license: apache-2.0
language: en
BART (large-sized model)
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).
Weights shared here are effectively from facebook/bart-large but with added noise for BOS embedding to assist the finetuning.
Intended uses & limitations
There have been quite a few issues related to finetuning BART for text generation, and this repo implements solution discussed in #15559. Particularly adding some noise to pre-trained model's BOS embedding. This seems to solve the problem of endless BOS generation for a finetuned BART model.
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:
from transformers import BartTokenizer, BartModel
tokenizer = BartTokenizer.from_pretrained('vedu/bart-large-perturbed')
model = BartModel.from_pretrained('vedu/bart-large-perturbed')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
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}
}