Sbert Large Nlu Ru

🚀 BERT large model (uncased) for Sentence Embeddings in Russian language

This is a BERT large model (uncased) designed for generating sentence embeddings in Russian. It offers high - quality embeddings and is described in detail in a related article.

🚀 Quick Start

The model is described in this article. For better quality, use mean token embeddings.

💻 Usage Examples

Basic Usage

You can use the model directly from the model repository to compute sentence embeddings:

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - 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/sbert_large_nlu_ru")
model = AutoModel.from_pretrained("ai-forever/sbert_large_nlu_ru")

#Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=24, return_tensors='pt')

#Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

#Perform pooling. In this case, mean pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

📄 License

This project is licensed under the MIT license.

👥 Authors

• SberDevices Team.
• Aleksandr Abramov: HF profile, Github, Kaggle Competitions Master;
• Denis Antykhov: Github;
• Ibragim Badertdinov: Github