Instructions to use xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing") model = AutoModelForSeq2SeqLM.from_pretrained("xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing") - Notebooks
- Google Colab
- Kaggle
Xuefeng Bai commited on
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README.md
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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,
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## Intended uses & limitations
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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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