data_juicer.ops.grouper package

Submodules

data_juicer.ops.grouper.key_value_grouper module

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

Bases: Grouper

Group samples to batched samples according values in given keys.

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

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)

Dataset –> dataset.

Parameters:

dataset – input dataset

Returns:

dataset of batched samples.

data_juicer.ops.grouper.naive_grouper module

class data_juicer.ops.grouper.naive_grouper.NaiveGrouper(*args, **kwargs)

Bases: Grouper

Group all samples to one batched sample.

__init__(*args, **kwargs)

Initialization method.

Parameters:

• args – extra args

• kwargs – extra args

process(dataset)

Dataset –> dataset.

Parameters:

dataset – input dataset

Returns:

dataset of batched samples.

data_juicer.ops.grouper.naive_reverse_grouper module

class data_juicer.ops.grouper.naive_reverse_grouper.NaiveReverseGrouper(batch_meta_export_path=None, *args, **kwargs)

Bases: Grouper

Split batched samples to samples.

__init__(batch_meta_export_path=None, *args, **kwargs)

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)

Dataset –> dataset.

Parameters:

dataset – input dataset

Returns:

dataset of batched samples.

Module contents

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

Bases: Grouper

Group samples to batched samples according values in given keys.

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

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)

Dataset –> dataset.

Parameters:

dataset – input dataset

Returns:

dataset of batched samples.

class data_juicer.ops.grouper.NaiveGrouper(*args, **kwargs)

Bases: Grouper

Group all samples to one batched sample.

__init__(*args, **kwargs)

Initialization method.

Parameters:

• args – extra args

• kwargs – extra args

process(dataset)

Dataset –> dataset.

Parameters:

dataset – input dataset

Returns:

dataset of batched samples.

class data_juicer.ops.grouper.NaiveReverseGrouper(batch_meta_export_path=None, *args, **kwargs)

Bases: Grouper

Split batched samples to samples.

__init__(batch_meta_export_path=None, *args, **kwargs)

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)

Dataset –> dataset.

Parameters:

dataset – input dataset

Returns:

dataset of batched samples.