wehub-resource-sync
54 lines
1.3 KiB
Markdown
54 lines
1.3 KiB
Markdown
---
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jupytext:
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formats: ipynb,md:myst
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text_representation:
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extension: .md
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format_name: myst
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format_version: '0.8'
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jupytext_version: 1.4.2
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kernelspec:
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display_name: Python 3
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language: python
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name: python3
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---
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# amr2text
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AMR captures “who is doing what to whom” in a sentence. Each sentence is represented as a rooted, directed, acyclic graph with labels on edges (relations) and leaves (concepts).
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The goal of AMR-to-Text Generation is to recover the original sentence realization given an AMR. This task can be seen as the reverse of the structured prediction found in AMR parsing.
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Before loading an AMR model, make sure to install HanLP with the `amr` dependencies:
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```shell
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pip install hanlp[amr] -U
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```
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To generate a sentence given an AMR:
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```{eval-rst}
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.. margin:: Batching is Faster
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.. Hint:: Generate multiple sentences at once for faster speed!
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```
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```{code-cell} ipython3
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:tags: [output_scroll]
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import hanlp
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generation = hanlp.load(hanlp.pretrained.amr2text.AMR3_GRAPH_PRETRAIN_GENERATION)
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print(generation('''
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(z0 / want-01
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:ARG0 (z1 / boy)
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:ARG1 (z2 / believe-01
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:ARG0 (z3 / girl)
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:ARG1 z1))
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'''))
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```
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All the pre-trained parsers and their scores are listed below.
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```{eval-rst}
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.. automodule:: hanlp.pretrained.amr2text
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:members:
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``` |