BART (large sized model) BART model pre trained on English language. 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. 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 decoder (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). Intended uses & limitations You can use the raw model for text infilling. However, the model is mostly meant to be fine tuned on a supervised dataset. See the model hub to look for fine tuned versions on a task that interests you. How to use Here is how to use this model in PyTorch: BibTeX entry and cita…
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