text_classifier.py

#coding:utf-8
#   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=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--dataset", type=str, default="chnsenticorp", help="The choice of dataset")
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.")
parser.add_argument("--use_pyreader", type=ast.literal_eval, default=False, help="Whether use pyreader to feed data.")
parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
parser.add_argument("--use_taskid", type=ast.literal_eval, default=False, help="Whether to use taskid ,if yes to use ernie v2.")
args = parser.parse_args()
# yapf: enable.

if __name__ == '__main__':
    dataset = None
    metrics_choices = []
    # Download dataset and use ClassifyReader to read dataset
    if args.dataset.lower() == "chnsenticorp":
        dataset = hub.dataset.ChnSentiCorp()
        module = hub.Module(name="ernie")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "nlpcc_dbqa":
        dataset = hub.dataset.NLPCC_DBQA()
        module = hub.Module(name="ernie")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "lcqmc":
        dataset = hub.dataset.LCQMC()
        module = hub.Module(name="ernie")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "mrpc":
        dataset = hub.dataset.GLUE("MRPC")
        if args.use_taskid:
            module = hub.Module(name="ernie_v2_eng_base")
        else:
            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
        metrics_choices = ["f1", "acc"]
    # The first metric will be choose to eval. Ref: task.py:799
    elif args.dataset.lower() == "qqp":
        dataset = hub.dataset.GLUE("QQP")
        if args.use_taskid:
            module = hub.Module(name="ernie_v2_eng_base")
        else:
            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
        metrics_choices = ["f1", "acc"]
    elif args.dataset.lower() == "sst-2":
        dataset = hub.dataset.GLUE("SST-2")
        if args.use_taskid:
            module = hub.Module(name="ernie_v2_eng_base")
        else:
            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "cola":
        dataset = hub.dataset.GLUE("CoLA")
        if args.use_taskid:
            module = hub.Module(name="ernie_v2_eng_base")
        else:
            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
        metrics_choices = ["matthews", "acc"]
    elif args.dataset.lower() == "qnli":
        dataset = hub.dataset.GLUE("QNLI")
        if args.use_taskid:
            module = hub.Module(name="ernie_v2_eng_base")
        else:
            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "rte":
        dataset = hub.dataset.GLUE("RTE")
        if args.use_taskid:
            module = hub.Module(name="ernie_v2_eng_base")
        else:
            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "mnli" or args.dataset.lower() == "mnli":
        dataset = hub.dataset.GLUE("MNLI_m")
        if args.use_taskid:
            module = hub.Module(name="ernie_v2_eng_base")
        else:
            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
        metrics_choices = ["acc"]
    elif args.dataset.lower() == "mnli_mm":
        dataset = hub.dataset.GLUE("MNLI_mm")
        if args.use_taskid:
            module = hub.Module(name="ernie_v2_eng_base")
        else:
            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
        metrics_choices = ["acc"]
    elif args.dataset.lower().startswith("xnli"):
        dataset = hub.dataset.XNLI(language=args.dataset.lower()[-2:])
        module = hub.Module(name="bert_multi_cased_L-12_H-768_A-12")
        metrics_choices = ["acc"]
    else:
        raise ValueError("%s dataset is not defined" % args.dataset)

    support_metrics = ["acc", "f1", "matthews"]
    for metric in metrics_choices:
        if metric not in support_metrics:
            raise ValueError("\"%s\" metric is not defined" % metric)

    inputs, outputs, program = module.context(
        trainable=True, max_seq_len=args.max_seq_len)
    reader = hub.reader.ClassifyReader(
        dataset=dataset,
        vocab_path=module.get_vocab_path(),
        max_seq_len=args.max_seq_len,
        use_task_id=args.use_taskid)

    # 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"]

    # 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,
    ]

    if args.use_taskid:
        feed_list.append(inputs["task_ids"].name)

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

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

    # Define a classfication finetune task by PaddleHub's API
    cls_task = hub.TextClassifierTask(
        data_reader=reader,
        feature=pooled_output,
        feed_list=feed_list,
        num_classes=dataset.num_labels,
        config=config,
        metrics_choices=metrics_choices)

    # Finetune and evaluate by PaddleHub's API
    # will finish training, evaluation, testing, save model automatically
    cls_task.finetune_and_eval()