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--- |
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language: |
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- multilingual |
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- af |
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- sq |
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- am |
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- ar |
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- hy |
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- as |
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- az |
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- eu |
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- be |
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- bn |
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- bs |
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- bg |
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- my |
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- ca |
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- ceb |
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- zh |
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- co |
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- hr |
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- cs |
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- da |
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- nl |
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- en |
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- eo |
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- et |
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- fi |
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- fr |
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- fy |
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- gl |
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- ka |
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- de |
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- el |
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- gu |
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- ht |
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- ha |
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- haw |
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- he |
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- hi |
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- hmn |
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- hu |
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- is |
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- ig |
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- id |
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- ga |
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- it |
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- ja |
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- jv |
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- kn |
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- kk |
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- km |
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- rw |
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- ko |
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- ku |
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- ky |
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- lo |
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- la |
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- lv |
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- lt |
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- lb |
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- mk |
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- mg |
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- ms |
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- ml |
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- mt |
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- mi |
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- mr |
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- mn |
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- ne |
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- no |
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- ny |
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- or |
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- fa |
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- pl |
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- pt |
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- pa |
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- ro |
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- ru |
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- sm |
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- gd |
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- sr |
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- st |
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- sn |
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- si |
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- sk |
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- sl |
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- so |
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- es |
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- su |
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- sw |
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- sv |
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- tl |
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- tg |
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- ta |
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- tt |
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- te |
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- th |
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- bo |
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- tr |
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- tk |
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- ug |
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- uk |
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- ur |
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- uz |
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- vi |
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- cy |
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- wo |
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- xh |
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- yi |
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- yo |
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- zu |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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license: apache-2.0 |
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--- |
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|
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# LaBSE |
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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. |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('sentence-transformers/LaBSE') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Evaluation Results |
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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) |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
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(3): Normalize() |
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) |
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``` |
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## Citing & Authors |
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Have a look at [LaBSE](https://tfhub.dev/google/LaBSE/1) for the respective publication that describes LaBSE. |
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