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  license: mit
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language: en
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+ tags:
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+ - AMRBART
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  license: mit
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  ---
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+
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+ ## AMRBART-large-finetuned-AMR3.0-AMRParsing
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+
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+ 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.
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+
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+ ## Model description
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+ Same with AMRBART.
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+
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+ ## Training data
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+
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+ The model is finetuned on [AMR3.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 55,635
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+ training instances, 1722 validation instances, and 1898 test instances.
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+
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+ ## Intended uses & limitations
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+
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+ You can use the model for AMR parsing, but it's mostly intended to be used in the domain of News.
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+
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+ ## How to use
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+ Here is how to initialize this model in PyTorch:
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+
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+ ```python
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+ from transformers import BartForConditionalGeneration
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+ model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing")
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+ ```
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+ Please refer to [this repository](https://github.com/muyeby/AMRBART) for tokenizer initialization and data preprocessing.
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+
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+
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+ ## BibTeX entry and citation info
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+ Please cite this paper if you find this model helpful
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+
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+ ```bibtex
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+ @inproceedings{bai-etal-2022-graph,
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+ title = "Graph Pre-training for {AMR} Parsing and Generation",
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+ author = "Bai, Xuefeng and
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+ Chen, Yulong and
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+ Zhang, Yue",
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+ booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
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+ month = may,
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+ year = "2022",
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+ address = "Online",
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+ publisher = "Association for Computational Linguistics",
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+ url = "todo",
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+ doi = "todo",
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+ pages = "todo"
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+ }
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+ ```