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---
license: cc-by-4.0
language:
- it
---
# GeNTE Evaluator
The **Gender-Neutral Translation (GeNTE) Evaluator** is a sequence classification model used for evaluating inclusive translations into Italian for the [GeNTE corpus](https://huggingface.co/datasets/FBK-MT/GeNTE).
It is built by fine-tuning the RoBERTa-based [UmBERTo model](https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1).
More details on the training process and the reproducibility can be found in the [official repository](https://github.com/hlt-mt/fbk-NEUTR-evAL/blob/main/solutions/GeNTE.md) ad the [paper](https://aclanthology.org/2023.emnlp-main.873/).
## Usage
You can use the GeNTE Evaluator as follows:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# load the tokenizer of UmBERTo
tokenizer = AutoTokenizer.from_pretrained("Musixmatch/umberto-wikipedia-uncased-v1", do_lower_case=False)
# load GeNTE Evaluator
model = AutoModelForSequenceClassification.from_pretrained("FBK-MT/GeNTE-evaluator")
# neutral example
sample = "Condividiamo il parere di chi ha presentato la relazione
che ha posto notevole enfasi sull'informazione in relazione ai rischi e sulla trasparenza,
in particolare nel campo sanitario e della sicurezza."
input = tokenizer(sample, return_tensors='pt')
with torch.no_grad():
probs = model(**input).logits
predicted_label = torch.argmax(probs, dim=1).item()
print(predicted_label) # 0 is neutral, 1 is gendered
```
## Citation
```
@inproceedings{piergentili-etal-2023-hi,
title = "Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the {G}e{NTE} Corpus",
author = "Piergentili, Andrea and
Savoldi, Beatrice and
Fucci, Dennis and
Negri, Matteo and
Bentivogli, Luisa",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.873",
doi = "10.18653/v1/2023.emnlp-main.873",
pages = "14124--14140",
}
```
## Contributions
Thanks to [@dfucci](https://huggingface.co/dfucci) for adding this model. |