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

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

This model is a fine-tuned version of 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 by bai et al. in ACL 2022.

## Model description

Same with AMRBART.

## Training data

The model is finetuned on AMR3.0, 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:

from transformers import BartForConditionalGeneration
model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing")


Please refer to this repository for tokenizer initialization and data preprocessing.

## BibTeX entry and citation info

@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",