| Name | hopsparser JSON |
| Version |
0.7.1
JSON |
| download |
| home_page | |
| Summary | A graph dependency parser to tinker with |
| upload_time | 2023-11-16 20:53:21 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
nlp
parsing
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
HOPS, an honest parser of sentences
===================================
[](https://pypi.org/project/hopsparser)
[](https://github.com/hopsparser/hopsparser/actions?query=workflow%3ACI)
[](https://github.com/psf/black)
> It ain't much but it's honest work.
This is a graph-based dependency parser inspired by [Dozat and Manning
(2017)](https://nlp.stanford.edu/pubs/dozat2017deep.pdf)'s biaffine graph parser. Contrary to Dozat,
the parser performs its own tagging and can use several lexers such as FastText, BERT and others. It
has been originally designed within the [FlauBERT](https://github.com/getalp/Flaubert) initiative.
The parser comes with pretrained models ready for parsing French, but it might be trained for other
languages without difficulties.
See the [documentation](http://hopsparser.readthedocs.io) for more information.
## Citation
If you use this parser for your scientific publication, or if you find the resources in this
repository useful, please cite the following paper
```biblatex
@inproceedings{grobol:hal-03223424,
title = {{Analyse en dépendances du français avec des plongements contextualisés}},
author = {Grobol, Loïc and Crabbé, Benoît},
url = {https://hal.archives-ouvertes.fr/hal-03223424},
booktitle = {{Actes de la 28ème Conférence sur le Traitement Automatique des Langues Naturelles}},
eventtitle = {{TALN-RÉCITAL 2021}},
venue = {Lille, France},
pdf = {https://hal.archives-ouvertes.fr/hal-03223424/file/HOPS_final.pdf},
hal_id = {hal-03223424},
hal_version = {v1},
}
```
## Development
If you want a development install (so you can modify the code locally and directly run it), you can
install it in editable mode with the tests extras after cloning the repository
```sh
git clone https://github.com/hopsparser/hopsparser
cd hopsparser
pip install -e ".[tests,traintools]"
```
In that case, you can run the smoke tests with `tox` to ensure that everything works on your end.
Note that using the editable mode requires `pip >= 21.3.1`.
## Licence
This software is released under the MIT Licence, with some files released under compatible free
licences, see [LICENCE.md](LICENCE.md) for the details.
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