metadata

language:
  - multilingual
  - ar
  - bg
  - ca
  - cs
  - da
  - de
  - el
  - en
  - es
  - et
  - fa
  - fi
  - fr
  - gl
  - gu
  - he
  - hi
  - hr
  - hu
  - hy
  - id
  - it
  - ja
  - ka
  - ko
  - ku
  - lt
  - lv
  - mk
  - mn
  - mr
  - ms
  - my
  - nb
  - nl
  - pl
  - pt
  - ro
  - ru
  - sk
  - sl
  - sq
  - sr
  - sv
  - th
  - tr
  - uk
  - ur
  - vi
language_bcp47:
  - fr-ca
  - pt-br
  - zh-cn
  - zh-tw
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers

sentence-transformers/distiluse-base-multilingual-cased-v2

This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v2')
embeddings = model.encode(sentences)
print(embeddings)

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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})
  (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

Citing & Authors

This model was trained by sentence-transformers.

If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "http://arxiv.org/abs/1908.10084",
}