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README.md
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## BART (Bidirectional and Auto-Regressive Transformer) Architecture
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BART’s primary task is used to generate clean semantically coherent text from corrupted text data but it can also be used for a variety of different NLP sub-tasks like language translation, question-answering tasks, text summarization, paraphrasing, etc.
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As BART is an autoencoder model, it consists of an encoder model and a decoder model. For its encoder model, BART uses a bi-directional encoder that is used in BERT, and for its decoder mode, it uses an autoregressive decoder that forms the core aspect of a GPT -1 model.
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## BART (Bidirectional and Auto-Regressive Transformer) Architecture
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BART’s primary task is used to generate clean semantically coherent text from corrupted text data but it can also be used for a variety of different NLP sub-tasks like language translation, question-answering tasks, text summarization, paraphrasing, etc.
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As BART is an autoencoder model, it consists of an encoder model and a decoder model. For its encoder model, BART uses a bi-directional encoder that is used in BERT, and for its decoder mode, it uses an autoregressive decoder that forms the core aspect of a GPT -1 model.
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