#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() == "tnews": dataset = hub.dataset.TNews() module = hub.Module(name="ernie") metrics_choices = ["acc", "f1"] 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()