Convert flags to argparse in `run_classifier_pytorch.py`

run_classifier_pytorch.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# 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.
+"""BERT finetuning runner."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# import csv
+# import os
+# import modeling_pytorch
+# import optimization
+# import tokenization
+
+import argparse
+
+parser = argparse.ArgumentParser()
+
+## Required parameters
+parser.add_argument("--data_dir",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "The input data dir. Should contain the .tsv files (or other data files) for the task.")
+parser.add_argument("--bert_config_file",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "The config json file corresponding to the pre-trained BERT model. \n"
+                        "This specifies the model architecture.")
+parser.add_argument("--task_name",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "The name of the task to train.")
+parser.add_argument("--vocab_file",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "The vocabulary file that the BERT model was trained on.")                    
+parser.add_argument("--output_dir",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "The output directory where the model checkpoints will be written.")                   
+
+## Other parameters
+parser.add_argument("--init_checkpoint",
+                    default = None,
+                    type = str,
+                    help = "Initial checkpoint (usually from a pre-trained BERT model).")
+parser.add_argument("--do_lower_case",
+                    default = True,
+                    type = bool,
+                    help = "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
+parser.add_argument("--max_seq_length",
+                    default = 128,
+                    type = int,
+                    help = "The maximum total input sequence length after WordPiece tokenization. \n"
+                        "Sequences longer than this will be truncated, and sequences shorter \n"
+                        "than this will be padded.")
+parser.add_argument("--do_train",
+                    default = False,
+                    type = bool,
+                    help = "Whether to run training.")
+parser.add_argument("--do_eval",
+                    default = False,
+                    type = bool,
+                    help = "Whether to run eval on the dev set.")                                             
+parser.add_argument("--train_batch_size",
+                    default = 32,
+                    type = int,
+                    help = "Total batch size for training.")
+parser.add_argument("--eval_batch_size",
+                    default = 8,
+                    type = int,
+                    help = "Total batch size for eval.")
+parser.add_argument("--learning_rate",
+                    default = 5e-5,
+                    type = float,
+                    help = "The initial learning rate for Adam.")                                       
+parser.add_argument("--num_train_epochs",
+                    default = 3.0,
+                    type = float,
+                    help = "Total number of training epochs to perform.")                    
+parser.add_argument("--warmup_proportion",
+                    default = 0.1,
+                    type = float,
+                    help = "Proportion of training to perform linear learning rate warmup for. "
+                        "E.g., 0.1 = 10%% of training.")
+parser.add_argument("--save_checkpoints_steps",
+                    default = 1000,
+                    type = int,
+                    help = "How often to save the model checkpoint.")                    
+parser.add_argument("--iterations_per_loop",
+                    default = 1000,
+                    type = int,
+                    help = "How many steps to make in each estimator call.")
+                    
+### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###                   
+parser.add_argument("--use_tpu",
+                    default = False,
+                    type = bool,
+                    help = "Whether to use TPU or GPU/CPU.") 
+parser.add_argument("--tpu_name",
+                    default = None,
+                    type = str,
+                    help = "The Cloud TPU to use for training. This should be either the name "
+                        "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
+                        "url.")
+parser.add_argument("--tpu_zone",
+                    default = None,
+                    type = str,
+                    help = "[Optional] GCE zone where the Cloud TPU is located in. If not "
+                        "specified, we will attempt to automatically detect the GCE project from "
+                        "metadata.")                    
+parser.add_argument("--gcp_project",
+                    default = None,
+                    type = str,
+                    help = "[Optional] Project name for the Cloud TPU-enabled project. If not "
+                        "specified, we will attempt to automatically detect the GCE project from "
+                        "metadata.")                     
+parser.add_argument("--master",
+                    default = None,
+                    type = str,
+                    help = "[Optional] TensorFlow master URL.")                                                    
+parser.add_argument("--num_tpu_cores",
+                    default = 8,
+                    type = int,
+                    help = "Only used if `use_tpu` is True. Total number of TPU cores to use.")
+### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ### 
+  
+args = parser.parse_args()
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