Keras 3 API documentation / KerasNLP / Models / OPT / OPTTokenizer

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()

Instantiate OPTTokenizer tokenizer from preset vocabulary.

Arguments

  • preset: string. Must be one of "opt_125m_en", "opt_1.3b_en", "opt_2.7b_en", "opt_6.7b_en".

Examples

# Load a preset tokenizer.
tokenizer = OPTTokenizer.from_preset("opt_125m_en")

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

# Detokenize some input.
tokenizer.detokenize([5, 6, 7, 8, 9])
Preset name Parameters Description
opt_125m_en 125.24M 12-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora.
opt_1.3b_en 1.32B 24-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora.
opt_2.7b_en 2.70B 32-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora.
opt_6.7b_en 6.70B 32-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora.