---
title: bart-large
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/bart-large"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/bart-large.md"
repository: AI-ModelScope/bart-large
last_updated: 2023-11-01
license: apache-2.0
pipeline_tag: text2text-generation
tasks:
  - text2text-generation
model_type:
  - bart
architectures:
  - BartModel
library_name:
  - transformer
  - tensorflow
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "vllm 0.9.2"
downloads: 905
stars: 1
---

# bart-large

> bart-large - AI-ModelScope 在 ModelScope 开源的模型。BART (large-sized model)

AI-ModelScope/bart-large 是 ModelScope 魔搭社区上的text2text-generation模型，采用 apache-2.0 许可，可用 deploy_task text/emb、vllm 0.9.2 部署。

- **Repository**: AI-ModelScope/bart-large
- **License**: apache-2.0
- **Tasks**: text2text-generation
- **Inference backends**: deploy_task text/emb, vllm 0.9.2
- **Downloads**: 905
- **Stars**: 1
- **Last updated**: 2023-11-01

Source: https://www.modelscope.cn/models/AI-ModelScope/bart-large

---

# 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](https://arxiv.org/abs/1910.13461) 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-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](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model in PyTorch:

```python
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
```

### BibTeX entry and citation info

```bibtex
@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}
}
```