计算词片词汇表函数
compute_word_piece_vocabulary function
keras_nlp.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 toFalse. - strip_accents: bool. If
True, all accent marks will be removed from text before tokenization. Defaults toFalse. - split: bool. If
True, input will be split on whitespace and punctuation marks, and all punctuation marks will be kept as tokens. IfFalse, input should be split ("pre-tokenized") before calling the tokenizer, and passed as a dense or ragged tensor of whole words.splitis required to beTruewhendatais a list of filenames. Defaults toTrue. - 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 whensplitisTrue. Defaults toTrue. - 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_nlp.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_nlp.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_nlp.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_nlp.tokenizers.compute_word_piece_vocabulary(
split_inputs, 13, split=False
)
tokenizer = keras_nlp.tokenizers.WordPieceTokenizer(vocabulary=vocab)
inputs.map(tokenizer.tokenize)