readme: ---
  language: en
  tags:
  - AMRBART
  license: mit
  ---
  
  ## AMRBART (base-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-base")
  ```
  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"
  }
  ```