| --- |
| 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) |
| ``` |
|
|
|
|
|
|
| ## 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. |
|
|
|
|
