sentence-transformers

sentence-transformers/LaBSE

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Model Documentation

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:

python
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.

Files & Weights

Filename	Size	Action
flax_model.msgpack	1.75 GB
model.safetensors	1.75 GB
pytorch_model.bin	1.75 GB