DistilBertTokenizer

[source]

DistilBertTokenizer class

keras_nlp.models.DistilBertTokenizer(
    vocabulary, lowercase=False, special_tokens_in_strings=False, **kwargs
)

A DistilBERT tokenizer using WordPiece subword segmentation.

This tokenizer class will tokenize raw strings into integer sequences and is based on keras_nlp.tokenizers.WordPieceTokenizer. Unlike the underlying tokenizer, it will check for all special tokens needed by DistilBERT models and provides a from_preset() method to automatically download a matching vocabulary for a DistilBERT preset.

This tokenizer does not provide truncation or padding of inputs. It can be combined with a keras_nlp.models.DistilBertPreprocessor layer for input packing.

If input is a batch of strings (rank > 0), the layer will output a tf.RaggedTensor where the last dimension of the output is ragged.

If input is a scalar string (rank == 0), the layer will output a dense tf.Tensor with static shape [None].

Arguments

• vocabulary: A list of strings or a string filename path. If passing a list, each element of the list should be a single word piece token string. If passing a filename, the file should be a plain text file containing a single word piece token per line.
• lowercase: If True, the input text will be first lowered before tokenization.
• special_tokens_in_strings: bool. A bool to indicate if the tokenizer should expect special tokens in input strings that should be tokenized and mapped correctly to their ids. Defaults to False.

Examples

# Unbatched input.
tokenizer = keras_nlp.models.DistilBertTokenizer.from_preset(
    "distil_bert_base_en_uncased",
)
tokenizer("The quick brown fox jumped.")

# Batched input.
tokenizer(["The quick brown fox jumped.", "The fox slept."])

# Detokenization.
tokenizer.detokenize(tokenizer("The quick brown fox jumped."))

# Custom vocabulary.
vocab = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
vocab += ["The", "quick", "brown", "fox", "jumped", "."]
tokenizer = keras_nlp.models.DistilBertTokenizer(vocabulary=vocab)
tokenizer("The quick brown fox jumped.")

[source]

from_preset method

DistilBertTokenizer.from_preset(preset, **kwargs)

Instantiate a keras_nlp.models.Tokenizer from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as a one of:

1. a built in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './bert_base_en'

For any Tokenizer subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

This constructor can be called in one of two ways. Either from the base class like keras_nlp.models.Tokenizer.from_preset(), or from a model class like keras_nlp.models.GemmaTokenizer.from_preset(). If calling from the base class, the subclass of the returning object will be inferred from the config in the preset directory.

Arguments

• preset: string. A built in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.
• load_weights: bool. If True, the weights will be loaded into the model architecture. If False, the weights will be randomly initialized.

Examples

# Load a preset tokenizer.
tokenizer = keras_nlp.tokenizerTokenizer.from_preset("bert_base_en")

# Tokenize some input.
tokenizer("The quick brown fox tripped.")

# Detokenize some input.
tokenizer.detokenize([5, 6, 7, 8, 9])