add more ernie classfication task and dataset

demo/ernie-classification/question_answering.py

+#   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)

demo/ernie-classification/question_matching.py

+#   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)

demo/ernie-classification/run_question_answering.sh

+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

demo/ernie-classification/run_question_matching.sh

+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

demo/ernie-classification/run_fintune_with_hub.sh → demo/ernie-classification/run_sentiment_cls.sh

 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 \

demo/ernie-classification/finetune_with_hub.py → demo/ernie-classification/sentiment_cls.py

@@ -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

paddlehub/dataset/__init__.py

@@ -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

paddlehub/dataset/chnsenticorp.py

@@ -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))

paddlehub/dataset/dataset.py

@@ -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()

paddlehub/dataset/lcqmc.py

+#   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))

paddlehub/dataset/msra_ner.py

@@ -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()

paddlehub/dataset/nlpcc_dbqa.py

+#   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))

paddlehub/finetune/evaluate.py

@@ -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

paddlehub/reader/nlp_reader.py

@@ -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