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Cataloged from sentence-transformers/LaBSE
All models by sentence-transformers →This is a port of the LaBSE model to PyTorch. It can be used to map 109 languages to a shared vector space.
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
Have a look at LaBSE for the respective publication that describes LaBSE.
pip install -U sentence-transformersfrom sentence_transformers import SentenceTransformersentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/LaBSE')embeddings = model.encode(sentences)print(embeddings)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())