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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** is a pretrained semantic parser which converts a sentence into an abstract meaning graph. You may find our paper [here](https://arxiv.org/pdf/2203.07836.pdf) (Arxiv). The original implementation is avaliable [here](https://github.com/goodbai-nlp/AMRBART/tree/acl2022) |
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[](https://paperswithcode.com/sota/amr-to-text-generation-on-ldc2017t10?p=graph-pre-training-for-amr-parsing-and-1) |
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[](https://paperswithcode.com/sota/amr-to-text-generation-on-ldc2020t02?p=graph-pre-training-for-amr-parsing-and-1) |
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[](https://paperswithcode.com/sota/amr-parsing-on-ldc2017t10?p=graph-pre-training-for-amr-parsing-and-1) |
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[](https://paperswithcode.com/sota/amr-parsing-on-ldc2020t02?p=graph-pre-training-for-amr-parsing-and-1) |
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**News**🎈 |
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- (2022/12/10) fix max_length bugs in AMR parsing and update results. |
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- (2022/10/16) release the AMRBART-v2 model which is simpler, faster, and stronger. |
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# Requirements |
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+ python 3.8 |
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+ pytorch 1.8 |
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+ transformers 4.21.3 |
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+ datasets 2.4.0 |
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+ Tesla V100 or A100 |
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We recommend to use conda to manage virtual environments: |
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``` |
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conda env update --name <env> --file requirements.yml |
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``` |
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# Data Processing |
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<!-- Since AMR corpus require LDC license, we upload some examples for format reference. If you have the license, feel free to contact us for getting the preprocessed data. --> |
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You may download the AMR corpora at [LDC](https://www.ldc.upenn.edu). |
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Please follow [this respository](https://github.com/goodbai-nlp/AMR-Process) to preprocess AMR graphs: |
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``` |
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bash run-process-acl2022.sh |
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``` |
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# Usage |
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Our model is avaliable at [huggingface](https://huggingface.co/xfbai). Here is how to initialize a AMR parsing model in PyTorch: |
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``` |
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from transformers import BartForConditionalGeneration |
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from model_interface.tokenization_bart import AMRBartTokenizer # We use our own tokenizer to process AMRs |
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model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing-v2") |
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tokenizer = AMRBartTokenizer.from_pretrained("xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing-v2") |
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``` |
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