# 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 __future__ import absolute_import from __future__ import division from __future__ import print_function import os import time import argparse from utils.args import ArgumentGroup # yapf: disable parser = argparse.ArgumentParser(__doc__) model_g = ArgumentGroup(parser, "model", "model configuration and paths.") model_g.add_arg("ernie_config_path", str, None, "Path to the json file for ernie model config.") model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.") model_g.add_arg("init_pretraining_params", str, None, "Init pre-training params which preforms fine-tuning from. If the " "arg 'init_checkpoint' has been set, this argument wouldn't be valid.") model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints.") model_g.add_arg("is_classify", bool, True, "is_classify") model_g.add_arg("is_regression", bool, False, "is_regression") model_g.add_arg("task_id", int, 0, "task id") train_g = ArgumentGroup(parser, "training", "training options.") train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.") train_g.add_arg("learning_rate", float, 5e-5, "Learning rate used to train with warmup.") train_g.add_arg("lr_scheduler", str, "linear_warmup_decay", "scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay']) train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.") train_g.add_arg("warmup_proportion", float, 0.1, "Proportion of training steps to perform linear learning rate warmup for.") train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.") train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.") train_g.add_arg("use_fp16", bool, False, "Whether to use fp16 mixed precision training.") train_g.add_arg("loss_scaling", float, 1.0, "Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled.") train_g.add_arg("test_save", str, "test_result", "test_save") train_g.add_arg("metric", str, "simple_accuracy", "metric") log_g = ArgumentGroup(parser, "logging", "logging related.") log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.") log_g.add_arg("verbose", bool, False, "Whether to output verbose log.") data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options") data_g.add_arg("tokenizer", str, "FullTokenizer", "ATTENTION: the INPUT must be splited by Word with blank while using SentencepieceTokenizer or WordsegTokenizer") data_g.add_arg("train_set", str, None, "Path to training data.") data_g.add_arg("test_set", str, None, "Path to test data.") data_g.add_arg("dev_set", str, None, "Path to validation data.") data_g.add_arg("vocab_path", str, None, "Vocabulary path.") data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.") data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training. see also --in_tokens.") data_g.add_arg("predict_batch_size", int, None, "Total examples' number in batch for predict. see also --in_tokens.") data_g.add_arg("in_tokens", bool, False, "If set, the batch size will be the maximum number of tokens in one batch. " "Otherwise, it will be the maximum number of examples in one batch.") data_g.add_arg("do_lower_case", bool, True, "Whether to lower case the input text. Should be True for uncased models and False for cased models.") data_g.add_arg("random_seed", int, None, "Random seed.") data_g.add_arg("label_map_config", str, None, "label_map_path.") data_g.add_arg("num_labels", int, 2, "label number") data_g.add_arg("diagnostic", str, None, "GLUE Diagnostic Dataset") data_g.add_arg("diagnostic_save", str, None, "GLUE Diagnostic save f") data_g.add_arg("max_query_length", int, 64, "Max query length.") data_g.add_arg("max_answer_length", int, 100, "Max answer length.") data_g.add_arg("doc_stride", int, 128, "When splitting up a long document into chunks, how much stride to take between chunks.") data_g.add_arg("n_best_size", int, 20, "The total number of n-best predictions to generate in the nbest_predictions.json output file.") data_g.add_arg("chunk_scheme", type=str, default="IOB", choices=["IO", "IOB", "IOE", "IOBES"], help="chunk scheme") run_type_g = ArgumentGroup(parser, "run_type", "running type options.") run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.") run_type_g.add_arg("use_fast_executor", bool, False, "If set, use fast parallel executor (in experiment).") run_type_g.add_arg("num_iteration_per_drop_scope", int, 10, "Iteration intervals to drop scope.") run_type_g.add_arg("do_train", bool, True, "Whether to perform training.") run_type_g.add_arg("do_val", bool, True, "Whether to perform evaluation on dev data set.") run_type_g.add_arg("do_test", bool, True, "Whether to perform evaluation on test data set.") run_type_g.add_arg("use_multi_gpu_test", bool, False, "Whether to perform evaluation using multiple gpu cards") run_type_g.add_arg("metrics", bool, True, "Whether to perform evaluation on test data set.") run_type_g.add_arg("shuffle", bool, True, "") run_type_g.add_arg("for_cn", bool, True, "model train for cn or for other langs.") parser.add_argument("--enable_ce", action='store_true', help="The flag indicating whether to run the task for continuous evaluation.") # yapf: enable