metadata

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. It is built by fine-tuning the RoBERTa-based UmBERTo model. More details on the training process and the reproducibility can be found in the official repository ad the paper.

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 for adding this model.