---
license: unlicense
pipeline_tag: sentence-similarity
language:
- ru
tags:
- PyTorch
- Transformers
- e-commerce
- encoder
---
A sentencepiece tokenizer was applied to a corpus of 269 million Russian search queries. 

The encoder-model was trained for the e-commerce search query similarity task, and the search queries were short.

The dataset for validation, which was manually annotated, comprised 362,000 instances.

![Validation results](https://huggingface.co/fkrasnov2/SBE/resolve/main/bvf_recall1k_query_len_eng.svg)


```python

## don't forget
# pip install protobuf sentencepiece

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained('fkrasnov2/SBE')
tokenizer = AutoTokenizer.from_pretrained('fkrasnov2/SBE')

input_ids = tokenizer.encode("чёрное платье", max_length=model.config.max_position_embeddings, truncation=True, return_tensors='pt')

model.eval()
vector = model(input_ids=input_ids, attention_mask=input_ids!=tokenizer.pad_token_id)[0][0,0]

assert model.config.hidden_size == vector.shape[0]
```

This model is designed for use in e-commerce IR and helps differentiate products.


**The same products**:
 - cos ( SBE("apple 16 синий про макс 256"), SBE("iphone 16 синий pro max 256") ) = 0.96 

 - cos ( SBE("iphone 15 pro max"), SBE("айфон 15 про макс") ) = 0.98

**Different products**:

- cos ( SBE("iphone 15 pro max"), SBE("iphone 16 pro max") ) = 0.85