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README (3).md

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+---
+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
+---
+
+# LaBSE
+This is a port of the [LaBSE](https://tfhub.dev/google/LaBSE/1) 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](https://www.SBERT.net) 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)
+```
+
+
+
+## Evaluation Results
+
+
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/LaBSE)
+
+
+
+## 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](https://tfhub.dev/google/LaBSE/1) for the respective publication that describes LaBSE.
+