Keras
Keras 3 API 문서
KerasNLP
Tokenizers
ByteTokenizer

ByteTokenizer

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ByteTokenizer class 

keras_nlp.tokenizers.ByteTokenizer(
    lowercase=True,
    sequence_length=None,
    normalization_form=None,
    errors="replace",
    replacement_char=65533,
    dtype="int32",
    **kwargs
)

Raw byte tokenizer.

This tokenizer is a vocabulary-free tokenizer which will tokenize text as as raw bytes from [0, 256).

Tokenizer outputs can either be padded and truncated with a sequence_length argument, or left un-truncated. The exact output will depend on the rank of the input tensors.

If input is a batch of strings: By default, the layer will output a tf.RaggedTensor where the last dimension of the output is ragged. If sequence_length is set, the layer will output a dense tf.Tensor where all inputs have been padded or truncated to sequence_length.

If input is a scalar string: There are two cases here. If sequence_length is set, the output will be a dense tf.Tensor of shape [sequence_length]. Otherwise, the output will be a dense tf.Tensor of shape [None].

The output dtype can be controlled via the dtype argument, which should be an integer type (“int16”, “int32”, etc.).

Arguments

  • lowercase: boolean. If True, the input text will be converted to lowercase before tokenization.
  • sequence_length: int. If set, the output will be converted to a dense tensor and padded/trimmed so all outputs are of sequence_length.
  • normalization_form: string. One of the following values: (None, “NFC”, “NFKC”, “NFD”, “NFKD”). If set, every UTF-8 string in the input tensor text will be normalized to the given form before tokenizing.
  • errors: One of (‘replace’, ‘remove’, ‘strict’). Specifies the detokenize() behavior when an invalid tokenizer is encountered. The value of 'strict' will cause the operation to produce a InvalidArgument error on any invalid input formatting. A value of 'replace' will cause the tokenizer to replace any invalid formatting in the input with the replacement_char codepoint. A value of 'ignore' will cause the tokenizer to skip any invalid formatting in the input and produce no corresponding output character.
  • replacement_char: int. The replacement character to use when an invalid byte sequence is encountered and when errors is set to “replace” (same behaviour as https://www.tensorflow.org/api_docs/python/tf/strings/unicode_transcode). (U+FFFD) is 65533. Defaults to 65533.

Examples

Basic usage.

>>> tokenizer = keras_hub.tokenizers.ByteTokenizer()
>>> outputs = tokenizer("hello")
>>> np.array(outputs)
array([104, 101, 108, 108, 111], dtype=int32)

Ragged outputs.

>>> inputs = ["hello", "hi"]
>>> tokenizer = keras_hub.tokenizers.ByteTokenizer()
>>> seq1, seq2 = tokenizer(inputs)
>>> np.array(seq1)
array([104, 101, 108, 108, 111])
>>> np.array(seq2)
array([104, 105])

Dense outputs.

>>> inputs = ["hello", "hi"]
>>> tokenizer = keras_hub.tokenizers.ByteTokenizer(sequence_length=8)
>>> seq1, seq2 = tokenizer(inputs)
>>> np.array(seq1)
array([104, 101, 108, 108, 111,   0,   0,   0], dtype=int32)
>>> np.array(seq2)
array([104, 105,   0,   0,   0,   0,   0,   0], dtype=int32)

Tokenize, then batch for ragged outputs.

>>> tokenizer = keras_hub.tokenizers.ByteTokenizer()
>>> ds = tf.data.Dataset.from_tensor_slices(["hello", "fun"])
>>> ds = ds.map(tokenizer)
>>> ds = ds.apply(tf.data.experimental.dense_to_ragged_batch(2))
>>> ds.take(1).get_single_element()
<tf.RaggedTensor [[104, 101, 108, 108, 111], [102, 117, 110]]>

Batch, then tokenize for ragged outputs.

>>> tokenizer = keras_hub.tokenizers.ByteTokenizer()
>>> ds = tf.data.Dataset.from_tensor_slices(["hello", "fun"])
>>> ds = ds.batch(2).map(tokenizer)
>>> ds.take(1).get_single_element()
<tf.RaggedTensor [[104, 101, 108, 108, 111], [102, 117, 110]]>

Tokenize, then batch for dense outputs (sequence_length provided).

>>> tokenizer = keras_hub.tokenizers.ByteTokenizer(sequence_length=5)
>>> ds = tf.data.Dataset.from_tensor_slices(["hello", "fun"])
>>> ds = ds.map(tokenizer)
>>> ds = ds.apply(tf.data.experimental.dense_to_ragged_batch(2))
>>> ds.take(1).get_single_element()
<tf.Tensor: shape=(2, 5), dtype=int32, numpy=
array([[104, 101, 108, 108, 111],
       [102, 117, 110,   0,   0]], dtype=int32)>

Batch, then tokenize for dense outputs. (sequence_length provided).

>>> tokenizer = keras_hub.tokenizers.ByteTokenizer(sequence_length=5)
>>> ds = tf.data.Dataset.from_tensor_slices(["hello", "fun"])
>>> ds = ds.batch(2).map(tokenizer)
>>> ds.take(1).get_single_element()
<tf.Tensor: shape=(2, 5), dtype=int32, numpy=
array([[104, 101, 108, 108, 111],
       [102, 117, 110,   0,   0]], dtype=int32)>

Detokenization.

>>> inputs = [104, 101, 108, 108, 111]
>>> tokenizer = keras_hub.tokenizers.ByteTokenizer()
>>> tokenizer.detokenize(inputs)
'hello'

Detokenization with invalid bytes.

>>> # The 255 below is invalid utf-8.
>>> inputs = [104, 101, 255, 108, 108, 111]
>>> tokenizer = keras_hub.tokenizers.ByteTokenizer(
...     errors="replace", replacement_char=88)
>>> tokenizer.detokenize(inputs)
'heXllo'

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tokenize method 

ByteTokenizer.tokenize(inputs)

Transform input tensors of strings into output tokens.

Arguments

  • inputs: Input tensor, or dict/list/tuple of input tensors.
  • *args: Additional positional arguments.
  • **kwargs: Additional keyword arguments.

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detokenize method 

ByteTokenizer.detokenize(inputs)

Transform tokens back into strings.

Arguments

  • inputs: Input tensor, or dict/list/tuple of input tensors.
  • *args: Additional positional arguments.
  • **kwargs: Additional keyword arguments.

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get_vocabulary method 

ByteTokenizer.get_vocabulary()

Get the tokenizer vocabulary as a list of strings terms.

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vocabulary_size method 

ByteTokenizer.vocabulary_size()

Get the integer size of the tokenizer vocabulary.

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token_to_id method 

ByteTokenizer.token_to_id(token)

Convert a string token to an integer id.

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id_to_token method 

ByteTokenizer.id_to_token(id)

Convert an integer id to a string token.