Bart Architecture

Architecture of BART

BART (Bidirectional and Auto-Regressive Transformers) is a powerful transformer-based model developed by Facebook AI. It ingeniously combines the strengths of both BERT's bidirectional encoder and GPT's autoregressive decoder to achieve versatile text generation and understanding capabilities.

How BART Works

BART operates as a denoising autoencoder. Its primary function is to reconstruct original, clean text from a corrupted or noisy version of that text. This is achieved through a sophisticated encoder-decoder architecture.

Encoder

The encoder component of BART is bidirectional. This means it processes the input text by reading it in both left-to-right and right-to-left directions simultaneously.

• Input: Corrupted text.

• Function: Generates a rich, contextualized representation of the input text by considering the relationships between all tokens.

Decoder

The decoder component of BART is unidirectional and operates in an autoregressive manner, processing text from left-to-right.

• Input: The contextualized representation generated by the encoder.

• Function: Uses this representation to reconstruct the original, uncorrupted text sequence token by token.

Training Objective

BART is trained to minimize a reconstruction loss. Specifically, it employs cross-entropy loss between the output of the decoder and the original, clean text sequence. This objective forces the model to learn how to accurately generate coherent and correct text from various forms of corrupted input, thereby mastering text generation tasks.

Architectural Variants

BART is available in different sizes, offering a trade-off between model capacity and computational resources:

• BART-base: Features 6 encoder layers and 6 decoder layers.

• BART-large: Features 12 encoder layers and 12 decoder layers.

Key Difference from BERT

The fundamental architectural difference between BART and BERT lies in their output mechanisms and the tasks they are primarily designed for:

• BERT: Primarily uses an encoder-only architecture. It excels at understanding text by predicting masked tokens directly, often using a classification head for downstream tasks.

• BART: Employs an encoder-decoder architecture. While the encoder processes corrupted text for contextual understanding, the decoder is crucial for generating new sequences, making it well-suited for text generation, summarization, and translation tasks. BART leverages its encoder-decoder structure to reconstruct the original sequence from a corrupted input.

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• BART-base vs BART-large

• Differences between BERT and BART

Interview Questions

• What are the main components of the BART model?

• How does BART’s encoder differ from its decoder?

• What does it mean that BART is a denoising autoencoder?

• How is the BART model trained?

• Can you explain the difference between BART-base and BART-large?

• How does BART combine the strengths of BERT and GPT architectures?

• What is the key architectural difference between BART and BERT?

• Why does BART use both an encoder and a decoder?

• How does BART handle corrupted input text during training?

• What type of loss function is used to train BART?