MEXMA

Usage

You use this model as you would any other XLM-RoBERTa model, taking into account that the "pooler" has not been trained, so you should use the CLS the encoder outputs directly as your sentence representation:

from transformers import AutoTokenizer, XLMRobertaModel

tokenizer = AutoTokenizer.from_pretrained("facebook/MEXMA")
model = XLMRobertaModel.from_pretrained("facebook/MEXMA", add_pooling_layer=False)
example_sentences = ['Sentence1', 'Sentence2']
example_inputs = tokenizer(example_sentences, return_tensors='pt')

outputs = model(**example_inputs)
sentence_representation = outputs.last_hidden_state[:, 0]
print(sentence_representation.shape) # torch.Size([2, 1024])

You can also use this model with SentenceTransformers:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("facebook/MEXMA")
example_sentences = ['Sentence1', 'Sentence2']
sentence_representation = model.encode(example_sentences)
print(sentence_representation.shape) # torch.Size([2, 1024])

License

This model is released under the MIT license.

Training code

For the training code of this model, please check the official MEXMA repo.

Paper

MEXMA: Token-level objectives improve sentence representations

Citation

If you use this model in your work, please cite:

@inproceedings{janeiro-etal-2025-mexma,
    title = "{MEXMA}: Token-level objectives improve sentence representations",
    author = "Janeiro, Jo{\~a}o Maria  and
      Piwowarski, Benjamin  and
      Gallinari, Patrick  and
      Barrault, Loic",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.1168/",
    doi = "10.18653/v1/2025.acl-long.1168",
    pages = "23960--23995",
    ISBN = "979-8-89176-251-0",
    abstract = "Cross-lingual sentence encoders (CLSE) create fixed-size sentence representations with aligned translations. Current pre-trained CLSE approaches use sentence-level objectives only. This can lead to loss of information, especially for tokens, which then degrades the sentence representation. We propose MEXMA, a novel approach that integrates both sentence-level and token-level objectives. The sentence representation in one language is used to predict masked tokens in another language, with both the sentence representation and *all tokens directly update the encoder*. We show that adding token-level objectives greatly improves the sentence representation quality across several tasks. Our approach outperforms current pre-trained cross-lingual sentence encoders on bitext mining as well as several downstream tasks. We also analyse the information encoded in our tokens, and how the sentence representation is built from them."
}