Mistral分词器

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MistralTokenizer class

keras_nlp.models.MistralTokenizer(proto, **kwargs)

Mistral tokenizer layer based on SentencePiece.

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

This tokenizer does not provide truncation or padding of inputs. It can be combined with a keras_nlp.models.MistralPreprocessor layer for input packing.

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

• proto: Either a 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_nlp.models.MistralTokenizer.from_preset(
    "mistral_7b_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."))

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from_preset method

MistralTokenizer.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])