def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--task",
default="",
type=str,
required=True,
help="The GLUE task to run",
)
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
)
parser.add_argument(
"--overwrite_cache",
action="store_true",
help="Overwrite the cached training and evaluation sets")
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
# Add BART specific options
parser.add_argument(
"--max_source_length",
default=1024,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_target_length",
default=56,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the dataset files for the CNN/DM summarization task.",
)
parser.add_argument(
"--logging_dir",
default='tensorboard_logs',
type=str,
required=False,
help="The directory for tensorboard_logs",
)
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
add_generic_args(parser, root_dir)
parser.add_argument(
"--max_source_length",
default=128,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_target_length",
default=25,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--val_max_target_length",
default=25,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--test_max_target_length",
default=25,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_val", type=int, default=-1, required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
parser.add_argument("--label_smoothing", type=float, default=0.0, required=False)
parser.add_argument(
"--prefix",
type=str,
default=None,
help="Prefix added at the beginning of each text, typically used with T5-based models.",
)
parser.add_argument(
"--early_stopping_patience",
type=int,
default=-1,
required=False,
help="-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
)
parser.add_argument(
"--distributed-port", type=int, default=-1, required=False, help="Port number for distributed training."
)
parser.add_argument(
"--model_type",
choices=["rag_sequence", "rag_token", "bart", "t5"],
type=str,
help="RAG model type: sequence or token, if none specified, the type is inferred from the model_name_or_path",
)
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--task",
default="",
type=str,
required=True,
help="The GLUE task to run",
)
parser.add_argument(
"--gpus",
default=0,
type=int,
help=
"The number of GPUs allocated for this, it is by default 0 meaning none",
)
parser.add_argument(
"--overwrite_cache",
action="store_true",
help="Overwrite the cached training and evaluation sets")
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--gpus",
default=0,
type=int,
help="The number of GPUs allocated for this, it is by default 0 meaning none",
)
parser.add_argument(
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
)
def IntorFloat(string):
if '.' in string: return float(string)
else: return int(string)
parser.add_argument(
"--val_check_interval",
default=1.0,
type=IntorFloat, help="How often to check the validation set. Use float to check within a training epoch"
)
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
add_generic_args(parser, root_dir)
parser.add_argument(
"--max_source_length",
default=1024,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_target_length",
default=100,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--val_max_target_length",
default=100, # these defaults are optimized for CNNDM. For xsum, see README.md.
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--test_max_target_length",
default=100,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--freeze_encoder", action="store_true")
parser.add_argument("--freeze_embeds", action="store_true")
parser.add_argument("--sortish_sampler", action="store_true", default=False)
parser.add_argument("--max_tokens_per_batch", type=int, default=None)
parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_val", type=int, default=500, required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
parser.add_argument(
"--task", type=str, default="summarization", required=False, help="# examples. -1 means use all."
)
parser.add_argument("--label_smoothing", type=float, default=0.0, required=False)
parser.add_argument("--src_lang", type=str, default="", required=False)
parser.add_argument("--tgt_lang", type=str, default="", required=False)
parser.add_argument("--eval_beams", type=int, default=None, required=False)
parser.add_argument(
"--val_metric", type=str, default=None, required=False, choices=["bleu", "rouge2", "loss", None]
)
parser.add_argument("--eval_max_gen_length", type=int, default=None, help="never generate more than n tokens")
parser.add_argument("--length_penalty", type=float, default=1.0, help="never generate more than n tokens")
parser.add_argument("--save_top_k", type=int, default=1, required=False, help="How many checkpoints to save")
parser.add_argument(
"--early_stopping_patience",
type=int,
default=-1,
required=False,
help="-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
)
return parser
def add_model_specific_args(parser, root_dir):
# Add NER specific options
BaseTransformer.add_model_specific_args(parser, root_dir)
parser.add_argument(
"--task_type", default="NER", type=str, help="Task type to fine tune in training (e.g. NER, POS, etc)"
)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--labels",
default="",
type=str,
help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.",
)
parser.add_argument(
"--gpus",
default=0,
type=int,
help="The number of GPUs allocated for this, it is by default 0 meaning none",
)
parser.add_argument(
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
)
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
add_generic_args(parser, root_dir)
parser.add_argument(
"--max_source_length",
default=1024,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_target_length",
default=56,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--val_max_target_length",
default=142, # these defaults are optimized for CNNDM. For xsum, see README.md.
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--test_max_target_length",
default=142,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--data_dir",
type=str,
required=True,
help="The input data dir. Should contain train.source, train.target, val.source, val.target, test.source, test.target",
)
parser.add_argument("--freeze_encoder", action="store_true")
parser.add_argument("--freeze_embeds", action="store_true")
parser.add_argument("--sortish_sampler", action="store_true", default=False)
parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_val", type=int, default=500, required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
parser.add_argument(
"--task", type=str, default="summarization", required=False, help="# examples. -1 means use all."
)
parser.add_argument("--label_smoothing", type=float, default=0.0, required=False)
parser.add_argument("--src_lang", type=str, default="", required=False)
parser.add_argument("--tgt_lang", type=str, default="", required=False)
parser.add_argument(
"--early_stopping_patience",
type=int,
default=-1,
required=False,
help="-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
)
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
add_generic_args(parser, root_dir)
parser.add_argument(
"--max_source_length",
default=48,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_target_length",
default=24,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--val_max_target_length",
default=24,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--test_max_target_length",
default=24,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--data_dir",
type=str,
required=True,
help="The input data dir. Should contain train.source, train.target, val.source, val.target, test.source, test.target",
)
parser.add_argument("--freeze_encoder", action="store_true")
parser.add_argument("--freeze_embeds", action="store_true")
parser.add_argument("--sortish_sampler",
action="store_true", default=False)
parser.add_argument("--logger_name", type=str,
choices=["default", "wandb", "wandb_shared"], default="default")
parser.add_argument("--n_train", type=int, default=-1,
required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_val", type=int, default=500,
required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_test", type=int, default=-1,
required=False, help="# examples. -1 means use all.")
parser.add_argument(
"--task", type=str, default="summarization", required=False, help="# examples. -1 means use all."
)
parser.add_argument("--src_lang", type=str, default="", required=False)
parser.add_argument("--tgt_lang", type=str, default="", required=False)
parser.add_argument("--atomic", action="store_true")
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
# Add BART specific options
parser.add_argument(
"--max_source_length",
default=384,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_target_length",
default=512,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the dataset files for the CNN/DM summarization task.",
)
parser.add_argument(
"--early_stopping_patience",
type=int,
default=-1,
required=False,
help=
"-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
)
parser.add_argument(
"--checkpoint",
default=None,
type=str,
help="The checkpoint to initialize model",
)
parser.add_argument(
"--checkpoint_model",
default=None,
type=str,
help=
"The input data dir. Should contain the dataset files for the CNN/DM summarization task.",
)
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
add_generic_args(parser, root_dir)
# fmt: off
parser.add_argument(
"--max_source_length",
default=1024,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_target_length",
default=56,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--val_max_target_length",
default=142, # these defaults are optimized for CNNDM. For xsum, see README.md.
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--test_max_target_length",
default=142,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--data_dir",
type=str,
required=True,
help="The input data dir. Should contain train.source, train.target, val.source, val.target, test.source, test.target",
)
parser.add_argument("--freeze_encoder", action="store_true")
parser.add_argument("--freeze_embeds", action="store_true")
parser.add_argument("--sortish_sampler", action="store_true", default=False)
parser.add_argument("--logger", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
# parser.add_argument("--wandb_project", type=str, default="default")
parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_val", type=int, default=500, required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
# fmt: on
return parser
def parse_args():
parser = ArgumentParser()
# add some script specific args
parser.add_argument("--seed", type=int, default=42)
parser.add_argument(
"--output_dir", type=str, default="", help="Directory to write outputs to or load checkpoint from"
)
parser.add_argument("--do_train", action="store_true", help="Run training loop")
parser.add_argument("--do_predict", action="store_true", help="Run test loop")
# enable all trainer args
parser = Trainer.add_argparse_args(parser)
# add the base module args
parser = BaseTransformer.add_model_specific_args(parser)
# add the glue module args
parser = GLUETransformer.add_model_specific_args(parser)
# cook them all up :)
args = parser.parse_args()
return args
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
add_generic_args(parser, root_dir)
parser.add_argument(
"--max_source_length",
default=1024,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_target_length",
default=56,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--val_max_target_length",
default=
142, # these defaults are optimized for CNNDM. For xsum, see README.md.
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--test_max_target_length",
default=142,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--freeze_encoder", action="store_true")
parser.add_argument("--freeze_embeds", action="store_true")
parser.add_argument("--sortish_sampler",
action="store_true",
default=False)
parser.add_argument("--max_tokens_per_batch", type=int, default=None)
parser.add_argument("--logger_name",
type=str,
choices=["default", "wandb", "wandb_shared"],
default="default")
parser.add_argument("--n_train",
type=int,
default=-1,
required=False,
help="# examples. -1 means use all.")
parser.add_argument("--n_val",
type=int,
default=500,
required=False,
help="# examples. -1 means use all.")
parser.add_argument("--n_test",
type=int,
default=-1,
required=False,
help="# examples. -1 means use all.")
parser.add_argument("--task",
type=str,
default="summarization",
required=False,
help="# examples. -1 means use all.")
parser.add_argument("--label_smoothing",
type=float,
default=0.0,
required=False)
parser.add_argument("--src_lang", type=str, default="", required=False)
parser.add_argument("--tgt_lang", type=str, default="", required=False)
parser.add_argument(
"--eval_beams",
type=int,
default=None,
required=False,
help="# beams to use. 0 corresponds to not using beam search.")
parser.add_argument("--val_metric",
type=str,
default=None,
required=False,
choices=["bleu", "rouge2", "loss", None])
parser.add_argument("--eval_max_gen_length",
type=int,
default=None,
help="never generate more than n tokens")
parser.add_argument("--save_top_k",
type=int,
default=1,
required=False,
help="How many checkpoints to save")
parser.add_argument(
"--early_stopping_patience",
type=int,
default=-1,
required=False,
help=
"-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
)
parser.add_argument(
'--json-summary',
type=str,
default="results/dllogger.json",
help='If provided, the json summary will be written to'
'the specified file.')
parser.add_argument(
'--distill',
type=str,
default=None,
help=
"string indicating distillation to perform, only sft supported",
choices=["sft", None])
parser.add_argument(
'--layers',
type=str,
default=None,
help=
"string indicating which layers to distill for SFT, split by '-' (ex. 0-6-11)"
)
parser.add_argument('--do_encoder',
action="store_true",
default=False,
help="if true distills the encoder")
parser.add_argument('--do_decoder',
action="store_true",
default=False,
help="if true distills the decoder")
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
add_generic_args(parser, root_dir)
parser.add_argument(
"--max_source_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_target_length",
default=25,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--val_max_target_length",
default=25,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--test_max_target_length",
default=25,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--logger_name",
type=str,
choices=["default", "wandb", "wandb_shared"],
default="default")
parser.add_argument("--n_train",
type=int,
default=-1,
required=False,
help="# examples. -1 means use all.")
parser.add_argument("--n_val",
type=int,
default=-1,
required=False,
help="# examples. -1 means use all.")
parser.add_argument("--n_test",
type=int,
default=-1,
required=False,
help="# examples. -1 means use all.")
parser.add_argument("--label_smoothing",
type=float,
default=0.0,
required=False)
parser.add_argument(
"--prefix",
type=str,
default=None,
help=
"Prefix added at the beginning of each text, typically used with T5-based models.",
)
parser.add_argument(
"--early_stopping_patience",
type=int,
default=-1,
required=False,
help=
"-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
)
parser.add_argument("--distributed-port",
type=int,
default=-1,
required=False,
help="Port number for distributed training.")
parser.add_argument(
"--model_type",
choices=["rag_sequence", "rag_token", "bart", "t5"],
type=str,
help=
"RAG model type: sequence or token, if none specified, the type is inferred from the model_name_or_path",
)
parser.add_argument(
"--context_encoder_name",
default="facebook/dpr-ctx_encoder-multiset-base",
type=str,
help=
"Name of the pre-trained context encoder checkpoint from the DPR",
)
parser.add_argument(
"--csv_path",
default=str(
Path(__file__).parent / "test_run" / "dummy-kb" /
"my_knowledge_dataset.csv"),
type=str,
help="path of the raw KB csv",
)
parser.add_argument("--end2end",
action="store_true",
help="whether to train the system end2end or not")
parser.add_argument("--index_gpus",
type=int,
help="how many GPUs used in re-encoding process")
parser.add_argument(
"--shard_dir",
type=str,
default=str(Path(__file__).parent / "test_run" / "kb-shards"),
help=
"directory used to keep temporary shards during the re-encode process",
)
parser.add_argument(
"--gpu_order",
type=str,
help=
"order of the GPU used during the fine-tuning. Used to finding free GPUs during the re-encode process. I do not have many GPUs :)",
)
parser.add_argument("--indexing_freq",
type=int,
help="frequency of re-encode process")
return parser
def add_model_specific_args(parser, root_dir):
BaseTransformer.add_model_specific_args(parser, root_dir)
add_generic_args(parser, root_dir)
parser.add_argument(
"--max_source_length",
default=1024,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument(
"--max_target_length",
default=56,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--freeze_encoder", action="store_true")
parser.add_argument("--freeze_embeds", action="store_true")
parser.add_argument("--sortish_sampler", action="store_true", default=False)
parser.add_argument("--max_tokens_per_batch", type=int, default=None)
parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_val", type=int, default=500, required=False, help="# examples. -1 means use all.")
parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
parser.add_argument(
"--task", type=str, default="summarization", required=False, help="# examples. -1 means use all."
)
parser.add_argument("--label_smoothing", type=float, default=0.0, required=False)
parser.add_argument("--src_lang", type=str, default="", required=False)
parser.add_argument("--tgt_lang", type=str, default="", required=False)
parser.add_argument("--eval_beams", type=int, default=None, required=False)
parser.add_argument(
"--val_metric", type=str, default=None, required=False, choices=["bleu", "rouge2", "loss", None]
)
parser.add_argument("--eval_max_gen_length", type=int, default=None, help="never generate more than n tokens")
parser.add_argument(
"--early_stopping_patience",
type=int,
default=-1,
required=False,
help="-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
)
####################################
## Decoding Strategy ##
####################################
parser.add_argument('--decode_method', choices=['greedy', 'beam', 'nucleus'])
parser.add_argument("--decode_num_beams", default=5, type=int, required=False, help="")
parser.add_argument("--decode_p", default=0.9, type=float, required=False, help="")
####################################
## Unlikelihood Loss Parameters ##
####################################
parser.add_argument("--unlikelihood_training", action="store_true", help="whether to use unlikelihood training")
parser.add_argument("--unlikelihood_training_mode", choices=["cochrane", "newsela", "both"], help="which weights to use for unlikelihood training")
parser.add_argument("--unlikelihood_cochrane_weights_file",
default="data/logr_weights/bart_freq_normalized_ids.txt",
type=str, required=False,
help="The file containing logistic regression weights learned on the Cochrane dataset for use in unlikelihood training")
parser.add_argument("--unlikelihood_newsela_weights_file",
default="data/logr_weights/bart_freq_newsela_ids.txt",
type=str, required=False,
help="The file containing logistic regression weights learned on the Newsela dataset for use in unlikelihood training")
parser.add_argument("--unlikelihood_exclude_tokens", default="", type=str, required=False, help="Comma-separated numbers")
parser.add_argument("--unlikelihood_num_weights", default=100, type=int, required=False, help="The number of weights in unlikelihood training, if -1 use all of them")
parser.add_argument("--unlikelihood_softmax", action="store_true", help="whether to softmax the token weights in unlikelihood training")
parser.add_argument("--unlikelihood_temperature", default=2, type=int, help="temperature to use in softmax when normalizing logistic regression weights")
parser.add_argument("--unlikelihood_selective_penalty", action="store_true", help="whether to use unlikelihood loss only if argmax is the penalty token")
parser.add_argument("--unlikelihood_alpha", default=100.0, type=float, required=False, help="")
##########################################
## Unlikelihood Loss Token Parameters ##
##########################################
parser.add_argument("--unlikelihood_training_tokens", action="store_true", help="whether to use unlikelihood training at token level")
parser.add_argument("--unlikelihood_tokens_alpha", default=1.0, type=float, required=False, help="")
return parser
