BART: Denoising Sequence-to-Sequence Pre-training for NLG & Translation (Explained)

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BART is a powerful model that can be used for many different text generation tasks, including summarization, machine translation, and abstract question answering. It could also be used for text classification and token classification. This video explains the architecture of BART and how it leverages 6 different pre-training objectives to achieve excellence.

BERT explained

Transformer Architecture Explained

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

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  1. Deep Learning Explainer
  2. deep learning in nlp
  3. nlp
  4. nlproc
  5. BART Denoising Sequence-to-Sequence Pre-training
  6. Pre-training for NaturalLanguage Generation
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Комментарии
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Understood very clearly! after this reading a research paper is very easy for me Thanks!

thepresistence
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Can this model read pdf data and learn from that when i till it to? Or is there anything that can do that in the web ui rather than having to train it through lora? I am very new to the matter but i would have a good use for some text generation that can sumarize my books

aphilzitrone
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dang this is a great video very clearly explained thanks!

lilyap
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great video!
what is the software for the floating cam?

amsterdan
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How to calculate rouge score, i am getting recall, precision and f values while using rouge-package. Is there any formula to calculate rouge score so that i can get values as you get

shahidvalishaik
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I never understand how bert, bart works...these language models work....and its so scary

chdhc