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sentence-embedding-LaBSE

Sentence Similarity
sentence-transformers
PyTorch
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bert
feature-extraction
text-embeddings-inference
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sentence-embedding-LaBSE / README.md
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metadata
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
  - transformers
license: 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)

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

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.