|
--- |
|
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](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. |
