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--- |
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language: |
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- multilingual |
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- ar |
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- bg |
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- ca |
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- cs |
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- da |
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- de |
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- el |
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- en |
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- es |
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- et |
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- fa |
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- fi |
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- fr |
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- gl |
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- gu |
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- he |
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- hi |
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- hr |
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- hu |
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- hy |
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- id |
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- it |
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- ja |
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- ka |
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- ko |
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- ku |
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- lt |
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- lv |
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- mk |
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- mn |
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- mr |
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- ms |
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- my |
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- nb |
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- nl |
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- pl |
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- pt |
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- ro |
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- ru |
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- sk |
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- sl |
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- sq |
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- sr |
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- sv |
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- th |
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- tr |
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- uk |
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- ur |
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- vi |
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license: apache-2.0 |
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library_name: sentence-transformers |
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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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language_bcp47: |
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- fr-ca |
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- pt-br |
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- zh-cn |
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- zh-tw |
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pipeline_tag: sentence-similarity |
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--- |
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# sentence-transformers/distiluse-base-multilingual-cased-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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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/distiluse-base-multilingual-cased-v2') |
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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/distiluse-base-multilingual-cased-v2) |
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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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) |
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(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
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) |
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``` |
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## Citing & Authors |
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This model was trained by [sentence-transformers](https://www.sbert.net/). |
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If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "http://arxiv.org/abs/1908.10084", |
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} |
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``` |
