5175b7e69c74b13405afa26761d79a21ff1621ef,official/bert/run_squad.py,,,#,40

Before Change

flags.DEFINE_bool("do_predict", False, "Whether to run eval on the dev set.")
flags.DEFINE_string("train_data_path", "",
                    "Training data path with train tfrecords.")
flags.DEFINE_string("bert_config_file", None,
                    "Bert configuration file to define core bert layers.")
flags.DEFINE_string(
    "model_dir", None,
    ("The directory where the model weights and training/evaluation summaries "
     "are stored."))
flags.DEFINE_string(
    "input_meta_data_path", None,
    "Path to file that contains meta data about input "
    "to be used for training and evaluation.")
flags.DEFINE_string("tpu", "", "TPU address to connect to.")
flags.DEFINE_string(
    "init_checkpoint", None,
    "Initial checkpoint (usually from a pre-trained BERT model).")
flags.DEFINE_enum(
    "strategy_type", "mirror", ["tpu", "mirror"],
    "Distribution Strategy type to use for training. `tpu` uses "
    "TPUStrategy for running on TPUs, `mirror` uses GPUs with "
    "single host.")
// Model training specific flags.
flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
flags.DEFINE_integer("num_train_epochs", 3,
                     "Total number of training epochs to perform.")
flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")

// Predict processing related.
flags.DEFINE_string("predict_file", None,
                    "Prediction data path with train tfrecords.")

After Change

    "The maximum length of an answer that can be generated. This is needed "
    "because the start and end predictions are not conditioned on one another.")

common_flags.define_common_bert_flags()

FLAGS = flags.FLAGS

Instances