---
language:
- multilingual
license: apache-2.0
library_name: pytorch
datasets:
- Prosho/pear-data
base_model: microsoft/infoxlm-large
tags:
- machine-translation
- mt-evaluation
- quality-estimation
- reference-free
- pairwise-ranking
- mbr
- pear
---
🍐 PEAR: Pairwise Evaluation for Automatic Relative Scoring in Machine Translation
[](https://2026.aclweb.org/)
[](https://aclanthology.org/2026.acl-long.1953/)
[](https://arxiv.org/abs/2601.18006)
[](https://pypi.org/project/pear-mt/)
[](https://github.com/prosho-97/pear)
[](https://huggingface.co/collections/Prosho/pear)
[](https://www.apache.org/licenses/LICENSE-2.0)
## Model variant
This repository contains the PEAR checkpoint trained without Knowledge Distillation (KD).
## Installation
PEAR supports Python 3.11, 3.12, and 3.13. Install the published inference toolkit from PyPI:
```bash
python -m pip install pear-mt
```
The distribution is named `pear-mt`; the Python import package and primary CLI
command are both named `pear`.
To install from source instead:
```bash
git clone https://github.com/prosho-97/pear.git
cd pear
python -m pip install .
```
## Quick start: pairwise QE scoring
```python
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="auto",
progress_bar=True,
)
```
Positive scores prefer `translations_a`; negative scores prefer
`translations_b`.
To score both candidate orders:
```python
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 use a human reference, or any other anchor translation, as the
second candidate:
```python
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
```python
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:
```bash
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:
```bash
pear score --model pear --mode reference --input refs.tsv --output scored.tsv --batch-size 16
```
MBR input JSONL rows must contain `src` and `hypotheses`:
```bash
pear mbr --model pear --input nbest.jsonl --output selected.jsonl --utility half --batch-size 16
```
Use `--gpus 0` to force CPU inference. The default, `--gpus auto`, selects one
available CUDA or MPS accelerator.
## Citation
If you use this model, please cite the PEAR paper:
```bibtex
@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"
}
```
## Links
- Package:
- Paper:
- Code:
- Dataset:
- Collection:
- PEAR-XL: