language:
- multilingual
license: apache-2.0
library_name: pytorch
tags:
- machine-translation
- mt-evaluation
- quality-estimation
- reference-free
- pairwise-ranking
- mbr
- pear
metrics:
- pear
base_model: microsoft/infoxlm-large
pipeline_tag: translation
🍐 PEAR: Pairwise Evaluation for Automatic Relative Scoring in Machine Translation
Installation
Install the PEAR inference toolkit from the GitHub repository:
git clone https://github.com/prosho-97/pear.git
cd pear
pip install .
Quick start: pairwise QE scoring
import pear
metric = pear.load_metric("pear") # resolves to this Hugging Face model
scores = pear.score_pairwise(
metric,
sources=["The cat is on the mat."],
translations_a=["El gato está en la alfombra."],
translations_b=["El gato está en el mapa."],
batch_size=16,
gpus=1,
progress_bar=True,
)
Positive scores prefer translations_a; negative scores prefer translations_b.
To score both candidate orders:
scores = pear.score_pairwise(
metric,
sources=["The cat is on the mat."],
translations_a=["El gato está en la alfombra."],
translations_b=["El gato está en el mapa."],
mode="both",
)
# {"forward": [...], "reverse": [...]}
Reference-anchored PEAR
PEAR can also be used with a human reference, or any other anchor translation, as the second candidate:
scores = pear.score_reference_anchored(
metric,
sources=["The cat is on the mat."],
translations=["El gato está en la alfombra."],
references=["El gato está sobre la alfombra."],
batch_size=16,
)
As in pairwise QE scoring, mode="both" is available for both-order reference-anchored inference.
PEAR for MBR decoding
from pear.mbr import pear_utility_matrix, select_mbr_hypothesis
metric = pear.load_metric("pear")
source = "Questa è una traduzione molto buona."
hypotheses = [
"This is a good translation.",
"This is a very good translation.",
"This is a bad translation.",
]
utility = pear_utility_matrix(metric, source, hypotheses, mode="half", batch_size=16)
index, expected_utility = select_mbr_hypothesis(utility)
print(hypotheses[index], expected_utility)
Use mode="full" for all off-diagonal ordered pairs, or mode="half" to score only one triangular half and fill the opposite direction by PEAR antisymmetry.
CLI examples
Pairwise TSV input must contain src, mt_0, and mt_1 columns:
pear score --model pear --input pairs.tsv --output scored.tsv --batch-size 16
pear score --hf-model Prosho/pear --input pairs.tsv --output scored.tsv --batch-size 16
Reference-anchored TSV input must contain src, mt, and ref columns:
pear score --model pear --mode reference --input refs.tsv --output scored.tsv --batch-size 16
MBR input JSONL rows must contain src and hypotheses:
pear mbr --model pear --input nbest.jsonl --output selected.jsonl --utility half --batch-size 16
Citation
If you use this model, please cite the PEAR paper:
@inproceedings{proietti-etal-2026-pear,
title = "{PEAR}: Pairwise Evaluation for Automatic Relative Scoring in Machine Translation",
author = "Proietti, Lorenzo and
Grundkiewicz, Roman and
Post, Matt",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1953/",
doi = "10.18653/v1/2026.acl-long.1953",
pages = "42189--42207",
ISBN = "979-8-89176-390-6"
}