cointegrated_rubert-tiny2

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language:

• ru pipeline_tag: sentence-similarity tags:
• russian
• fill-mask
• pretraining
• embeddings
• masked-lm
• tiny
• feature-extraction
• sentence-similarity
• sentence-transformers
• transformers license: mit widget:
• text: Миниатюрная модель для [MASK] разных задач.

---

This is an updated version of cointegrated/rubert-tiny: a small Russian BERT-based encoder with high-quality sentence embeddings. This post in Russian gives more details.

The differences from the previous version include:

• a larger vocabulary: 83828 tokens instead of 29564;
• larger supported sequences: 2048 instead of 512;
• sentence embeddings approximate LaBSE closer than before;
• meaningful segment embeddings (tuned on the NLI task)
• the model is focused only on Russian.

The model should be used as is to produce sentence embeddings (e.g. for KNN classification of short texts) or fine-tuned for a downstream task.

Sentence embeddings can be produced as follows:

# pip install transformers sentencepiece
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2")
model = AutoModel.from_pretrained("cointegrated/rubert-tiny2")
# 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,)

Alternatively, you can use the model with sentence_transformers:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('cointegrated/rubert-tiny2')
sentences = ["привет мир", "hello world", "здравствуй вселенная"]
embeddings = model.encode(sentences)
print(embeddings)

For those who want to run the inference with VLLM, there is a vLLM-optimized version of this model: WpythonW/rubert-tiny2-vllm