XLMRobertaPreprocessor layer

[source]

XLMRobertaPreprocessor class

keras_nlp.models.XLMRobertaPreprocessor(
    tokenizer, sequence_length=512, truncate="round_robin", **kwargs
)

An XLM-RoBERTa preprocessing layer which tokenizes and packs inputs.

This preprocessing layer will do three things:

1. Tokenize any number of input segments using the tokenizer.
2. Pack the inputs together using a keras_nlp.layers.MultiSegmentPacker. with the appropriate "<s>", "</s>" and "<pad>" tokens, i.e., adding a single "<s>" at the start of the entire sequence, "</s></s>" at the end of each segment, save the last and a "</s>" at the end of the entire sequence.
3. Construct a dictionary with keys "token_ids" and "padding_mask", that can be passed directly to an XLM-RoBERTa model.

This layer can be used directly with tf.data.Dataset.map to preprocess string data in the (x, y, sample_weight) format used by keras.Model.fit.

Arguments

• tokenizer: A keras_nlp.tokenizers.XLMRobertaTokenizer instance.
• sequence_length: The length of the packed inputs.
• truncate: The algorithm to truncate a list of batched segments to fit within sequence_length. The value can be either round_robin or waterfall: - "round_robin": Available space is assigned one token at a time in a round-robin fashion to the inputs that still need some, until the limit is reached. - "waterfall": The allocation of the budget is done using a "waterfall" algorithm that allocates quota in a left-to-right manner and fills up the buckets until we run out of budget. It supports an arbitrary number of segments.

Call arguments

• x: A tensor of single string sequences, or a tuple of multiple tensor sequences to be packed together. Inputs may be batched or unbatched. For single sequences, raw python inputs will be converted to tensors. For multiple sequences, pass tensors directly.
• y: Any label data. Will be passed through unaltered.
• sample_weight: Any label weight data. Will be passed through unaltered.

Examples

Directly calling the layer on data.

preprocessor = keras_nlp.models.XLMRobertaPreprocessor.from_preset(
    "xlm_roberta_base_multi"
)

# Tokenize and pack a single sentence.
preprocessor("The quick brown fox jumped.")

# Tokenize a batch of single sentences.
preprocessor(["The quick brown fox jumped.", "اسمي اسماعيل"])

# Preprocess a batch of sentence pairs.
# When handling multiple sequences, always convert to tensors first!
first = tf.constant(["The quick brown fox jumped.", "اسمي اسماعيل"])
second = tf.constant(["The fox tripped.", "الأسد ملك الغابة"])
preprocessor((first, second))

# 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_nlp.models.XLMRobertaTokenizer(proto=proto)
preprocessor = keras_nlp.models.XLMRobertaPreprocessor(tokenizer)
preprocessor("The quick brown fox jumped.")

Mapping with tf.data.Dataset.

preprocessor = keras_nlp.models.XLMRobertaPreprocessor.from_preset(
    "xlm_roberta_base_multi"
)

first = tf.constant(["The quick brown fox jumped.", "Call me Ishmael."])
second = tf.constant(["The fox tripped.", "Oh look, a whale."])
label = tf.constant([1, 1])

# Map labeled single sentences.
ds = tf.data.Dataset.from_tensor_slices((first, label))
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

# Map unlabeled single sentences.
ds = tf.data.Dataset.from_tensor_slices(first)
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

# Map labeled sentence pairs.
ds = tf.data.Dataset.from_tensor_slices(((first, second), label))
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

# Map unlabeled sentence pairs.
ds = tf.data.Dataset.from_tensor_slices((first, second))
# Watch out for tf.data's default unpacking of tuples here!
# Best to invoke the `preprocessor` directly in this case.
ds = ds.map(
    lambda first, second: preprocessor(x=(first, second)),
    num_parallel_calls=tf.data.AUTOTUNE,
)

[source]

from_preset method

XLMRobertaPreprocessor.from_preset(preset, **kwargs)

Instantiate a keras_nlp.models.Preprocessor 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 Preprocessor subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

As there are usually multiple preprocessing classes for a given model, this method should be called on a specific subclass like keras_nlp.models.BertPreprocessor.from_preset().

Arguments

• preset: string. A built in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.

Examples

# Load a preprocessor for Gemma generation.
preprocessor = keras_nlp.models.GemmaCausalLMPreprocessor.from_preset(
    "gemma_2b_en",
)

# Load a preprocessor for Bert classification.
preprocessor = keras_nlp.models.BertPreprocessor.from_preset(
    "bert_base_en",
)

tokenizer property

keras_nlp.models.XLMRobertaPreprocessor.tokenizer

The tokenizer used to tokenize strings.