Keras
Keras 3 API 문서
KerasHub
Pretrained Models
XLM-RoBERTa
XLMRobertaTextClassifier model

XLMRobertaTextClassifier model

source

XLMRobertaTextClassifier class 

keras_hub.models.XLMRobertaTextClassifier(
    backbone,
    num_classes,
    preprocessor=None,
    activation=None,
    hidden_dim=None,
    dropout=0.0,
    **kwargs
)

An end-to-end XLM-RoBERTa model for classification tasks.

This model attaches a classification head to a keras_hub.model.XLMRobertaBackbone instance, mapping from the backbone outputs to logits suitable for a classification task. For usage of this model with pre-trained weights, see the from_preset() constructor.

This model can optionally be configured with a preprocessor layer, in which case it will automatically apply preprocessing to raw inputs during fit(), predict(), and evaluate(). This is done by default when creating the model with from_preset().

Disclaimer: Pre-trained models are provided on an “as is” basis, without warranties or conditions of any kind. The underlying model is provided by a third party and subject to a separate license, available here.

Arguments

  • backbone: A keras_hub.models.XLMRobertaBackbone instance.
  • num_classes: int. Number of classes to predict.
  • preprocessor: A keras_hub.models.XLMRobertaTextClassifierPreprocessor or None. If None, this model will not apply preprocessing, and inputs should be preprocessed before calling the model.
  • activation: Optional str or callable. The activation function to use on the model outputs. Set activation="softmax" to return output probabilities. Defaults to None.
  • hidden_dim: int. The size of the pooler layer.
  • dropout: float. The dropout probability value, applied to the pooled output, and after the first dense layer.

Examples

Raw string data.

features = ["The quick brown fox jumped.", "نسيت الواجب"]
labels = [0, 3]
# Pretrained classifier.
classifier = keras_hub.models.XLMRobertaTextClassifier.from_preset(
    "xlm_roberta_base_multi",
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)
classifier.predict(x=features, batch_size=2)
# Re-compile (e.g., with a new learning rate).
classifier.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(5e-5),
    jit_compile=True,
)
# Access backbone programmatically (e.g., to change `trainable`).
classifier.backbone.trainable = False
# Fit again.
classifier.fit(x=features, y=labels, batch_size=2)

Preprocessed integer data.

features = {
    "token_ids": np.ones(shape=(2, 12), dtype="int32"),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
labels = [0, 3]
# Pretrained classifier without preprocessing.
classifier = keras_hub.models.XLMRobertaTextClassifier.from_preset(
    "xlm_roberta_base_multi",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)

Custom backbone and vocabulary.

features = ["The quick brown fox jumped.", "نسيت الواجب"]
labels = [0, 3]
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
)
preprocessor = keras_hub.models.XLMRobertaTextClassifierPreprocessor(
    tokenizer,
    sequence_length=128,
)
backbone = keras_hub.models.XLMRobertaBackbone(
    vocabulary_size=250002,
    num_layers=4,
    num_heads=4,
    hidden_dim=256,
    intermediate_dim=512,
    max_sequence_length=128,
)
classifier = keras_hub.models.XLMRobertaTextClassifier(
    backbone=backbone,
    preprocessor=preprocessor,
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)

source

from_preset method 

XLMRobertaTextClassifier.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_hub.models.Task 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 Task 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 a task specific base class like keras_hub.models.CausalLM.from_preset(), or from a model class like keras_hub.models.BertTextClassifier.from_preset(). If calling from the a 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, saved weights will be loaded into the model architecture. If False, all weights will be randomly initialized.

Examples

# Load a Gemma generative task.
causal_lm = keras_hub.models.CausalLM.from_preset(
    "gemma_2b_en",
)
# Load a Bert classification task.
model = keras_hub.models.TextClassifier.from_preset(
    "bert_base_en",
    num_classes=2,
)
Preset nameParametersDescription
xlm_roberta_base_multi277.45M12-layer XLM-RoBERTa model where case is maintained. Trained on CommonCrawl in 100 languages.
xlm_roberta_large_multi558.84M24-layer XLM-RoBERTa model where case is maintained. Trained on CommonCrawl in 100 languages.

backbone property 

keras_hub.models.XLMRobertaTextClassifier.backbone

A keras_hub.models.Backbone model with the core architecture.

preprocessor property 

keras_hub.models.XLMRobertaTextClassifier.preprocessor

A keras_hub.models.Preprocessor layer used to preprocess input.