text_classifier.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 """

import argparse
import ast

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("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--dataset", type=str, default="chnsenticorp", help="Directory to model checkpoint", choices=["chnsenticorp", "nlpcc_dbqa", "lcqmc"])
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--warmup_proportion", type=float, default=0.0, help="Warmup proportion params for warmup strategy")
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__':
    # Step1: load Paddlehub ERNIE pretrained model
    module = hub.Module(name="ernie")
    # module = hub.Module(name="bert_multi_cased_L-12_H-768_A-12")
    inputs, outputs, program = module.context(
        trainable=True, max_seq_len=args.max_seq_len)

    # Step2: Download dataset and use ClassifyReader to read dataset
    dataset = None
    if args.dataset.lower() == "chnsenticorp":
        dataset = hub.dataset.ChnSentiCorp()
    elif args.dataset.lower() == "nlpcc_dbqa":
        dataset = hub.dataset.NLPCC_DBQA()
    elif args.dataset.lower() == "lcqmc":
        dataset = hub.dataset.LCQMC()
    else:
        raise ValueError("%s dataset is not defined" % args.dataset)

    reader = hub.reader.ClassifyReader(
        dataset=dataset,
        vocab_path=module.get_vocab_path(),
        max_seq_len=args.max_seq_len)

    # Step3: construct transfer learning network
    # Use "pooled_output" for classification tasks on an entire sentence.
    # Use "sequence_output" for token-level output.
    pooled_output = outputs["pooled_output"]

    # Define a classfication finetune task by PaddleHub's API
    cls_task = hub.create_text_cls_task(
        feature=pooled_output, num_classes=dataset.num_labels)

    # Setup feed list for data feeder
    # Must feed all the tensor of ERNIE's module need
    feed_list = [
        inputs["input_ids"].name, inputs["position_ids"].name,
        inputs["segment_ids"].name, inputs["input_mask"].name,
        cls_task.variable('label').name
    ]

    # Step4: Select finetune strategy, setup config and finetune
    strategy = hub.AdamWeightDecayStrategy(
        weight_decay=args.weight_decay,
        learning_rate=args.learning_rate,
        lr_scheduler="linear_warmup_decay",
    )

    # Setup runing config for PaddleHub Finetune API
    config = hub.RunConfig(
        use_cuda=args.use_gpu,
        num_epoch=args.num_epoch,
        batch_size=args.batch_size,
        checkpoint_dir=args.checkpoint_dir,
        strategy=strategy)

    # 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)