OPTTokenizer

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OPTTokenizer class

keras_nlp.models.OPTTokenizer(vocabulary=None, merges=None, **kwargs)

An OPT tokenizer using Byte-Pair Encoding subword segmentation.

This tokenizer class will tokenize raw strings into integer sequences and is based on keras_nlp.tokenizers.BytePairTokenizer. Unlike the underlying tokenizer, it will check for all special tokens needed by OPT models and provides a from_preset() method to automatically download a matching vocabulary for a OPT preset.

This tokenizer does not provide truncation or padding of inputs.

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.

Examples

# Unbatched input.
tokenizer = keras_nlp.models.OPTTokenizer.from_preset(
    "opt_125m_en",
)
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.
vocab = {"<pad>": 1, "</s>": 2, "Ġquick": 4, "Ġfox": 5}
merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
merges += ["Ġ f", "o x", "Ġf ox"]
tokenizer = keras_nlp.models.OPTTokenizer(vocabulary=vocab, merges=merges)
tokenizer("The quick brown fox jumped.")

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from_preset method

OPTTokenizer.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 a 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_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

• 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_nlp.tokenizerTokenizer.from_preset("bert_base_en")

# Tokenize some input.
tokenizer("The quick brown fox tripped.")

# Detokenize some input.
tokenizer.detokenize([5, 6, 7, 8, 9])