metadata
pipeline_tag: translation
language: multilingual
library_name: transformers
license: cc-by-nc-sa-4.0
🛡️ Guardians of the Machine Translation Meta-Evaluation:
Sentinel Metrics Fall In!
This repository contains the SENTINELCAND metric model pre-trained on Direct Assessments (DA) annotations and further fine-tuned on Multidimensional Quality Metrics (MQM) data. For details on how to use our sentinel metric models, check our GitHub repository.
Usage
After having installed our repository package, you can use this model within Python in the following way:
from sentinel_metric import download_model, load_from_checkpoint
model_path = download_model("sapienzanlp/sentinel-cand-mqm")
model = load_from_checkpoint(model_path)
data = [
{"mt": "There's no place like home."},
{"mt": "Toto, I've a feeling we're not in Kansas anymore."}
]
output = model.predict(data, batch_size=8, gpus=1)
Output:
# Segment scores
>>> output.scores
[0.5421186089515686, 0.29804396629333496]
# System score
>>> output.system_score
0.4200812876224518
Cite this work
This work has been published at ACL 2024 (Main Conference). If you use any part, please consider citing our paper as follows:
@inproceedings{perrella-etal-2024-guardians,
title = "Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!",
author = "Perrella, Stefano and Proietti, Lorenzo and Scir{\`e}, Alessandro and Barba, Edoardo and Navigli, Roberto",
editor = "Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand", publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.856",
pages = "16216--16244",
}
License
This work is licensed under Creative Commons Attribution-ShareAlike-NonCommercial 4.0.