sentence-transformers_distiluse-base-multilingual-cased-v2

---

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 license: apache-2.0 library_name: sentence-transformers tags:
• sentence-transformers
• feature-extraction
• sentence-similarity language_bcp47:
• fr-ca
• pt-br
• zh-cn
• zh-tw pipeline_tag: sentence-similarity

---

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)

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",
}