readme: ---
  language: en
  tags:
  - AMRBART
  license: mit
  ---
  
  ## AMRBART-large-finetuned-AMR2.0-AMRParsing
  
  This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR2.0 dataset. It achieves a Smatch of 85.4 on the evaluation set: More details are 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.
  
  ## Model description
  Same with AMRBART.
  
  ## Training data
  
  The model is finetuned on [AMR2.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 36,521
  training instances, 1,368 validation instances, and 1,371 test instances.
  
  ## Intended uses & limitations
  
  You can use the model for AMR parsing, but it's mostly intended to be used in the domain of News. 
  
  ## How to use
  Here is how to initialize this model in PyTorch:
  
  ```python
  from transformers import BartForConditionalGeneration
  model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-large-finetuned-AMR2.0-AMRParsing")
  ```
  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"
  }
  ```