tfr.extension.premade.TFRBertDataConfig

Data config for TFR-BERT task.

Inherits From: RankingDataConfig

tfr.extension.premade.TFRBertDataConfig(
    default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
    restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
    input_path: Union[Sequence[str], str, base_config.Config] = '',
    tfds_name: Union[str, base_config.Config] = '',
    tfds_split: str = '',
    global_batch_size: int = 0,
    is_training: bool = True,
    drop_remainder: bool = True,
    shuffle_buffer_size: int = 100,
    cache: bool = False,
    cycle_length: Optional[int] = None,
    block_length: int = 1,
    deterministic: Optional[bool] = None,
    sharding: bool = True,
    enable_tf_data_service: bool = False,
    tf_data_service_address: Optional[str] = None,
    tf_data_service_job_name: Optional[str] = None,
    tfds_data_dir: str = '',
    tfds_as_supervised: bool = False,
    tfds_skip_decoding_feature: str = '',
    enable_shared_tf_data_service_between_parallel_trainers: bool = False,
    apply_tf_data_service_before_batching: bool = False,
    trainer_id: Optional[str] = None,
    seed: Optional[int] = None,
    prefetch_buffer_size: Optional[int] = None,
    autotune_algorithm: Optional[str] = None,
    data_format: str = tfr_data.ELWC,
    dataset_fn: str = 'tfrecord',
    list_size: Optional[int] = None,
    shuffle_examples: bool = False,
    convert_labels_to_binary: bool = False,
    mask_feature_name: Optional[str] = MASK,
    seq_length: int = 128,
    read_query_id: bool = False,
    read_document_id: bool = False
)

BUILDER

default_params Dataclass field
restrictions Dataclass field
input_path Dataclass field
tfds_name Dataclass field
tfds_split Dataclass field
global_batch_size Dataclass field
is_training Dataclass field
drop_remainder Dataclass field
shuffle_buffer_size Dataclass field
cache Dataclass field
cycle_length Dataclass field
block_length Dataclass field
deterministic Dataclass field
sharding Dataclass field
enable_tf_data_service Dataclass field
tf_data_service_address Dataclass field
tf_data_service_job_name Dataclass field
tfds_data_dir Dataclass field
tfds_as_supervised Dataclass field
tfds_skip_decoding_feature Dataclass field
enable_shared_tf_data_service_between_parallel_trainers Dataclass field
apply_tf_data_service_before_batching Dataclass field
trainer_id Dataclass field
seed Dataclass field
prefetch_buffer_size Dataclass field
autotune_algorithm Dataclass field
data_format Dataclass field
dataset_fn Dataclass field
list_size Dataclass field
shuffle_examples Dataclass field
convert_labels_to_binary Dataclass field
mask_feature_name Dataclass field
seq_length Dataclass field
read_query_id Dataclass field
read_document_id Dataclass field

Methods

as_dict

as_dict()

Returns a dict representation of params_dict.ParamsDict.

For the nested params_dict.ParamsDict, a nested dict will be returned.

from_args

@classmethod
from_args(
    *args, **kwargs
)

Builds a config from the given list of arguments.

from_json

@classmethod
from_json(
    file_path: str
)

Wrapper for from_yaml.

from_yaml

@classmethod
from_yaml(
    file_path: str
)

get

get(
    key, value=None
)

Accesses through built-in dictionary get method.

lock

lock()

Makes the ParamsDict immutable.

override

override(
    override_params, is_strict=True
)

Override the ParamsDict with a set of given params.

Args
override_params a dict or a ParamsDict specifying the parameters to be overridden.
is_strict a boolean specifying whether override is strict or not. If True, keys in override_params must be present in the ParamsDict. If False, keys in override_params can be different from what is currently defined in the ParamsDict. In this case, the ParamsDict will be extended to include the new keys.

replace

replace(
    **kwargs
)

Overrides/returns a unlocked copy with the current config unchanged.

validate

validate()

Validate the parameters consistency based on the restrictions.

This method validates the internal consistency using the pre-defined list of restrictions. A restriction is defined as a string which specifies a binary operation. The supported binary operations are {'==', '!=', '<', '<=', '>', '>='}. Note that the meaning of these operators are consistent with the underlying Python immplementation. Users should make sure the define restrictions on their type make sense.

For example, for a ParamsDict like the following

a:
  a1: 1
  a2: 2
b:
  bb:
    bb1: 10
    bb2: 20
  ccc:
    a1: 1
    a3: 3

one can define two restrictions like this ['a.a1 == b.ccc.a1', 'a.a2 <= b.bb.bb2']

What it enforces are

  • a.a1 = 1 == b.ccc.a1 = 1
  • a.a2 = 2 <= b.bb.bb2 = 20

Raises
KeyError if any of the following happens (1) any of parameters in any of restrictions is not defined in ParamsDict, (2) any inconsistency violating the restriction is found.
ValueError if the restriction defined in the string is not supported.

__contains__

__contains__(
    key
)

Implements the membership test operator.

__eq__

__eq__(
    other
)

IMMUTABLE_TYPES (<class 'str'>, <class 'int'>, <class 'float'>, <class 'bool'>, <class 'NoneType'>)
RESERVED_ATTR ['_locked', '_restrictions']
SEQUENCE_TYPES (<class 'list'>, <class 'tuple'>)
apply_tf_data_service_before_batching False
autotune_algorithm None
block_length 1
cache False
convert_labels_to_binary False
cycle_length None
data_format 'example_list_with_context'
dataset_fn 'tfrecord'
default_params None
deterministic None
drop_remainder True
enable_shared_tf_data_service_between_parallel_trainers False
enable_tf_data_service False
global_batch_size 0
input_path ''
is_training True
list_size None
mask_feature_name 'example_list_mask'
prefetch_buffer_size None
read_document_id False
read_query_id False
restrictions None
seed None
seq_length 128
sharding True
shuffle_buffer_size 100
shuffle_examples False
tf_data_service_address None
tf_data_service_job_name None
tfds_as_supervised False
tfds_data_dir ''
tfds_name ''
tfds_skip_decoding_feature ''
tfds_split ''
trainer_id None