This is a pytorch version of the universal-sentence-encoder-cmlm/multilingual-base-br model. It can be used to map 109 languages to a shared vector space. As the model is based LaBSE, it perform quite comparable on downstream tasks.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

python

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/use-cmlm-multilingual')
embeddings = model.encode(sentences)
print(embeddings)

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Normalize()
)

Citing & Authors

Have a look at universal-sentence-encoder-cmlm/multilingual-base-br for the respective publication that describes this model.