README.md

---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- ru
- en
---

# bge-m3 model for english and russian

This is a tokenizer shrinked version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3).

The current model has only English and Russian tokens left in the vocabulary.
Thus, the vocabulary is 21% of the original, and number of parameters in the whole model is 63.3% of the original, without any loss in the quality of English and Russian embeddings.
<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('TatonkaHF/bge-m3_en_ru')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
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()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('TatonkaHF/bge-m3_en_ru')
model = AutoModel.from_pretrained('TatonkaHF/bge-m3_en_ru')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, 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'])

print("Sentence embeddings:")
print(sentence_embeddings)
```

## Specs 

Other bge-m3 models are also shrinked.

| Model name                |
|---------------------------|
| [bge-m3-retromae_en_ru](https://huggingface.co/TatonkaHF/bge-m3-retromae_en_ru)     |
| [bge-m3-unsupervised_en_ru](https://huggingface.co/TatonkaHF/bge-m3-unsupervised_en_ru) |
| [bge-m3_en_ru](https://huggingface.co/TatonkaHF/bge-m3_en_ru)              |


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Reference:

Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, Zheng Liu. [BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://arxiv.org/abs/2402.03216).

Inspired by [LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru) and [https://discuss.huggingface.co/t/tokenizer-shrinking-recipes/8564/1](https://discuss.huggingface.co/t/tokenizer-shrinking-recipes/8564/1).

License: [mit](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md)

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