BART (large sized model), fine tuned on CNN Daily Mail BART model pre trained on English language, and fine tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence to Sequence Pre training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart). Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. Model description BART is a transformer encoder encoder (seq2seq) model with a bidirectional (BERT like) encoder and an autoregressive (GPT like) decoder. BART is pre trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. BART is particularly effective when fine tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine tuned on CNN Daily Mail, a large collection of text summary pairs. Intended uses & limitations You can use this model for text summarizat…
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