### Model Overview
A RoBERTa encoder network.

This network implements a bi-directional Transformer-based encoder as
described in ["RoBERTa: A Robustly Optimized BERT Pretraining Approach"](https://arxiv.org/abs/1907.11692).
It includes the embedding lookups and transformer layers, but does not
include the masked language model head used during pretraining.

The default constructor gives a fully customizable, randomly initialized
RoBERTa encoder with any number of layers, heads, and embedding
dimensions. To load preset architectures and weights, use the `from_preset()`
constructor.

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](https://github.com/facebookresearch/fairseq).


__Arguments__


- __vocabulary_size__: int. The size of the token vocabulary.
- __num_layers__: int. The number of transformer layers.
- __num_heads__: int. The number of attention heads for each transformer.
    The hidden size must be divisible by the number of attention heads.
- __hidden_dim__: int. The size of the transformer encoding layer.
- __intermediate_dim__: int. The output dimension of the first Dense layer in
    a two-layer feedforward network for each transformer.
- __dropout__: float. Dropout probability for the Transformer encoder.
- __max_sequence_length__: int. The maximum sequence length this encoder can
    consume. The sequence length of the input must be less than
    `max_sequence_length` default value. This determines the variable
    shape for positional embeddings.

### Example Usage
```python
import keras
import keras_nlp
import numpy as np
```

Raw string data.
```python
features = ["The quick brown fox jumped.", "I forgot my homework."]
labels = [0, 3]

# Pretrained classifier.
classifier = keras_nlp.models.RobertaClassifier.from_preset(
    "${VARIATION_SLUG}",
    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.
```python
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_nlp.models.RobertaClassifier.from_preset(
    "${VARIATION_SLUG}",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)
```

## Example Usage with HuggingFace uri

```python
import keras
import keras_nlp
import numpy as np
```

Raw string data.
```python
features = ["The quick brown fox jumped.", "I forgot my homework."]
labels = [0, 3]

# Pretrained classifier.
classifier = keras_nlp.models.RobertaClassifier.from_preset(
    "${VARIATION_SLUG}",
    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.
```python
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_nlp.models.RobertaClassifier.from_preset(
    "${VARIATION_SLUG}",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)
```