data_juicer.ops.grouper#

class data_juicer.ops.grouper.KeyValueGrouper(group_by_keys: List[str] | None = None, *args, **kwargs)[源代码]#

基类：Grouper

Group samples to batched samples according values in given keys.

__init__(group_by_keys: List[str] | None = None, *args, **kwargs)[源代码]#

Initialization method.

参数:

  • group_by_keys -- group samples according values in the keys. Support for nested keys such as "__dj__stats__.text_len". It is [self.text_key] in default.

  • args -- extra args

  • kwargs -- extra args

process(dataset)[源代码]#

Dataset --> dataset.

参数:

dataset -- input dataset

返回:

dataset of batched samples.

class data_juicer.ops.grouper.NaiveGrouper(*args, **kwargs)[源代码]#

基类：Grouper

Group all samples to one batched sample.

__init__(*args, **kwargs)[源代码]#

Initialization method.

参数:

  • args -- extra args

  • kwargs -- extra args

process(dataset)[源代码]#

Dataset --> dataset.

参数:

dataset -- input dataset

返回:

dataset of batched samples.

class data_juicer.ops.grouper.NaiveReverseGrouper(batch_meta_export_path=None, *args, **kwargs)[源代码]#

基类：Grouper

Split batched samples to samples.

__init__(batch_meta_export_path=None, *args, **kwargs)[源代码]#

Initialization method.

参数:

  • batch_meta_export_path -- the path to export the batch meta. Just drop the batch meta if it is None.

  • args -- extra args

  • kwargs -- extra args

process(dataset)[源代码]#

Dataset --> dataset.

参数:

dataset -- input dataset

返回:

dataset of batched samples.