def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
labels, num_labels, use_one_hot_embeddings, token_start_mask,
hidden_size, num_layers, hidden_dropout_prob):
"""Creates a classification model."""
model = modeling.BertModel(config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
embedding = model.get_sequence_output()
batch_size, max_seq_length, embedding_size = modeling.get_shape_list(
embedding, expected_rank=3)
lengths = tf.reduce_sum(
input_mask, reduction_indices=1
) # [batch_size] vector, sequence lengths of current batch
mask = tf.to_float(token_start_mask)
# mlp = MLP_and_softmax(embedded_chars=embedding, hidden_size=hidden_size, num_layers=num_layers,
# hidden_dropout_prob=hidden_dropout_prob, initializers=initializers, num_labels=num_labels,
# seq_length=max_seq_length, labels=labels, length_mask=mask, is_training=is_training)
# rst = mlp.compute()
blstm_crf = BLSTM_CRF(embedded_chars=embedding,
hidden_unit=hidden_size,
cell_type='lstm',
num_layers=num_layers,
dropout_rate=1.0 - hidden_dropout_prob,
initializers=initializers,
num_labels=num_labels,
seq_length=max_seq_length,
labels=labels,
lengths=lengths,
is_training=is_training)
rst = blstm_crf.add_blstm_crf_layer_not_really_working(crf_only=False)
return rst
def create_model(bert_config, is_training, input_ids, input_mask,
segment_ids, labels, num_labels, use_one_hot_embeddings):
"""
创建X模型
:param bert_config: bert 配置
:param is_training:
:param input_ids: 数据的idx 表示
:param input_mask:
:param segment_ids:
:param labels: 标签的idx 表示
:param num_labels: 类别数量
:param use_one_hot_embeddings:
:return:
"""
# 使用数据加载BertModel,获取对应的字embedding
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings
)
# 获取对应的embedding 输入数据[batch_size, seq_length, embedding_size]
embedding = model.get_sequence_output()
max_seq_length = embedding.shape[1].value
used = tf.sign(tf.abs(input_ids))
lengths = tf.reduce_sum(used, reduction_indices=1) # [batch_size] 大小的向量，包含了当前batch中的序列长度
blstm_crf = BLSTM_CRF(embedded_chars=embedding, hidden_unit=FLAGS.lstm_size, cell_type=FLAGS.cell, num_layers=FLAGS.num_layers,
dropout_rate=FLAGS.droupout_rate, initializers=initializers, num_labels=num_labels,
seq_length=max_seq_length, labels=labels, lengths=lengths, is_training=is_training)
rst = blstm_crf.add_blstm_crf_layer(crf_only=True)
return rst
def create_model(bert_config, is_training, input_ids, input_mask,
segment_ids, labels, num_labels, use_one_hot_embeddings,
dropout_rate=1.0, lstm_size=1, cell='lstm', num_layers=1):
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings
)
bert_layer = model.get_sequence_output()
a = bert_layer.get_shape()
max_seq_length = bert_layer.shape[1].value
# 算序列真实长度
used = tf.sign(tf.abs(input_ids))
# [batch_size] 大小的向量，包含了当前batch中的序列长度
lengths = tf.reduce_sum(used, reduction_indices=1)
# 这里添加pos embedding层
# 添加CRF output layer
blstm_crf = BLSTM_CRF(embedded_chars=bert_layer, hidden_unit=lstm_size, cell_type=cell, num_layers=num_layers,
dropout_rate=dropout_rate, initializers=initializers, num_labels=num_labels,
seq_length=max_seq_length, labels=labels, lengths=lengths, is_training=is_training)
rst = blstm_crf.add_blstm_crf_layer(crf_only=True)
return rst
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
labels, num_labels, use_one_hot_embeddings):
"""Creates a classification model."""
model = modeling.BertModel(config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
embedding = model.get_sequence_output(
) # BERT模型输出的embedding [batch_size,max_seq_len,embedding_size]
max_seq_length = embedding.shape[1].value
used = tf.sign(tf.abs(input_ids))
lengths = tf.reduce_sum(used,
axis=1) # [batch_size] 大小的向量，包含了当前batch中的序列长度
blstm_crf = BLSTM_CRF(embedded_chars=embedding,
hidden_unit=512,
cell_type="lstm",
num_layers=2,
dropout_rate=0.1,
initializers=initializers,
num_labels=num_labels,
seq_length=max_seq_length,
labels=labels,
lengths=lengths,
is_training=is_training)
loss, logits, trans, pred_ids = blstm_crf.add_blstm_crf_layer(
crf_only=False)
return loss, logits, trans, pred_ids
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
labels, num_labels, use_one_hot_embeddings):
"""Creates a classification model."""
# 使用数据加载BertModel,获取对应的字embedding
model = modeling.BertModel(config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
# In the demo, we are doing a simple classification task on the entire
# segment.
#
# If you want to use the token-level output, use model.get_sequence_output()
# instead.
embedding = model.get_sequence_output()
max_seq_length = embedding.shape[1].value
used = tf.sign(tf.abs(input_ids))
lengths = tf.reduce_sum(used, reduction_indices=1)
blstm_crf = BLSTM_CRF(embedded_chars=embedding,
hidden_unit=FLAGS.lstm_size,
cell_type=FLAGS.cell,
num_layers=FLAGS.num_layers,
dropout_rate=FLAGS.droupout_rate,
initializers=initializers,
num_labels=num_labels,
seq_length=max_seq_length,
labels=labels,
lengths=lengths,
is_training=is_training)
rst = blstm_crf.add_blstm_crf_layer(crf_only=True)
return rst
def slot_filling(model, lengths, slot_id, num_slot, is_training):
'''
slot filling
'''
#获取对应的embedding 输入数据[batch_size, seq_length, embedding_size]
embedding = model.get_sequence_output()
max_seq_length = embedding.shape[1].value
#添加CRF out
blstm_crf_config = {
"embedded_chars": embedding,
"hidden_unit": config['lstm_size'],
"cell_type": config['cell'],
"num_layers": config['num_layers'],
"dropout_rate": config['dropout_rate'],
"initializers": initializers,
"num_labels": num_slot,
"seq_length": max_seq_length,
"labels": slot_id,
"lengths": lengths,
"is_training": is_training
}
blstm_crf = BLSTM_CRF(blstm_crf_config)
loss, logits, trans, pred_ids = blstm_crf.add_blstm_crf_layer(
crf_only=False)
return loss, logits, trans, pred_ids
def create_model(bert_config, is_training, input_ids, input_mask,
segment_ids, labels, num_labels, use_one_hot_embeddings):
"""
创建X模型
:param bert_config: bert 配置
:param is_training:
:param input_ids: 数据的idx 表示
:param input_mask:
:param segment_ids:
:param labels: 标签的idx 表示
:param num_labels: 类别数量
:param use_one_hot_embeddings:
:return:
"""
# 使用数据加载BertModel,获取对应的字embedding
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings
)
# 获取对应的embedding 输入数据[batch_size, seq_length, embedding_size]
embedding = model.get_sequence_output()
max_seq_length = embedding.shape[1].value
used = tf.sign(tf.abs(input_ids))
lengths = tf.reduce_sum(used, reduction_indices=1) # [batch_size] 大小的向量，包含了当前batch中的序列长度
blstm_crf = BLSTM_CRF(embedded_chars=embedding, hidden_unit=FLAGS.lstm_size, cell_type=FLAGS.cell, num_layers=FLAGS.num_layers,
droupout_rate=FLAGS.droupout_rate, initializers=initializers, num_labels=num_labels,
seq_length=max_seq_length, labels=labels, lengths=lengths, is_training=is_training)
rst = blstm_crf.add_blstm_crf_layer()
return rst
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
labels, num_labels, use_one_hot_embeddings):
# 使用数据加载BertModel,获取对应的字embedding
model = modeling.BertModel(config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
# 获取bert的输出embedding: NxTxH
embedding = model.get_sequence_output()
max_seq_length = embedding.shape[1].value
used = tf.sign(tf.abs(input_ids))
lengths = tf.reduce_sum(
used, reduction_indices=1) # [batch_size] 大小的向量，包含了当前batch中的序列长度
blstm_crf = BLSTM_CRF(embedded_chars=embedding,
hidden_unit=lstm_size,
cell_type=cell,
num_layers=num_layers,
droupout_rate=droupout_rate,
initializers=initializers,
num_labels=num_labels,
seq_length=max_seq_length,
labels=labels,
lengths=lengths,
is_training=is_training)
rst = blstm_crf.add_blstm_crf_layer()
return rst
def create_model(bert_config,
is_training,
input_ids,
input_mask,
segment_ids,
labels,
num_labels,
use_one_hot_embeddings,
dropout_rate=1.0,
lstm_size=1,
cell='lstm',
num_layers=1):
"""
创建X模型
:param bert_config: bert 配置
:param is_training:
:param input_ids: 数据的idx 表示
:param input_mask:
:param segment_ids:
:param labels: 标签的idx 表示
:param num_labels: 类别数量
:param use_one_hot_embeddings:
:return:
"""
# 使用数据加载BertModel,获取对应的字embedding
import tensorflow as tf
model = modeling.BertModel(config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
# Get the corresponding embedding input data [batch_size, seq_length, embedding_size]
embedding = model.get_sequence_output()
max_seq_length = embedding.shape[1].value
# Calculate the true length of the sequence
used = tf.sign(tf.abs(input_ids))
lengths = tf.reduce_sum(
used, reduction_indices=1) # [batch_size] 大小的向量，包含了当前batch中的序列长度
# Add CRF output layer
blstm_crf = BLSTM_CRF(embedded_chars=embedding,
hidden_unit=lstm_size,
cell_type=cell,
num_layers=num_layers,
dropout_rate=dropout_rate,
initializers=initializers,
num_labels=num_labels,
seq_length=max_seq_length,
labels=labels,
lengths=lengths,
is_training=is_training)
rst = blstm_crf.add_blstm_crf_layer(crf_only=False)
return rst
def create_model(bert_config,
is_training,
input_ids,
input_mask,
segment_ids,
labels,
num_labels,
use_one_hot_embeddings,
dropout_rate=1.0,
lstm_size=1,
cell='lstm',
num_layers=1):
"""Creates a classification model."""
import tensorflow as tf
model = modeling.BertModel(config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
# 获取对应的embedding 输入数据[batch_size, seq_length, embedding_size]
embedding = model.get_sequence_output()
max_seq_length = embedding.shape[1].value
# 算序列真实长度
used = tf.sign(tf.abs(input_ids))
lengths = tf.reduce_sum(
used, reduction_indices=1) # [batch_size] 大小的向量，包含了当前batch中的序列长度
# 添加CRF output layer
blstm_crf = BLSTM_CRF(embedded_chars=embedding,
hidden_unit=lstm_size,
cell_type=cell,
num_layers=num_layers,
dropout_rate=dropout_rate,
initializers=initializers,
num_labels=num_labels,
seq_length=max_seq_length,
labels=labels,
lengths=lengths,
is_training=is_training)
rst = blstm_crf.add_blstm_crf_layer(crf_only=False)
return rst
def create_bert_crf_model(bert_config, is_training, input_ids, input_mask,
segment_ids, labels, num_labels,crf_only, #use_one_hot_embeddings,
dropout_rate, lstm_size, cell, num_layers):
"""
创建X模型
:param bert_config: bert 配置
:param is_training:
:param input_ids: 数据的idx 表示
:param input_mask:
:param segment_ids:
:param labels: 标签的idx 表示
:param num_labels: 类别数量
:param use_one_hot_embeddings:
:return:
"""
# 使用数据加载BertModel,获取对应的字embedding
import tensorflow as tf
#from bert_base.bert import modeling
sys.path.append('../../../bert_base/bert')
import modeling
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=True,#use_one_hot_embeddings
)
# 获取对应的embedding 输入数据[batch_size, seq_length, embedding_size]
embedding = model.get_sequence_output()
max_seq_length = embedding.shape[1].value
# 算序列真实长度
used = tf.sign(tf.abs(input_ids))
lengths = tf.reduce_sum(used, reduction_indices=1) # [batch_size] 大小的向量，包含了当前batch中的序列长度
# 添加CRF output layer
blstm_crf = BLSTM_CRF(embedded_chars=embedding, hidden_unit=lstm_size, cell_type=cell, num_layers=num_layers,
dropout_rate=dropout_rate, initializers=initializers, num_labels=num_labels,
seq_length=max_seq_length, labels=labels, lengths=lengths, is_training=is_training)
rst = blstm_crf.add_blstm_crf_layer(crf_only=crf_only)
return rst
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
labels, num_labels, use_one_hot_embeddings):
dropout_rate = 1.0
lstm_size = 1
cell = 'lstm'
num_layers = 1
model = modeling.BertModel(config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
# 获取对应的embedding 输入数据[batch_size, seq_length, embedding_size]
embedding = model.get_sequence_output()
max_seq_leng = embedding.shape[1].value
# 算序列真实长度
used = tf.sign(tf.abs(input_ids))
lengths = tf.reduce_sum(
used, reduction_indices=1) # [batch_size] 大小的向量，包含了当前batch中的序列长度
# 添加CRF output layer
from tensorflow.contrib.layers.python.layers import initializers
from lstm_crf_layer import BLSTM_CRF
blstm_crf = BLSTM_CRF(embedded_chars=embedding,
hidden_unit=lstm_size,
cell_type=cell,
num_layers=num_layers,
dropout_rate=dropout_rate,
initializers=initializers,
num_labels=num_labels,
seq_length=max_seq_leng,
labels=labels,
lengths=lengths,
is_training=is_training)
loss, logits, trans, pred_ids = blstm_crf.add_blstm_crf_layer(
crf_only=True)
return loss, logits, trans, pred_ids
def model_fn(features, labels, mode, params):
#### Training or Evaluation
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
#### Get loss from inputs
#********************************************************************************************#
bsz_per_core = tf.shape(features["input_ids"])[0]
inp = tf.transpose(features["input_ids"], [1, 0])
seg_id = tf.transpose(features["segment_ids"], [1, 0])
inp_mask = tf.transpose(features["input_mask"], [1, 0])
label_ids = features["label_ids"]
xlnet_config = xlnet.XLNetConfig(json_path=FLAGS.model_config_path)
run_config = xlnet.create_run_config(is_training, True, FLAGS)
xlnet_model = xlnet.XLNetModel(xlnet_config=xlnet_config,
run_config=run_config,
input_ids=inp,
seg_ids=seg_id,
input_mask=inp_mask)
#summary = xlnet_model.get_pooled_out(FLAGS.summary_type, FLAGS.use_summ_proj)
# 获取对应的embedding 输入数据[batch_size, seq_length, embedding_size]
xlnet_model_out = xlnet_model.get_sequence_output()
embedding = tf.transpose(xlnet_model_out, [1, 0, 2])
max_seq_length = embedding.shape[1].value
# 算序列真实长度
used = tf.sign(tf.abs(features["input_ids"]))
lengths = tf.reduce_sum(
used, reduction_indices=1) # [batch_size] 大小的向量，包含了当前batch中的序列长度
# 添加CRF output layer
blstm_crf = BLSTM_CRF(embedded_chars=embedding,
hidden_unit=10,
cell_type="lstm",
num_layers=1,
dropout_rate=0.5,
initializers=initializers,
num_labels=n_class,
seq_length=max_seq_length,
labels=label_ids,
lengths=lengths,
is_training=is_training)
total_loss, logits, trans, pred_ids = blstm_crf.add_blstm_crf_layer(
crf_only=True)
#********************************************************************************************#
#### Check model parameters
num_params = sum([np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info('#params: {}'.format(num_params))
#### load pretrained models
scaffold_fn = model_utils.init_from_checkpoint(FLAGS)
#### Evaluation mode
if mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(label_ids, pred_ids):
return {
"eval_loss":
tf.metrics.mean_squared_error(labels=label_ids,
predictions=pred_ids),
}
eval_metrics = metric_fn(features["label_ids"], pred_ids)
eval_spec = tf.estimator.EstimatorSpec(
mode=mode, loss=total_loss, eval_metric_ops=eval_metrics)
return eval_spec
elif mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
"logits": logits,
"labels": label_ids,
"pred_ids": pred_ids,
"input_mask": features["input_mask"]
}
output_spec = tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions)
return output_spec
#### Configuring the optimizer
train_op, learning_rate, _ = model_utils.get_train_op(
FLAGS, total_loss)
monitor_dict = {}
monitor_dict["lr"] = learning_rate
#### Constucting training TPUEstimatorSpec with new cache.
train_spec = tf.estimator.EstimatorSpec(mode=mode,
loss=total_loss,
train_op=train_op)
return train_spec
