LaBSE/README.md

language, pipeline_tag, tags, library_name, license

language	pipeline_tag	tags	library_name	license
multilingual af sq am ar hy as az eu be bn bs bg my ca ceb zh co hr cs da nl en eo et fi fr fy gl ka de el gu ht ha haw he hi hmn hu is ig id ga it ja jv kn kk km rw ko ku ky lo la lv lt lb mk mg ms ml mt mi mr mn ne no ny or fa pl pt pa ro ru sm gd sr st sn si sk sl so es su sw sv tl tg ta tt te th bo tr tk ug uk ur uz vi cy wo xh yi yo zu	sentence-similarity	sentence-transformers feature-extraction sentence-similarity	sentence-transformers	apache-2.0

LaBSE

This is a port of the LaBSE model to PyTorch. It can be used to map 109 languages to a shared vector space.

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:

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

model = SentenceTransformer('sentence-transformers/LaBSE')
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': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (3): Normalize()
)

Citing & Authors

Have a look at LaBSE for the respective publication that describes LaBSE.