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Getting started

This page takes you from nothing to a first analysis. Pick the path that suits you; both produce CoNLL-U output.

The quickest way to try LIMA, under Linux x86_64 with Python ≥ 3.7:

# A recent pip is required to get the right wheel
pip install --upgrade pip
pip install aymara==0.5.0b6

Install the English models, then analyze a text from Python:

lima_models -i eng

>>> 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

The package also installs a lima command that analyzes files:

lima my-text.txt

Continue with Using LIMA from Python.

The Docker image contains the latest LIMA built from the master branch:

docker pull aymara/lima-ubuntu22.04:latest
docker run -it --rm -v "$PWD":/data aymara/lima-ubuntu22.04:latest bash

Inside the container, install the models of a treebank and analyze a file:

lima_models.py -l eng-UD_English-EWT
analyzeText -l eng-UD_English-EWT -p deepud /data/my-text.txt

lima_models.py -l eng would pick the best English treebank and print the exact -l value to use. Continue with Language models and the command line.

What next?