data_juicer.ops.grouper#

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

Group samples to batched samples according values in given keys.

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

Initialization method.

Parameters:

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)[source]#

Dataset –> dataset.

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

Group all samples to one batched sample.

__init__(*args, **kwargs)[source]#

Initialization method.

Parameters:

process(dataset)[source]#

Dataset –> dataset.

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

Split batched samples to samples.

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

Initialization method.

Parameters:

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)[source]#

Dataset –> dataset.