AlbertTokenizer
classkeras_nlp.tokenizers.AlbertTokenizer(proto, **kwargs)
ALBERT tokenizer layer based on SentencePiece.
This tokenizer class will tokenize raw strings into integer sequences and
is based on keras_nlp.tokenizers.SentencePieceTokenizer
. Unlike the
underlying tokenizer, it will check for all special tokens needed by
ALBERT models and provides a from_preset()
method to automatically
download a matching vocabulary for a ALBERT preset.
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
string
path to a SentencePiece proto file, or a
bytes
object with a serialized SentencePiece proto. See the
SentencePiece repository
for more details on the format.Examples
# Unbatched input.
tokenizer = keras_nlp.models.AlbertTokenizer.from_preset(
"albert_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.
bytes_io = io.BytesIO()
ds = tf.data.Dataset.from_tensor_slices(["The quick brown fox jumped."])
sentencepiece.SentencePieceTrainer.train(
sentence_iterator=ds.as_numpy_iterator(),
model_writer=bytes_io,
vocab_size=10,
model_type="WORD",
pad_id=0,
unk_id=1,
bos_id=2,
eos_id=3,
pad_piece="<pad>",
unk_piece="<unk>",
bos_piece="[CLS]",
eos_piece="[SEP]",
user_defined_symbols="[MASK]",
)
tokenizer = keras_nlp.models.AlbertTokenizer(
proto=bytes_io.getvalue(),
)
tokenizer("The quick brown fox jumped.")
from_preset
methodAlbertTokenizer.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
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_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
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.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 |
---|---|---|
albert_base_en_uncased | 11.68M | 12-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus. |
albert_large_en_uncased | 17.68M | 24-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus. |
albert_extra_large_en_uncased | 58.72M | 24-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus. |
albert_extra_extra_large_en_uncased | 222.60M | 12-layer ALBERT model where all input is lowercased. Trained on English Wikipedia + BooksCorpus. |