CLIPTokenizer
classkeras_hub.tokenizers.CLIPTokenizer(
vocabulary=None, merges=None, pad_with_end_token=False, **kwargs
)
A CLIP tokenizer using Byte-Pair Encoding subword segmentation.
This tokenizer class will tokenize raw strings into integer sequences and
is based on keras_hub.tokenizers.BytePairTokenizer
. Unlike the
underlying tokenizer, it will check for all special tokens needed by CLIP
models and provides a from_preset()
method to automatically download
a matching vocabulary for a CLIP 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
end_token
.Examples
# Unbatched input.
tokenizer = keras_hub.models.CLIPTokenizer.from_preset(
"clip_vit_base_patch32"
)
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
methodCLIPTokenizer.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 | Parameters | Description |
---|---|---|
clip_vit_base_patch16 | 149.62M | 150 million parameter, 12-layer for vision and 12-layer for text, patch size of 16, CLIP model. |
clip_vit_base_patch32 | 151.28M | 151 million parameter, 12-layer for vision and 12-layer for text, patch size of 32, CLIP model. |
clip_vit_b_32_laion2b_s34b_b79k | 151.28M | 151 million parameter, 12-layer for vision and 12-layer for text, patch size of 32, Open CLIP model. |
clip_vit_large_patch14 | 427.62M | 428 million parameter, 24-layer for vision and 12-layer for text, patch size of 14, CLIP model. |
clip_vit_large_patch14_336 | 427.94M | 428 million parameter, 24-layer for vision and 12-layer for text, patch size of 14, image size of 336, CLIP model. |
clip_vit_h_14_laion2b_s32b_b79k | 986.11M | 986 million parameter, 32-layer for vision and 24-layer for text, patch size of 14, Open CLIP model. |
clip_vit_g_14_laion2b_s12b_b42k | 1.37B | 1.4 billion parameter, 40-layer for vision and 24-layer for text, patch size of 14, Open CLIP model. |
clip_vit_bigg_14_laion2b_39b_b160k | 2.54B | 2.5 billion parameter, 48-layer for vision and 32-layer for text, patch size of 14, Open CLIP model. |