# xfbai /AMRBART-large-finetuned-AMR3.0-AMRParsing

 --- language: en tags: - AMRBART license: mit --- ## AMRBART-large-finetuned-AMR3.0-AMRParsing This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR3.0 dataset. It achieves a Smatch of 84.2 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 [AMR3.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 55,635 training instances, 1,722 validation instances, and 1,898 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-AMR3.0-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" }