 64000 | Loss: 0.2433 | Spent: 8.8 secs | LR: 0.000166
INFO:tensorflow:Step 64100 | Loss: 0.3281 | Spent: 8.7 secs | LR: 0.000164
INFO:tensorflow:Step 64200 | Loss: 0.4053 | Spent: 8.1 secs | LR: 0.000162
INFO:tensorflow:Step 64300 | Loss: 0.2972 | Spent: 8.3 secs | LR: 0.000160
INFO:tensorflow:Step 64400 | Loss: 0.4895 | Spent: 8.4 secs | LR: 0.000158
INFO:tensorflow:Step 64500 | Loss: 0.3721 | Spent: 8.2 secs | LR: 0.000156
INFO:tensorflow:Step 64600 | Loss: 0.4737 | Spent: 8.3 secs | LR: 0.000153
INFO:tensorflow:Step 64700 | Loss: 0.3817 | Spent: 8.7 secs | LR: 0.000151
INFO:tensorflow:Step 64800 | Loss: 0.5392 | Spent: 8.3 secs | LR: 0.000149
INFO:tensorflow:Step 64900 | Loss: 0.6117 | Spent: 8.3 secs | LR: 0.000147
INFO:tensorflow:Step 65000 | Loss: 0.3718 | Spent: 8.3 secs | LR: 0.000145
INFO:tensorflow:Step 65100 | Loss: 0.4861 | Spent: 8.4 secs | LR: 0.000143
INFO:tensorflow:Step 65200 | Loss: 0.4008 | Spent: 8.2 secs | LR: 0.000141
INFO:tensorflow:Step 65300 | Loss: 0.3485 | Spent: 8.5 secs | LR: 0.000139
INFO:tensorflow:Step 65400 | Loss: 0.6461 | Spent: 8.1 secs | LR: 0.000137
INFO:tensorflow:Step 65500 | Loss: 0.5095 | Spent: 8.2 secs | LR: 0.000135
INFO:tensorflow:Step 65600 | Loss: 0.4875 | Spent: 8.5 secs | LR: 0.000133
INFO:tensorflow:Step 65700 | Loss: 0.3766 | Spent: 8.0 secs | LR: 0.000131
INFO:tensorflow:Step 65800 | Loss: 0.3541 | Spent: 7.9 secs | LR: 0.000128
INFO:tensorflow:Step 65900 | Loss: 0.5405 | Spent: 8.2 secs | LR: 0.000126
INFO:tensorflow:Step 66000 | Loss: 0.3736 | Spent: 8.4 secs | LR: 0.000124
INFO:tensorflow:Step 66100 | Loss: 0.7233 | Spent: 8.3 secs | LR: 0.000122
INFO:tensorflow:Step 66200 | Loss: 0.5302 | Spent: 8.3 secs | LR: 0.000120
INFO:tensorflow:Step 66300 | Loss: 0.4006 | Spent: 8.4 secs | LR: 0.000118
INFO:tensorflow:Step 66400 | Loss: 0.3581 | Spent: 8.3 secs | LR: 0.000116
INFO:tensorflow:Step 66500 | Loss: 0.4025 | Spent: 8.5 secs | LR: 0.000114
INFO:tensorflow:Step 66600 | Loss: 0.4006 | Spent: 8.4 secs | LR: 0.000112
INFO:tensorflow:Step 66700 | Loss: 0.3758 | Spent: 8.3 secs | LR: 0.000110
INFO:tensorflow:Step 66800 | Loss: 0.4412 | Spent: 8.2 secs | LR: 0.000108
INFO:tensorflow:Step 66900 | Loss: 0.4461 | Spent: 8.6 secs | LR: 0.000106
INFO:tensorflow:Step 67000 | Loss: 0.3212 | Spent: 8.2 secs | LR: 0.000103
INFO:tensorflow:Step 67100 | Loss: 0.4298 | Spent: 8.4 secs | LR: 0.000101
Reading ../data/test.csv
INFO:tensorflow:测试集:微众银行智能客服
INFO:tensorflow:Evaluation: Testing Accuracy: 0.813
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.785     0.862     0.822      5000
     Matched      0.847     0.764     0.803      5000

    accuracy                          0.813     10000
   macro avg      0.816     0.813     0.812     10000
weighted avg      0.816     0.813     0.812     10000

Reading ../data/dev.json
INFO:tensorflow:测试集:蚂蚁金融语义相似度
INFO:tensorflow:Evaluation: Testing Accuracy: 0.692
INFO:tensorflow:
              precision    recall  f1-score   support

 Not Matched      0.757     0.815     0.785      2978
     Matched      0.504     0.418     0.457      1338

    accuracy                          0.692      4316
   macro avg      0.631     0.617     0.621      4316
weighted avg      0.679     0.692     0.683      4316

INFO:tensorflow:Best | Accuracy 1: 0.823 | Accuracy 2: 0.698
Reading ../data/train.csv