ltscore 1.0.1

A multilingual grammaticality util based on LanguageTool

LTScore

Cysill wrapper to score Welsh texts' grammaticality. As it connects to the Cysill API, ensure to have a working internet connexion before using it.

Installation:

pip install ltscore

Run the Docker image locally

You can download the LanguageTool image from Docker Hub, then using the port 8010 as indicated below.

docker pull erikvl87/languagetool
docker run --rm -p 8010:8010 erikvl87/languagetool

For more details about the Docker container's configuration, see this link.

LTScore as a CLI

There are two ways to use the CLI, either by entering a text file's path, or directly a text. The CLI returns a string of numbers representing the grammaticality score of the text.

# With a text as a positional character
ltscore -l br "Kalz a tud a zo amañ!"

# With a -p or --path flag
ltscore -l br -p ./src/ltscore/assets/text-sample-br.txt 

or alternatively with the pipe operator:

cat src/ltscore/assets/text-sample-br.txt | ltscore -l br

JSON file processing

Given a file data.ndjson with the following content:

{"source": "'Mañ an dud o tont.", "target": "Les gens arrivent.", "prediction": "Les gens vient."}
{"source": "Un devezh dilabour eo Lun Fask.", "target": "Le lundi de Pâques est un jour férié.", "prediction": "Le lundi de Pâques ai un jours fériée."}

running ltscore -t prediction -l fr -p data.ndjson and will get the file updated in the following way:

{"source":"'Mañ an dud o tont.","target":"Les gens arrivent.","prediction":"Les gens vient.","ltscore":33.333333333333336,"mistake_categories":["CAT_GRAMMAIRE"]}
{"source":"Un devezh dilabour eo Lun Fask.","target":"Le lundi de Pâques est un jour férié.","prediction":"Le lundi de Pâques ai un jours fériée.","ltscore":25.0,"mistake_categories":["CAT_HOMONYMES_PARONYMES","AGREEMENT"]}

LTScore as a library

One can also use this package as a python library:

from ltscore import get_score, get_mistakes

# get the scores
df["scores"] = df.apply(lambda row: get_score(row["text"], language=row["language"]), axis=1)
# returns a list of mistakes for each text
df["mistakes"] = df.apply(lambda row: get_mistakes(row["text"], language=row["language"]), axis=1)