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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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## AMRBART-large-finetuned-AMR3.0-AMRParsing |
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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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## Model description |
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Same with AMRBART. |
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## Training data |
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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, 1,722 validation instances, and 1,898 test instances. |
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## Intended uses & limitations |
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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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## How to use |
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Here is how to initialize this model in PyTorch: |
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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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## BibTeX entry and citation info |
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Please cite this paper if you find this model helpful |
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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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``` |
