Bart Large

🚀 BART (Large-sized Model)

A pre-trained BART model on the English language, useful for various NLP tasks such as text generation and comprehension.

🚀 Quick Start

The BART model is pre - trained on English. 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.

✨ Features

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.

💻 Usage Examples

Basic Usage

from transformers import BartTokenizer, BartModel

tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
model = BartModel.from_pretrained('facebook/bart-large')

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state

📚 Documentation

BibTeX entry and citation info

@article{DBLP:journals/corr/abs-1910-13461,
  author    = {Mike Lewis and
               Yinhan Liu and
               Naman Goyal and
               Marjan Ghazvininejad and
               Abdelrahman Mohamed and
               Omer Levy and
               Veselin Stoyanov and
               Luke Zettlemoyer},
  title     = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
               Generation, Translation, and Comprehension},
  journal   = {CoRR},
  volume    = {abs/1910.13461},
  year      = {2019},
  url       = {http://arxiv.org/abs/1910.13461},
  eprinttype = {arXiv},
  eprint    = {1910.13461},
  timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

📄 License

This model is licensed under the Apache - 2.0 license.