CLIPTokenizer

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CLIPTokenizer class

keras_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

  • vocabulary: string or dict, maps token to integer ids. If it is a string, it should be the file path to a json file.
  • merges: string or list, contains the merge rule. If it is a string, it should be the file path to merge rules. The merge rule file should have one merge rule per line. Every merge rule contains merge entities separated by a space.
  • pad_with_end_token: bool. Whether to pad the output with 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."))

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from_preset method

CLIPTokenizer.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:

  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_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

  • 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_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.