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---
language: en
tags:
- AMRBART
license: mit
---
## AMRBART (large-sized model)
AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https://arxiv.org/pdf/2203.07836.pdf) by bai et al. in ACL 2022 and first released in [this repository](https://github.com/muyeby/AMRBART).
## Model description
AMRBART follows the BART model which uses a transformer encoder-encoder architecture. AMRBART is pre-trained with 6 tasks:
+ learning to reconstruct the text based on the corrupted text.
+ learning to reconstruct AMR graphs based on the corrupted AMR graph.
+ learning to reconstruct the text based on the corrupted text and its corresponding AMR graph.
+ learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding text.
+ learning to reconstruct the text based on the corrupted text and its corresponding corrupted AMR graph.
+ learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding corrupted text.
AMRBART is particularly effective when fine-tuned for AMR parsing and AMR-to-text generation tasks.
## Training data
The AMRBART model is pre-trained on [AMR3.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 55,635
training instances and [English Gigaword](https://catalog.ldc.upenn.edu/LDC2003T05) (we randomly sampled 200,000 sentences).
## Intended uses & limitations
You can use the raw model for either AMR encoding or AMR parsing, but it's mostly intended to
be fine-tuned on a downstream task.
## How to use
Here is how to initialize this model in PyTorch:
```python
from transformers import BartForConditionalGeneration
model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-large")
```
Please refer to [this repository](https://github.com/muyeby/AMRBART) for tokenizer initialization and data preprocessing.
## BibTeX entry and citation info
Please cite this paper if you find this model helpful
```bibtex
@inproceedings{bai-etal-2022-graph,
title = "Graph Pre-training for {AMR} Parsing and Generation",
author = "Bai, Xuefeng and
Chen, Yulong and
Zhang, Yue",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "todo",
doi = "todo",
pages = "todo"
}
``` |