readme: ---
  language: ["ru", "en"]
  tags:
  - russian
  - fill-mask
  - pretraining
  - embeddings
  - masked-lm
  - tiny
  - feature-extraction
  - sentence-similarity
  license: mit
  widget:
  - text: "Миниатюрная модель для [MASK] разных задач."
  ---
  This is a very small distilled version of the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model for Russian and English (45 MB, 12M parameters). There is also an **updated version of this model**, [rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2), with a larger vocabulary and better quality on practically all Russian NLU tasks.
  
  This model is useful if you want to fine-tune it for a relatively simple Russian task (e.g. NER or sentiment classification), and you care more about speed and size than about accuracy. It is approximately x10 smaller and faster than a base-sized BERT. Its `[CLS]` embeddings can be used as a sentence representation aligned between Russian and English. 
  
  It was trained on the [Yandex Translate corpus](https://translate.yandex.ru/corpus), [OPUS-100](https://huggingface.co/datasets/opus100) and [Tatoeba](https://huggingface.co/datasets/tatoeba), using MLM loss (distilled from [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)), translation ranking loss, and `[CLS]` embeddings distilled from [LaBSE](https://huggingface.co/sentence-transformers/LaBSE), [rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence), Laser and USE.
  
  There is a more detailed [description in Russian](https://habr.com/ru/post/562064/). 
  
  Sentence embeddings can be produced as follows:
  
  ```python
  # pip install transformers sentencepiece
  import torch
  from transformers import AutoTokenizer, AutoModel
  tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny")
  model = AutoModel.from_pretrained("cointegrated/rubert-tiny")
  # model.cuda()  # uncomment it if you have a GPU
  
  def embed_bert_cls(text, model, tokenizer):
      t = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
      with torch.no_grad():
          model_output = model(**{k: v.to(model.device) for k, v in t.items()})
      embeddings = model_output.last_hidden_state[:, 0, :]
      embeddings = torch.nn.functional.normalize(embeddings)
      return embeddings[0].cpu().numpy()
  
  print(embed_bert_cls('привет мир', model, tokenizer).shape)
  # (312,)
  ```