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A python library that makes AMR parsing, generation and visualization simple.
| Date | Stars |
|---|---|
| 2026-07-24 | 268 |
| 2026-07-25 | 267 |
| 2026-07-28 | 267 |
| 2026-07-30 | 267 |
| 2026-08-06 | 267 |
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# amrlib
**A python library that makes AMR parsing, generation and visualization simple.**
For the latest documentation, see **[ReadTheDocs](https://amrlib.readthedocs.io/en/latest/)**.
**!! Note:** The models must be downloaded and installed separately. See the [Installation Instructions](https://amrlib.readthedocs.io/en/latest/install).
## About
amrlib is a python module designed to make processing for [Abstract Meaning Representation](https://amr.isi.edu/)
(AMR) simple by providing the following functions
* Sentence to Graph (StoG) parsing to create AMR graphs from English sentences.
* Graph to Sentence (GtoS) generation for turning AMR graphs into English sentences.
* A QT based GUI to facilitate conversion of sentences to graphs and back to sentences
* Methods to plot AMR graphs in both the GUI and as library functions
* Training and test code for both the StoG and GtoS models.
* A [SpaCy](https://github.com/explosion/spaCy) extension that allows direct conversion of
SpaCy `Docs` and `Spans` to AMR graphs.
* Sentence to Graph alignment routines
- FAA_Aligner (Fast_Align Algorithm), based on the ISI aligner code detailed in this
[paper](https://www.isi.edu/~damghani/papers/amr_eng_align.pdf).
- RBW_Aligner (Rule Based Word) for simple, single token to single node alignment
* An evaluation metric API including including...
- Smatch (multiprocessed with enhanced/detailed scores) for graph parsing
<br> *see note at the bottom about smatch scoring*
- BLEU for sentence generation
- Alignment scoring metrics detailing precision/recall
## AMR Models
The system includes different neural-network models for parsing and for generation. **!! Note:** Models must be downloaded and installed separately.
See [amrlib-models](https://github.com/bjascob/amrlib-models) for all parse and generate model download links.
* Parse (StoG) model_parse_xfm_bart_large gives an **83.7 SMATCH score** with LDC2020T02.
<br> For a technical description of the parse model see its [wiki-page](https://github.com/bjascob/amrlib/wiki/The-parse_xfm-model)
* Generation (GtoS) generate_t5wtense gives a **54 BLEU** with tense tags or **44 BLEU** with un-tagged LDC2020T02.
## AMR View
The GUI allows for simple viewing, conversion and plotting of AMR Graphs.
<!--- docs/images/AMRView01.png --->
<!--- https://github.com/bjascob/amrlib/raw/master/docs/images/AMRView01.png --->
## AMR CoReference Resolution
The library does not contain code for AMR co-reference resolution but there is a related project
at [amr_coref](https://github.com/bjascob/amr_coref).
The following papers have GitHub projects/code that have similar or better scoring than the above..
* [VGAE as Cheap Supervision for AMR Coreference Resolution](https://github.com/IreneZihuiLi/VG-AMRCoref)
* [End-to-end AMR Coreference Resolution](https://github.com/Sean-Blank/AMRcoref)
## Requirements and Installation
The project was built and tested under Python 3 and Ubuntu but should run on any Linux, Windows, Mac, etc.. system.
See [Installation Instructions](https://amrlib.readthedocs.io/en/latest/install) for details on setup.
## Library Usage
To convert sentences to graphs
```
import amrlib
stog = amrlib.load_stog_model()
graphs = stog.parse_sents(['This is a test of the system.', 'This is a second sentence.'])
for graph in graphs:
print(graph)
```
To convert graphs to sentences
```
import amrlib
gtos = amrlib.load_gtos_model()
sents, _ = gtos.generate(graphs)
for sent in sents:
print(sent)
```
For a detailed description see the [Model API](https://amrlib.readthedocs.io/en/latest/api_model/).
## Usage as a Spacy Extension
To use as an extension, you need spaCy version 2.0 or later. To setup the extension and use it do the following
```
import amrlib
import spacy
amrlib.setup_spacy_extension()
nlp = spacy.load('en_core_web_sm')
doc = nlp('This is a test of the SpaCy extensioExcerpt of 5,099 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5e58c1638f61b29f, topic:neural-network, topic:pytorch
matched fp:5e58c1638f61b29f, topic:transformer
matched fp:5e58c1638f61b29f, topic:spacy