XLMRobertaTokenizer

• Original Link : https://keras.io/api/keras_hub/models/xlm_roberta/xlm_roberta_tokenizer/
• Last Checked at : 2024-11-26

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XLMRobertaTokenizer class

keras_hub.tokenizers.XLMRobertaTokenizer(proto, **kwargs)

An XLM-RoBERTa tokenizer using SentencePiece subword segmentation.

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

Note: If you are providing your own custom SentencePiece model, the original fairseq implementation of XLM-RoBERTa re-maps some token indices from the underlying sentencepiece output. To preserve compatibility, we do the same re-mapping here.

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

tokenizer = keras_hub.models.XLMRobertaTokenizer.from_preset(
    "xlm_roberta_base_multi",
)
# Unbatched inputs.
tokenizer("the quick brown fox")
# Batched inputs.
tokenizer(["the quick brown fox", "الأرض كروية"])
# Detokenization.
tokenizer.detokenize(tokenizer("the quick brown fox"))
# Custom vocabulary
def train_sentencepiece(ds, vocab_size):
    bytes_io = io.BytesIO()
    sentencepiece.SentencePieceTrainer.train(
        sentence_iterator=ds.as_numpy_iterator(),
        model_writer=bytes_io,
        vocab_size=vocab_size,
        model_type="WORD",
        unk_id=0,
        bos_id=1,
        eos_id=2,
    )
    return bytes_io.getvalue()
ds = tf.data.Dataset.from_tensor_slices(
    ["the quick brown fox", "the earth is round"]
)
proto = train_sentencepiece(ds, vocab_size=10)
tokenizer = keras_hub.models.XLMRobertaTokenizer(proto=proto)

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from_preset method

XLMRobertaTokenizer.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])