sentence-transformers_LaBSE

---

language:

• 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 pipeline_tag: sentence-similarity tags:
• sentence-transformers
• feature-extraction
• sentence-similarity library_name: sentence-transformers license: apache-2.0

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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.