提交 dae2ef9b 编写于 作者: Z Zeyu Chen

add more ernie classfication task and dataset

上级 a86073f0
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import argparse
import numpy as np
import paddle
import paddle.fluid as fluid
import paddlehub as hub
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--hub_module_dir", type=str, default=None, help="PaddleHub module directory")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--data_dir", type=str, default=None, help="Path to training data.")
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.")
args = parser.parse_args()
# yapf: enable.
if __name__ == '__main__':
# Select a finetune strategy
strategy = hub.BERTFinetuneStrategy(
weight_decay=args.weight_decay,
learning_rate=args.learning_rate,
warmup_strategy="linear_warmup_decay",
)
# Setup runing config for PaddleHub Finetune API
config = hub.RunConfig(
eval_interval=100,
use_cuda=True,
num_epoch=args.num_epoch,
batch_size=args.batch_size,
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# loading Paddlehub ERNIE pretrained model
module = hub.Module(name="ernie")
# Sentence classification dataset reader
reader = hub.reader.ClassifyReader(
dataset=hub.dataset.NLPCC_DBQA(), # download NLPCC_DBQA dataset
vocab_path=module.get_vocab_path(),
max_seq_len=args.max_seq_len)
num_labels = len(reader.get_labels())
input_dict, output_dict, program = module.context(
sign_name="tokens", trainable=True, max_seq_len=args.max_seq_len)
with fluid.program_guard(program):
label = fluid.layers.data(name="label", shape=[1], dtype='int64')
# Use "pooled_output" for classification tasks on an entire sentence.
# Use "sequence_outputs" for token-level output.
pooled_output = output_dict["pooled_output"]
# Setup feed list for data feeder
# Must feed all the tensor of ERNIE's module need
feed_list = [
input_dict["input_ids"].name, input_dict["position_ids"].name,
input_dict["segment_ids"].name, input_dict["input_mask"].name,
label.name
]
# Define a classfication finetune task by PaddleHub's API
cls_task = hub.create_text_classification_task(
pooled_output, label, num_classes=num_labels)
# Finetune and evaluate by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
hub.finetune_and_eval(
task=cls_task,
data_reader=reader,
feed_list=feed_list,
config=config)
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import argparse
import numpy as np
import paddle
import paddle.fluid as fluid
import paddlehub as hub
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--hub_module_dir", type=str, default=None, help="PaddleHub module directory")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--data_dir", type=str, default=None, help="Path to training data.")
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.")
args = parser.parse_args()
# yapf: enable.
if __name__ == '__main__':
# Select a finetune strategy
strategy = hub.BERTFinetuneStrategy(
weight_decay=args.weight_decay,
learning_rate=args.learning_rate,
warmup_strategy="linear_warmup_decay",
)
# Setup runing config for PaddleHub Finetune API
config = hub.RunConfig(
eval_interval=100,
use_cuda=True,
num_epoch=args.num_epoch,
batch_size=args.batch_size,
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# loading Paddlehub ERNIE pretrained model
module = hub.Module(name="ernie")
# Sentence classification dataset reader
reader = hub.reader.ClassifyReader(
dataset=hub.dataset.LCQMC(), # download LCQMC dataset
vocab_path=module.get_vocab_path(),
max_seq_len=args.max_seq_len)
num_labels = len(reader.get_labels())
input_dict, output_dict, program = module.context(
sign_name="tokens", trainable=True, max_seq_len=args.max_seq_len)
with fluid.program_guard(program):
label = fluid.layers.data(name="label", shape=[1], dtype='int64')
# Use "pooled_output" for classification tasks on an entire sentence.
# Use "sequence_outputs" for token-level output.
pooled_output = output_dict["pooled_output"]
# Setup feed list for data feeder
# Must feed all the tensor of ERNIE's module need
feed_list = [
input_dict["input_ids"].name, input_dict["position_ids"].name,
input_dict["segment_ids"].name, input_dict["input_mask"].name,
label.name
]
# Define a classfication finetune task by PaddleHub's API
cls_task = hub.create_text_classification_task(
pooled_output, label, num_classes=num_labels)
# Finetune and evaluate by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
hub.finetune_and_eval(
task=cls_task,
data_reader=reader,
feed_list=feed_list,
config=config)
export CUDA_VISIBLE_DEVICES=3
CKPT_DIR="./ckpt_dbqa"
python -u question_answering.py \
--batch_size 8 \
--weight_decay 0.01 \
--checkpoint_dir $CKPT_DIR \
--num_epoch 3 \
--max_seq_len 512 \
--learning_rate 2e-5
export CUDA_VISIBLE_DEVICES=0
CKPT_DIR="./ckpt_question_matching"
python -u sentiment_cls.py \
--batch_size 32 \
--weight_decay 0.00 \
--checkpoint_dir $CKPT_DIR \
--num_epoch 3 \
--max_seq_len 128 \
--learning_rate 2e-5
export CUDA_VISIBLE_DEVICES=3
CKPT_DIR="./ckpt"
python -u finetune_with_hub.py \
CKPT_DIR="./ckpt_sentiment_cls"
python -u sentiment_cls.py \
--batch_size 32 \
--weight_decay 0.01 \
--checkpoint_dir $CKPT_DIR \
......
......@@ -49,10 +49,11 @@ if __name__ == '__main__':
# Setup runing config for PaddleHub Finetune API
config = hub.RunConfig(
eval_interval=10,
eval_interval=100,
use_cuda=True,
num_epoch=args.num_epoch,
batch_size=args.batch_size,
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# loading Paddlehub ERNIE pretrained model
......
......@@ -15,5 +15,6 @@
from .dataset import InputExample, HubDataset
from .chnsenticorp import ChnSentiCorp
from .msra_ner import MSRA_NER
from .nlpcc_dbqa import NLPCC_DBQA
from .dogcat import DogCatDataset as DogCat
from .flowers import FlowersDataset as Flowers
......@@ -16,12 +16,12 @@ from collections import namedtuple
import os
import csv
from paddlehub.dataset import InputExample
from paddlehub.dataset import HubDataset
from paddlehub.dataset import InputExample, HubDataset
from paddlehub.common.downloader import default_downloader
from paddlehub.common.dir import DATA_HOME
from paddlehub.common.logger import logger
DATA_URL = "https://paddlehub-dataset.bj.bcebos.com/chnsenticorp_data.tar.gz"
DATA_URL = "https://paddlehub-dataset.bj.bcebos.com/chnsenticorp.tar.gz"
class ChnSentiCorp(HubDataset):
......@@ -31,8 +31,12 @@ class ChnSentiCorp(HubDataset):
"""
def __init__(self):
self.dataset_dir = os.path.join(DATA_HOME, "chnsenticorp")
if not os.path.exists(self.dataset_dir):
ret, tips, self.dataset_dir = default_downloader.download_file_and_uncompress(
url=DATA_URL, save_path=DATA_HOME, print_progress=True)
else:
logger.info("Dataset {} already cached.".format(self.dataset_dir))
self._load_train_examples()
self._load_test_examples()
......@@ -69,6 +73,7 @@ class ChnSentiCorp(HubDataset):
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
examples = []
seq_id = 0
header = next(reader) # skip header
for line in reader:
example = InputExample(
guid=seq_id, label=line[0], text_a=line[1])
......@@ -81,4 +86,4 @@ class ChnSentiCorp(HubDataset):
if __name__ == "__main__":
ds = ChnSentiCorp()
for e in ds.get_train_examples():
print(e)
print("{}\t{}\t{}\t{}".format(e.guid, e.text_a, e.text_b, e.label))
......@@ -13,6 +13,30 @@
# limitations under the License.
class InputExample(object):
"""
Input data structure of BERT/ERNIE, can satisfy single sequence task like
text classification, sequence lableing; Sequence pair task like dialog
task.
"""
def __init__(self, guid, text_a, text_b=None, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
class HubDataset(object):
def get_train_examples(self):
raise NotImplementedError()
......
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import namedtuple
import os
import csv
from paddlehub.dataset import InputExample, HubDataset
from paddlehub.common.downloader import default_downloader
from paddlehub.common.dir import DATA_HOME
from paddlehub.common.logger import logger
DATA_URL = "https://paddlehub-dataset.bj.bcebos.com/lcqmc.tar.gz"
class NLPCC_DBQA(HubDataset):
def __init__(self):
self.dataset_dir = os.path.join(DATA_HOME, "lcqmc")
if not os.path.exists(self.dataset_dir):
ret, tips, self.dataset_dir = default_downloader.download_file_and_uncompress(
url=DATA_URL, save_path=DATA_HOME, print_progress=True)
else:
logger.info("Dataset {} already cached.".format(self.dataset_dir))
self._load_train_examples()
self._load_test_examples()
self._load_dev_examples()
def _load_train_examples(self):
self.train_file = os.path.join(self.dataset_dir, "train.tsv")
self.train_examples = self._read_tsv(self.train_file)
def _load_dev_examples(self):
self.dev_file = os.path.join(self.dataset_dir, "dev.tsv")
self.dev_examples = self._read_tsv(self.dev_file)
def _load_test_examples(self):
self.test_file = os.path.join(self.dataset_dir, "test.tsv")
self.test_examples = self._read_tsv(self.test_file)
def get_train_examples(self):
return self.train_examples
def get_dev_examples(self):
return self.dev_examples
def get_test_examples(self):
return self.test_examples
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _read_tsv(self, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
examples = []
seq_id = 0
header = next(reader) # skip header
for line in reader:
example = InputExample(
guid=seq_id, label=line[2], text_a=line[0], text_b=line[1])
seq_id += 1
examples.append(example)
return examples
if __name__ == "__main__":
ds = NLPCC_DBQA()
for e in ds.get_train_examples():
print("{}\t{}\t{}\t{}".format(e.guid, e.text_a, e.text_b, e.label))
......@@ -19,14 +19,19 @@ from collections import namedtuple
from paddlehub.common.downloader import default_downloader
from paddlehub.common.dir import DATA_HOME
from paddlehub.common.logger import logger
DATA_URL = "https://paddlehub-dataset.bj.bcebos.com/msra_ner.tar.gz"
class MSRA_NER(object):
def __init__(self):
self.dataset_dir = os.path.join(DATA_HOME, "msra_ner")
if not os.path.exists(self.dataset_dir):
ret, tips, self.dataset_dir = default_downloader.download_file_and_uncompress(
url=DATA_URL, save_path=DATA_HOME, print_progress=True)
else:
logger.info("Dataset {} already cached.".format(self.dataset_dir))
self._load_label_map()
self._load_train_examples()
......
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import namedtuple
import os
import csv
from paddlehub.dataset import InputExample, HubDataset
from paddlehub.common.downloader import default_downloader
from paddlehub.common.dir import DATA_HOME
from paddlehub.common.logger import logger
DATA_URL = "https://paddlehub-dataset.bj.bcebos.com/nlpcc-dbqa.tar.gz"
class NLPCC_DBQA(HubDataset):
def __init__(self):
self.dataset_dir = os.path.join(DATA_HOME, "nlpcc-dbqa")
if not os.path.exists(self.dataset_dir):
ret, tips, self.dataset_dir = default_downloader.download_file_and_uncompress(
url=DATA_URL, save_path=DATA_HOME, print_progress=True)
else:
logger.info("Dataset {} already cached.".format(self.dataset_dir))
self._load_train_examples()
self._load_test_examples()
self._load_dev_examples()
def _load_train_examples(self):
self.train_file = os.path.join(self.dataset_dir, "train.tsv")
self.train_examples = self._read_tsv(self.train_file)
def _load_dev_examples(self):
self.dev_file = os.path.join(self.dataset_dir, "dev.tsv")
self.dev_examples = self._read_tsv(self.dev_file)
def _load_test_examples(self):
self.test_file = os.path.join(self.dataset_dir, "test.tsv")
self.test_examples = self._read_tsv(self.test_file)
def get_train_examples(self):
return self.train_examples
def get_dev_examples(self):
return self.dev_examples
def get_test_examples(self):
return self.test_examples
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _read_tsv(self, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
examples = []
seq_id = 0
header = next(reader) # skip header
for line in reader:
example = InputExample(
guid=seq_id, label=line[3], text_a=line[1], text_b=line[2])
seq_id += 1
examples.append(example)
return examples
if __name__ == "__main__":
ds = NLPCC_DBQA()
for e in ds.get_train_examples():
print("{}\t{}\t{}\t{}".format(e.guid, e.text_a, e.text_b, e.label))
......@@ -48,6 +48,8 @@ def evaluate_cls_task(task, data_reader, feed_list, phase="test", config=None):
feed=data_feeder.feed(batch),
fetch_list=[loss.name, accuracy.name])
num_eval_examples += num_batch_examples
if num_eval_examples % 10000 == 0:
logger.info("{} examples evaluated.".format(num_eval_examples))
acc_sum += accuracy_v * num_batch_examples
loss_sum += loss_v * num_batch_examples
eval_time_used = time.time() - eval_time_begin
......
......@@ -18,6 +18,7 @@ import numpy as np
from collections import namedtuple
from paddlehub.reader import tokenization
from paddlehub.common.logger import logger
from .batching import pad_batch_data
......@@ -46,7 +47,7 @@ class BaseReader(object):
self.label_map = {}
for index, label in enumerate(self.dataset.get_labels()):
self.label_map[label] = index
print("Dataset label map = {}".format(self.label_map))
logger.info("Dataset label map = {}".format(self.label_map))
self.current_example = 0
self.current_epoch = 0
......@@ -154,6 +155,9 @@ class BaseReader(object):
position_ids = list(range(len(token_ids)))
if self.label_map:
if example.label not in self.label_map:
raise KeyError(
"example.label = {%s} not in label" % example.label)
label_id = self.label_map[example.label]
else:
label_id = example.label
......
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