gemba 0.1.0

GEMBA — GPT Estimation Metric Based Assessment

GEMBA-MQM and GEMBA-DA

Setup

Install required packages with python >= 3.8 (tested with 3.12.7)

pip install -r requirements.txt

Set up secrets either for Azure API or OpenAI API:

export OPENAI_AZURE_ENDPOINT=
export OPENAI_AZURE_KEY=

or

export OPENAI_API_KEY=

Scoring with GEMBA

Install the gemba package with pip install gemba and use the following code:

from gemba import get_gemba_scores

source = ["Hello, how are you?", "I am fine, thank you.", "I am not fine, thank you."]
hypothesis = ["Hallo, wie geht es dir?", "Ich bin gut, danke.", "Ich bin Adolf, wer bist du?"]
source_lang = "en"
target_lang = "de"

answers, errors = get_gemba_scores(source, hypothesis, source_lang, target_lang, method="GEMBA-MQM_norm", model="gpt-4o")

for answer, error in zip(answers, errors):
    print(answer, error)

Alternatively, you can run the main file on two text files. It assumes two files with the same number of lines. It prints the score for each line pair:

python main.py --source=source.txt --hypothesis=hypothesis.txt --source_lang=English --target_lang=Czech --method="GEMBA-MQM" --model="gpt-4"

The main recommended methods: GEMBA-MQM and GEMBA-DA with the model gpt-4.

Collecting and evaluating experiments for GEMBA-DA

Get mt-metric-eval and download resources:

git clone https://github.com/google-research/mt-metrics-eval.git
cd mt-metrics-eval
pip install .
alias mtme='python3 -m mt_metrics_eval.mtme'
mtme --download
cd ..
mv ~/.mt-metrics-eval/mt-metrics-eval-v2 mt-metrics-eval-v2

Collect data and run the scorer

python gemba_da.py 

export PYTHONPATH=mt-metrics-eval:$PYTHONPATH
python evaluate.py

License

GEMBA code and data are released under the CC BY-SA 4.0 license.

Paper

You can read more about GEMBA-DA in our arXiv paper or GEMBA-MQM in our arXiv paper.

How to Cite

GEMBA-MQM

@inproceedings{kocmi-federmann-2023-gemba-mqm,
    title = {GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4},
    author = {Kocmi, Tom  and Federmann, Christian},
    booktitle = "Proceedings of the Eighth Conference on Machine Translation",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
}

GEMBA-DA

@inproceedings{kocmi-federmann-2023-large,
    title = "Large Language Models Are State-of-the-Art Evaluators of Translation Quality",
    author = "Kocmi, Tom and Federmann, Christian",
    booktitle = "Proceedings of the 24th Annual Conference of the European Association for Machine Translation",
    month = jun,
    year = "2023",
    address = "Tampere, Finland",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2023.eamt-1.19",
    pages = "193--203",
}