---
license: mit
language:
- ru
- en
tags:
- PyTorch
- Transformers
---

# ru-en RoBERTa large model for Sentence Embeddings in Russian and English.
The model is described [in this article](<link of our arxiv>)  
Russian MTEB [metrics](<lin of our ruMTEB>)

For better quality, use cls token embeddings. 
Also, use next prefixes for tasks:
- For assimethric retrieval tasks like search/QuestAnsw: "search_query: "/"search_document: ".
- NLI, NLU and paraphrasing tasks: "classification: ".
- Title body/abstract and clasterization: "clustering: ".
## Usage (HuggingFace Models Repository)
You can use the model directly from the model repository to compute sentence embeddings:
```python
from transformers import AutoTokenizer, AutoModel
import torch

#You might to use two variants of mode for embeddings creation:
#CLS token embs or MEAN Pooling.
#You can choose embs pooling with best quality for your downstream tasks. 

#Mean Pooling example - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
    return sum_embeddings / sum_mask

#Sentences we want sentence embeddings for
sentences = ['Привет! Как твои дела?',
             'А правда, что 42 твое любимое число?']
#Load AutoModel from huggingface model repository
tokenizer = AutoTokenizer.from_pretrained("ai-forever/ru-en-RoSBERTa")
model = AutoModel.from_pretrained("ai-forever/ru-en-RoSBERTa")
#Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=512, return_tensors='pt')

#Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)
    
#In this case, mean pooling
sentence_mean_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

#In this case, cls "pooling"
last_hidden_states = model_output[0]
sentence_cls_embeddings = last_hidden_states[:,0]

```