ElectraTokenizer
classkeras_nlp.tokenizers.ElectraTokenizer(vocabulary, lowercase=False, **kwargs)
A ELECTRA tokenizer using WordPiece subword segmentation.
This tokenizer class will tokenize raw strings into integer sequences and
is based on keras_hub.tokenizers.WordPieceTokenizer
.
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
True
, the input text will be first lowered before
tokenization.Examples
# Custom Vocabulary.
vocab = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
vocab += ["The", "quick", "brown", "fox", "jumped", "."]
# Instantiate the tokenizer.
tokenizer = keras_hub.models.ElectraTokenizer(vocabulary=vocab)
# Unbatched input.
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."))
from_preset
methodElectraTokenizer.from_preset(preset, config_file="tokenizer.json", **kwargs)
Instantiate a keras_hub.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
one of:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./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_hub.models.Tokenizer.from_preset()
, or from
a model class like keras_hub.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
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_hub.tokenizer.Tokenizer.from_preset("bert_base_en")
# Tokenize some input.
tokenizer("The quick brown fox tripped.")
# Detokenize some input.
tokenizer.detokenize([5, 6, 7, 8, 9])
Preset name | Parameters | Description |
---|---|---|
electra_small_discriminator_uncased_en | 13.55M | 12-layer small ELECTRA discriminator model. All inputs are lowercased. Trained on English Wikipedia + BooksCorpus. |
electra_small_generator_uncased_en | 13.55M | 12-layer small ELECTRA generator model. All inputs are lowercased. Trained on English Wikipedia + BooksCorpus. |
electra_base_discriminator_uncased_en | 109.48M | 12-layer base ELECTRA discriminator model. All inputs are lowercased. Trained on English Wikipedia + BooksCorpus. |
electra_base_generator_uncased_en | 33.58M | 12-layer base ELECTRA generator model. All inputs are lowercased. Trained on English Wikipedia + BooksCorpus. |
electra_large_discriminator_uncased_en | 335.14M | 24-layer large ELECTRA discriminator model. All inputs are lowercased. Trained on English Wikipedia + BooksCorpus. |
electra_large_generator_uncased_en | 51.07M | 24-layer large ELECTRA generator model. All inputs are lowercased. Trained on English Wikipedia + BooksCorpus. |