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keras/
bart_large_en_cnn

Name: bart_large_en_cnn
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Model Overview

BART encoder-decoder network.

This class implements a Transformer-based encoder-decoder model as described in "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension".

The default constructor gives a fully customizable, randomly initialized BART model 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.

Presets

The following model checkpoints are provided by the Keras team. Full code examples for each are available below.

Preset name	Parameters	Description
bart_base_en	139.42M	6-layer BART model where case is maintained. Trained on BookCorpus, English Wikipedia and CommonCrawl
bart_large_en	406.29M	12-layer BART model where case is maintained. Trained on BookCorpus, English Wikipedia and CommonCrawl.
bart_large_en_cnn	406.29M	The bart_large_en backbone model fine-tuned on the CNN+DM summarization dataset.

Arguments

vocabulary_size: int. The size of the token vocabulary.
num_layers: int. The number of transformer encoder layers and transformer decoder 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 and pooler layers.
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 that this encoder can consume. If None, max_sequence_length uses the value from sequence length. This determines the variable shape for positional embeddings.

Example Usage

python

import keras
import keras_hub
import numpy as np

Use generate() to do text generation, given an input context.

python

bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_large_en_cnn")
bart_lm.generate("The quick brown fox", max_length=30)

# Generate with batched inputs.
bart_lm.generate(["The quick brown fox", "The whale"], max_length=30)

Compile the generate() function with a custom sampler.

python

bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_large_en_cnn")
bart_lm.compile(sampler="greedy")
bart_lm.generate("The quick brown fox", max_length=30)

Use generate() with encoder inputs and an incomplete decoder input (prompt).

python

bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_large_en_cnn")
bart_lm.generate(
    {
        "encoder_text": "The quick brown fox",
        "decoder_text": "The fast"
    }
)

Use generate() without preprocessing.

python

# Preprocessed inputs, with encoder inputs corresponding to
# "The quick brown fox", and the decoder inputs to "The fast". Use
# `"padding_mask"` to indicate values that should not be overridden.
prompt = {
    "encoder_token_ids": np.array([[0, 133, 2119, 6219, 23602, 2, 1, 1]]),
    "encoder_padding_mask": np.array(
        [[True, True, True, True, True, True, False, False]]
    ),
    "decoder_token_ids": np.array([[2, 0, 133, 1769, 2, 1, 1]]),
    "decoder_padding_mask": np.array([[True, True, True, True, False, False]])
}

bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
    "bart_large_en_cnn",
    preprocessor=None,
)
bart_lm.generate(prompt)

Call fit() on a single batch.

python

features = {
    "encoder_text": ["The quick brown fox jumped.", "I forgot my homework."],
    "decoder_text": ["The fast hazel fox leapt.", "I forgot my assignment."]
}
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_large_en_cnn")
bart_lm.fit(x=features, batch_size=2)

Call fit() without preprocessing.

python

x = {
    "encoder_token_ids": np.array([[0, 133, 2119, 2, 1]] * 2),
    "encoder_padding_mask": np.array([[1, 1, 1, 1, 0]] * 2),
    "decoder_token_ids": np.array([[2, 0, 133, 1769, 2]] * 2),
    "decoder_padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = np.array([[0, 133, 1769, 2, 1]] * 2)
sw = np.array([[1, 1, 1, 1, 0]] * 2)

bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
    "bart_large_en_cnn",
    preprocessor=None,
)
bart_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)

Example Usage with Hugging Face URI

python

import keras
import keras_hub
import numpy as np

Use generate() to do text generation, given an input context.

python

bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_large_en_cnn")
bart_lm.generate("The quick brown fox", max_length=30)

# Generate with batched inputs.
bart_lm.generate(["The quick brown fox", "The whale"], max_length=30)

Compile the generate() function with a custom sampler.

python

bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_large_en_cnn")
bart_lm.compile(sampler="greedy")
bart_lm.generate("The quick brown fox", max_length=30)

Use generate() with encoder inputs and an incomplete decoder input (prompt).

python

bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_large_en_cnn")
bart_lm.generate(
    {
        "encoder_text": "The quick brown fox",
        "decoder_text": "The fast"
    }
)

Use generate() without preprocessing.

python

# Preprocessed inputs, with encoder inputs corresponding to
# "The quick brown fox", and the decoder inputs to "The fast". Use
# `"padding_mask"` to indicate values that should not be overridden.
prompt = {
    "encoder_token_ids": np.array([[0, 133, 2119, 6219, 23602, 2, 1, 1]]),
    "encoder_padding_mask": np.array(
        [[True, True, True, True, True, True, False, False]]
    ),
    "decoder_token_ids": np.array([[2, 0, 133, 1769, 2, 1, 1]]),
    "decoder_padding_mask": np.array([[True, True, True, True, False, False]])
}

bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
    "hf://keras/bart_large_en_cnn",
    preprocessor=None,
)
bart_lm.generate(prompt)

Call fit() on a single batch.

python

features = {
    "encoder_text": ["The quick brown fox jumped.", "I forgot my homework."],
    "decoder_text": ["The fast hazel fox leapt.", "I forgot my assignment."]
}
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_large_en_cnn")
bart_lm.fit(x=features, batch_size=2)

Call fit() without preprocessing.

python

x = {
    "encoder_token_ids": np.array([[0, 133, 2119, 2, 1]] * 2),
    "encoder_padding_mask": np.array([[1, 1, 1, 1, 0]] * 2),
    "decoder_token_ids": np.array([[2, 0, 133, 1769, 2]] * 2),
    "decoder_padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = np.array([[0, 133, 1769, 2, 1]] * 2)
sw = np.array([[1, 1, 1, 1, 0]] * 2)

bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
    "hf://keras/bart_large_en_cnn",
    preprocessor=None,
)
bart_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)

keras/bart_large_en_cnn

Model Overview

Links

Presets

Example Usage

Example Usage with Hugging Face URI

keras/
bart_large_en_cnn