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In this article, we have explored the differences between two state of the art NLP models namely BERT and BART.
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Natural Language Processing (NLP) is a rapidly growing field that aims to enable machines to understand and generate human language. One of the most important tasks in NLP is language understanding, which involves analyzing and interpreting text. In recent years, transformer-based models such as BERT and BART have emerged as powerful tools for natural language understanding. In this article, we will compare and contrast BERT and BART, two state-of-the-art models for natural language understanding.
BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model that was introduced by Google in 2018. BERT is trained on a massive amount of text data using a technique called unsupervised pre-training. This allows BERT to learn general-purpose language representations that can be fine-tuned for specific tasks such as question answering and sentiment analysis. BERT has achieved state-of-the-art results on a wide range of NLP tasks and is widely used in industry and academia.
BART (Denoising Autoencoder from Transformer) is a transformer-based model that was introduced by Facebook AI in 2020. Like BERT, BART is also pre-trained on a large amount of text data. However, unlike BERT, BART is trained to reconstruct the original sentence from a corrupted version of it, which is called denoising autoencoding. This allows BART to learn more robust representations of the text and to handle more complex language tasks. BART has also achieved state-of-the-art results on a wide range of NLP tasks and is also widely used in industry and academia.
One of the main differences between BERT and BART is the pre-training task. BERT is trained on a task called masked language modeling, where certain words in the input text are replaced with a special token, and the model is trained to predict the original words. On the other hand, BART is trained on a task called denoising autoencoding, where the input text is corrupted with random tokens, and the model is trained to reconstruct the original text. This difference in pre-training tasks leads to different strengths and weaknesses of the two models.
BERT is known for its excellent performance on tasks that require understanding the context and relationships between words, such as question answering and sentiment analysis. BERT's pre-training task encourages the model to learn representations that are sensitive to the context in which words appear. This makes BERT well-suited for tasks that require understanding the meaning of words in different contexts.
On the other hand, BART is known for its excellent performance on tasks that require handling complex language, such as text summarization and machine translation. BART's pre-training task encourages the model to learn representations that are robust to noise and variations in the input text. This makes BART well-suited for tasks that require handling text that is noisy, ambiguous, or written in different languages.
Another difference between BERT and BART is the architecture of the transformer. BERT uses a transformer architecture with a multi-layer encoder, whereas BART uses a transformer architecture with a multi-layer encoder-decoder. This difference in architecture leads to different computational requirements and memory usage for the two models.
BERT has a relatively simple architecture, which makes it easy to use and run on a wide range of devices. However, BERT's architecture also makes it less suitable for tasks that require handling long sequences of text or that require generating text.
On the other hand, BART has a more complex architecture, which makes it more suitable for tasks that require handling long sequences of text or that require generating text.
|Model architecture||Transformer-based encoder-decoder||Transformer-based encoder-decoder with Masked LM|
|Pre-training||Unsupervised pre-training on large corpus of text||Supervised pre-training on a smaller corpus of text|
|Fine-tuning||Fine-tuning on specific task||Fine-tuning on specific task|
|Performance||Higher for language understanding tasks||Comparable for language understanding tasks|
|Use cases||Language understanding, language generation, text classification, Q&A||Text summarization, text completion, text generation|
In conclusion, both BERT and BART are powerful pre-trained models that have shown exceptional performance on various NLP tasks. BERT has set new state-of-the-art benchmarks on a wide range of NLP tasks, including sentiment analysis, named entity recognition, and question answering. BART, on the other hand, has shown promising results on text-to-text generation tasks such as summarization and machine translation. Both models have their strengths and weaknesses and the choice of which model to use will depend on the specific NLP task at hand.
- Lewis, Mike, et al. "Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension." arXiv preprint arXiv:1910.13461 (2019).
- Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding." arXiv preprint arXiv:1810.04805 (2018).
- “Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing.” Google Research, 2 Nov. 2018, ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html.