Bart Base

🚀 BART (base-sized model)

BART is a pre-trained model on the English language. It addresses natural language generation, translation, and comprehension tasks. This model offers a powerful solution for various NLP applications, leveraging its unique encoder - decoder architecture.

🚀 Quick Start

BART is a transformer encoder - decoder (seq2seq) model. It features a bidirectional (BERT - like) encoder and an autoregressive (GPT - like) decoder. It's pre - trained by corrupting text with an arbitrary noising function and then learning to reconstruct the original text.

💻 Usage Examples

Basic Usage

from transformers import BartTokenizer, BartModel

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

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

last_hidden_states = outputs.last_hidden_state

✨ Features

BART is particularly effective when fine - tuned for text generation (e.g., summarization, translation). It also performs well in comprehension tasks (e.g., text classification, question answering).

📚 Documentation

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. Check the model hub to find fine - tuned versions for tasks that interest you.

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 released under the Apache 2.0 license.