► Keras 3 API documentation / KerasNLP / Preprocessing Layers / StartEndPacker layer

StartEndPacker layer

[source]

StartEndPacker class

keras_nlp.layers.StartEndPacker(
    sequence_length,
    start_value=None,
    end_value=None,
    pad_value=None,
    return_padding_mask=False,
    name=None,
    **kwargs
)

Adds start and end tokens to a sequence and pads to a fixed length.

This layer is useful when tokenizing inputs for tasks like translation, where each sequence should include a start and end marker. It should be called after tokenization. The layer will first trim inputs to fit, then add start/end tokens, and finally pad, if necessary, to sequence_length.

Input data should be passed as tensors, tf.RaggedTensors, or lists. For batched input, inputs should be a list of lists or a rank two tensor. For unbatched inputs, each element should be a list or a rank one tensor.

Arguments

  • sequence_length: int. The desired output length.
  • start_value: int/str/list/tuple. The ID(s) or token(s) that are to be placed at the start of each sequence. The dtype must match the dtype of the input tensors to the layer. If None, no start value will be added.
  • end_value: int/str/list/tuple. The ID(s) or token(s) that are to be placed at the end of each input segment. The dtype must match the dtype of the input tensors to the layer. If None, no end value will be added.
  • pad_value: int/str. The ID or token that is to be placed into the unused positions after the last segment in the sequence. If None, 0 or "" will be added depending on the dtype of the input tensor.
  • return_padding_mask: bool. Whether to return a boolean padding mask of all locations that are filled in with the pad_value.

Call arguments

  • inputs: A tf.Tensor, tf.RaggedTensor, or list of python strings.
  • sequence_length: Pass to override the configured sequence_length of the layer.
  • add_start_value: Pass False to not append a start value for this input.
  • add_end_value: Pass False to not append an end value for this input.

Examples

Unbatched input (int).

>>> inputs = [5, 6, 7]
>>> start_end_packer = keras_nlp.layers.StartEndPacker(
...     sequence_length=7, start_value=1, end_value=2,
... )
>>> outputs = start_end_packer(inputs)
>>> np.array(outputs)
array([1, 5, 6, 7, 2, 0, 0], dtype=int32)

Batched input (int).

>>> inputs = [[5, 6, 7], [8, 9, 10, 11, 12, 13, 14]]
>>> start_end_packer = keras_nlp.layers.StartEndPacker(
...     sequence_length=6, start_value=1, end_value=2,
... )
>>> outputs = start_end_packer(inputs)
>>> np.array(outputs)
array([[ 1,  5,  6,  7,  2,  0],
       [ 1,  8,  9, 10, 11,  2]], dtype=int32)

Unbatched input (str).

>>> inputs = tf.constant(["this", "is", "fun"])
>>> start_end_packer = keras_nlp.layers.StartEndPacker(
...     sequence_length=6, start_value="<s>", end_value="</s>",
...     pad_value="<pad>"
... )
>>> outputs = start_end_packer(inputs)
>>> np.array(outputs).astype("U")
array(['<s>', 'this', 'is', 'fun', '</s>', '<pad>'], dtype='<U5')

Batched input (str).

>>> inputs = tf.ragged.constant([["this", "is", "fun"], ["awesome"]])
>>> start_end_packer = keras_nlp.layers.StartEndPacker(
...     sequence_length=6, start_value="<s>", end_value="</s>",
...     pad_value="<pad>"
... )
>>> outputs = start_end_packer(inputs)
>>> np.array(outputs).astype("U")
array([['<s>', 'this', 'is', 'fun', '</s>', '<pad>'],
       ['<s>', 'awesome', '</s>', '<pad>', '<pad>', '<pad>']], dtype='<U7')

Multiple start tokens.

>>> inputs = tf.ragged.constant([["this", "is", "fun"], ["awesome"]])
>>> start_end_packer = keras_nlp.layers.StartEndPacker(
...     sequence_length=6, start_value=["</s>", "<s>"], end_value="</s>",
...     pad_value="<pad>"
... )
>>> outputs = start_end_packer(inputs)
>>> np.array(outputs).astype("U")
array([['</s>', '<s>', 'this', 'is', 'fun', '</s>'],
       ['</s>', '<s>', 'awesome', '</s>', '<pad>', '<pad>']], dtype='<U7')
StartEndPacker layer
StartEndPacker class