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compute_word_piece_vocabulary function

`compute_word_piece_vocabulary` function

keras_hub.tokenizers.compute_word_piece_vocabulary(
    data,
    vocabulary_size,
    vocabulary_output_file=None,
    lowercase=False,
    strip_accents=False,
    split=True,
    split_on_cjk=True,
    suffix_indicator="##",
    reserved_tokens=["[PAD]", "[CLS]", "[SEP]", "[UNK]", "[MASK]"],
)

A utility to train a WordPiece vocabulary.

Trains a WordPiece vocabulary from an input dataset or a list of filenames.

For custom data loading and pretokenization (split=False), the input data should be a tf.data.Dataset. If data is a list of filenames, the file format is required to be plain text files, and the text would be read in line by line during training.

Arguments

data: A tf.data.Dataset, or a list of filenames.
vocabulary_size: int. The maximum size of a vocabulary to be trained.
vocabulary_output_file: str. The location to write a vocabulary file. defaults to None.
lowercase: bool. If True, the input text will be lowercased before tokenization. Defaults to False.
strip_accents: bool. If True, all accent marks will be removed from text before tokenization. Defaults to False.
split: bool. If True, input will be split on whitespace and punctuation marks, and all punctuation marks will be kept as tokens. If False, input should be split ("pre-tokenized") before calling the tokenizer, and passed as a dense or ragged tensor of whole words. split is required to be True when data is a list of filenames. Defaults to True.
split_on_cjk: bool. If True, input will be split on CJK characters, i.e., Chinese, Japanese, Korean and Vietnamese characters (https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)). Note that this is applicable only when split is True. Defaults to True.
suffix_indicator: str. The characters prepended to a WordPiece to indicate that it is a suffix to another subword. E.g. "##ing". Defaults to "##".
reserved_tokens: list of strings. A list of tokens that must be included in the vocabulary.

Returns

Returns a list of vocabulary terms.

Examples

Basic Usage (from Dataset).

>>> inputs = tf.data.Dataset.from_tensor_slices(["bat sat pat mat rat"])
>>> vocab = compute_word_piece_vocabulary(inputs, 13)
>>> vocab
['[PAD]', '[CLS]', '[SEP]', '[UNK]', '[MASK]', 'a', 'b', 'm', 'p', 'r', 's', 't', '##at']
>>> tokenizer = keras_hub.tokenizers.WordPieceTokenizer(vocabulary=vocab, oov_token="[UNK]")
>>> outputs = inputs.map(tokenizer.tokenize)
>>> for x in outputs:
...     print(x)
tf.Tensor([ 6 12 10 12  8 12  7 12  9 12], shape=(10,), dtype=int32)

Basic Usage (from filenames).

with open("test.txt", "w+") as f:
    f.write("bat sat pat mat rat\n")
inputs = ["test.txt"]
vocab = keras_hub.tokenizers.compute_word_piece_vocabulary(inputs, 13)

Custom Split Usage (from Dataset).

>>> def normalize_and_split(x):
...     "Strip punctuation and split on whitespace."
...     x = tf.strings.regex_replace(x, r"\p{P}", "")
...     return tf.strings.split(x)
>>> inputs = tf.data.Dataset.from_tensor_slices(["bat sat: pat mat rat.\n"])
>>> split_inputs = inputs.map(normalize_and_split)
>>> vocab = compute_word_piece_vocabulary(
...     split_inputs, 13, split=False,
... )
>>> vocab
['[PAD]', '[CLS]', '[SEP]', '[UNK]', '[MASK]', 'a', 'b', 'm', 'p', 'r', 's', 't', '##at']
>>> tokenizer = keras_hub.tokenizers.WordPieceTokenizer(vocabulary=vocab)
>>> inputs.map(tokenizer.tokenize)

Custom Split Usage (from filenames).

def normalize_and_split(x):
    "Strip punctuation and split on whitespace."
    x = tf.strings.regex_replace(x, r"\p{P}", "")
    return tf.strings.split(x)
with open("test.txt", "w+") as f:
    f.write("bat sat: pat mat rat.\n")
inputs = tf.data.TextLineDataset(["test.txt"])
split_inputs = inputs.map(normalize_and_split)
vocab = keras_hub.tokenizers.compute_word_piece_vocabulary(
    split_inputs, 13, split=False
)
tokenizer = keras_hub.tokenizers.WordPieceTokenizer(vocabulary=vocab)
inputs.map(tokenizer.tokenize)

compute_word_piece_vocabulary function

compute_word_piece_vocabulary function