AlbertTextClassifierPreprocessor layer

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AlbertTextClassifierPreprocessor class

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

An ALBERT preprocessing layer which tokenizes and packs inputs.

This preprocessing layer will do three things:

• Tokenize any number of input segments using the tokenizer.
• Pack the inputs together using a keras_hub.layers.MultiSegmentPacker. with the appropriate "[CLS]", "[SEP]" and "<pad>" tokens.
• Construct a dictionary with keys "token_ids", "segment_ids" and "padding_mask", that can be passed directly to keras_hub.models.AlbertBackbone.

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.

The call method of this layer accepts three arguments, x, y, and sample_weight. x can be a python string or tensor representing a single segment, a list of python strings representing a batch of single segments, or a list of tensors representing multiple segments to be packed together. y and sample_weight are both optional, can have any format, and will be passed through unaltered.

Special care should be taken when using tf.data to map over an unlabeled tuple of string segments. tf.data.Dataset.map will unpack this tuple directly into the call arguments of this layer, rather than forward all argument to x. To handle this case, it is recommended to explicitly call the layer, e.g. ds.map(lambda seg1, seg2: preprocessor(x=(seg1, seg2))).

Arguments

• tokenizer: A keras_hub.models.AlbertTokenizer instance.
• sequence_length: The length of the packed inputs.
• truncate: string. 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.

Examples

Directly calling the layer on data.

preprocessor = keras_hub.models.TextClassifierPreprocessor.from_preset(
    "albert_base_en_uncased"
)

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

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

# Preprocess a batch of sentence pairs.
# When handling multiple sequences, always convert to tensors first!
first = tf.constant(["The quick brown fox jumped.", "Call me Ishmael."])
second = tf.constant(["The fox tripped.", "Oh look, a whale."])
preprocessor((first, second))

# Custom vocabulary.
bytes_io = io.BytesIO()
ds = tf.data.Dataset.from_tensor_slices(["The quick brown fox jumped."])
sentencepiece.SentencePieceTrainer.train(
    sentence_iterator=ds.as_numpy_iterator(),
    model_writer=bytes_io,
    vocab_size=10,
    model_type="WORD",
    pad_id=0,
    unk_id=1,
    bos_id=2,
    eos_id=3,
    pad_piece="<pad>",
    unk_piece="<unk>",
    bos_piece="[CLS]",
    eos_piece="[SEP]",
    user_defined_symbols="[MASK]",
)
tokenizer = keras_hub.models.AlbertTokenizer(
    proto=bytes_io.getvalue(),
)
preprocessor = keras_hub.models.AlbertTextClassifierPreprocessor(tokenizer)
preprocessor("The quick brown fox jumped.")

Mapping with tf.data.Dataset.

preprocessor = keras_hub.models.TextClassifierPreprocessor.from_preset(
    "albert_base_en_uncased"
)

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,
)

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from_preset method

AlbertTextClassifierPreprocessor.from_preset(
    preset, config_file="preprocessor.json", **kwargs
)

Instantiate a keras_hub.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 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_hub.models.BertTextClassifierPreprocessor.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_hub.models.GemmaCausalLMPreprocessor.from_preset(
    "gemma_2b_en",
)

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

tokenizer property

keras_nlp.models.AlbertTextClassifierPreprocessor.tokenizer

The tokenizer used to tokenize strings.