aymara 0.5.0b6

Python bindings to the LIMA linguistic analyzer

LIMA python bindings

Introducing LIMA

LIMA is a multilingual linguistic analyzer developed by the CEA LIST, LASTI laboratory (French acronym for Text and Image Semantic Analysis Laboratory). LIMA is Free Software, available under the MIT license.

LIMA has state of the art performance for more than 60 languages thanks to its recent deep learning (neural network) based modules. But it includes also a very powerful rules based mechanism called ModEx allowing to quickly extract information (entities, relations, events…) in new domains where annotated data does not exist.

For more information, detailed installation instructions and documentation, please refer to the LIMA Wiki.

Installation

LIMA python bindings are currently available under Linux only (x86_64).

Under Linux with python >= 3.7 and < 4, and upgraded pip:

At time of writing, the current version cannot be installed using pip because the generated Python Wheel is larger than the limit. We are waiting for a validation of our demand for a higher limit. That's why instructions below ask you to download yourself the wheel and install it from your filesystem.

# Upgrading pip is fundamental in order to obtain the correct LIMA version
$ pip install --upgrade pip
$ wget https://github.com/aymara/lima-python/releases/download/continuous/aymara-0.5.0b6-cp37-abi3-manylinux_2_28_x86_64.whl
$ pip install ./aymara-0.5.0b6-cp37-abi3-manylinux_2_28_x86_64.whl
$ lima_models.py -l eng
# Either simply use the lima command to produce an analysis of a file in CoNLLU format:
$ lima <path to the file to analyse>
# Or use the python API:
$ python
>>> import aymara.lima
>>> nlp = aymara.lima.Lima("ud-eng")
>>> doc = nlp('Hello, World!')
>>> print(doc[0].lemma)
hello
>>> print(repr(doc))
1       Hello   hello   INTJ    _       _               0       root    _       Pos=0|Len=5
2       ,       ,       PUNCT   _       _               1       punct   _       Pos=5|Len=1
3       World   World   PROPN   _       Number:Sing     1       vocative        _       Pos=7|Len=5
4       !       !       PUNCT   _       _               1       punct   _       Pos=12|Len=1

To use the deeplima pipelines (improved but experimental), you will have to install with both lima_models and deeplima_models. Check the user manual for more information about using these models.

Running

$ lima <utf-8 text file to analyze>
# OR
$ python
Python 3.8.10 (default, Nov 26 2021, 20:14:08)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import aymara.lima
>>> l = aymara.lima.Lima("ud-eng")
>>> r = l.analyzeText("The author wrote a novel.", lang="ud-eng")
>>> print(r)
# sent_id = 1
# text = The author wrote a novel.
1       The     the     DET     _       Definite=Def|PronType=Art       2       det     _       Len=3|Pos=1
2       author  author  NOUN    _       Number=Sing     3       nsubj   _       Len=6|Pos=5
3       wrote   write   VERB    _       Mood=Ind|Tense=Past|VerbForm=Fin        0       root    _       Len=5|Pos=12
4       a       a       DET     _       Definite=Ind|PronType=Art       5       det     _       Len=1|Pos=18
5       novel   novel   NOUN    _       Number=Sing     3       obj     _       Len=5|Pos=20|SpaceAfter=No
6       .       .       PUNCT   _       _       3       punct   _       Len=1|Pos=25

>>>

Note that some error messages could be displayed during the Lima object instantiation. If you get a valid result, you can ignore them. Most of them are debug messages that will be removed in a later version.

You can replace the language (ud-eng) used by eng to use the legacy pipeline. This is the same for ud-fra and fre. Note that legacy pipelines do not use the Universal Dependencies tagset, but a proprietary one.

Python bindings API documentation

The Lima python API documentation is on readthedocs.

Configuration and customization

To configure finely LIMA for your needs, follow the same instructions as for the native C++ tools, available here: [[https://github.com/aymara/lima/wiki/LIMA-User-Manual]].

PySide LIMA python bindings build and deploy instructions

Building the wheel

Use docker using the gbuild-manylinux_2_28.sh script:

./gbuild-manylinux_2_28.sh

Deploying the wheel

Use Twine (pip install twine) to deploy the whell to PyPI with the help of the deploy-manylinux_2_28.sh script.

./deploy-manylinux_2_28.sh

Developper Notes

To see the wheels accepted on current system:

>>> from packaging.tags import sys_tags
>>> [str(t) for t in sys_tags()]

See:

• https://cibuildwheel.readthedocs.io/en/stable/
• https://wiki.qt.io/Qt_for_Python
• https://www.qt.io/blog/2018/05/31/write-python-bindings

License notes

Some part of this API are largely inspired by the spaCy one and some text of the documentation are reproduced from the spaCy documentation. spaCy is also released under the MIT license. We thus reproduce its copyright notice below:

The MIT License (MIT)

Copyright (C) 2016-2022 ExplosionAI GmbH, 2016 spaCy GmbH, 2015 Matthew Honnibal

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.