PaliGemmaTokenizer
classkeras_hub.tokenizers.PaliGemmaTokenizer(proto, **kwargs)
PaliGemma tokenizer layer based on SentencePiece.
This tokenizer class will tokenize raw strings into integer sequences and
is based on keras_hub.tokenizers.SentencePieceTokenizer
. Unlike the
underlying tokenizer, it will check for all special tokens needed by
PaliGemma models and provides a from_preset()
method to automatically
download a matching vocabulary for a PaliGemma 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_hub.models.PaliGemmaTokenizer.from_preset(
"pali_gemma_3b_224"
)
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=8,
model_type="WORD",
pad_id=0,
bos_id=1,
eos_id=2,
unk_id=3,
pad_piece="<pad>",
bos_piece="<bos>",
eos_piece="<eos>",
unk_piece="<unk>",
)
tokenizer = keras_hub.models.PaliGemmaTokenizer(
proto=bytes_io.getvalue(),
)
tokenizer("The quick brown fox jumped.")
from_preset
methodPaliGemmaTokenizer.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 |
---|---|---|
pali_gemma_3b_mix_224 | 2.92B | image size 224, mix fine tuned, text sequence length is 256 |
pali_gemma_3b_mix_448 | 2.92B | image size 448, mix fine tuned, text sequence length is 512 |
pali_gemma_3b_224 | 2.92B | image size 224, pre trained, text sequence length is 128 |
pali_gemma_3b_448 | 2.92B | image size 448, pre trained, text sequence length is 512 |
pali_gemma_3b_896 | 2.93B | image size 896, pre trained, text sequence length is 512 |