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This is the rut5-base model, with the decoder fine-tuned to recover (approximately) Russian sentences from their LaBSE embeddings. Details are here (in Russian).

It can be used, for example, for:

  • Paraphrasing Russian sentences;
  • Translating from the 109 LaBSE languages to Russian;
  • Summarizing a collection of sentences with a single sentence;
  • Interpolating between sentences;
  • Few-shot text style transfer (including cross-lingual).

Example code:

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoModel
from transformers.modeling_outputs import BaseModelOutput

enc_tokenizer = AutoTokenizer.from_pretrained('cointegrated/LaBSE-en-ru')
encoder = AutoModel.from_pretrained('cointegrated/LaBSE-en-ru')

dec_tokenizer = AutoTokenizer.from_pretrained('cointegrated/rut5-base-labse-decoder')
decoder = AutoModelForSeq2SeqLM.from_pretrained('cointegrated/rut5-base-labse-decoder')

def encode(texts):
    encoded_input = enc_tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors='pt')
    with torch.no_grad():
        model_output = encoder(**encoded_input.to(encoder.device))
        embeddings = model_output.pooler_output
        embeddings = torch.nn.functional.normalize(embeddings)
    return embeddings
# encode some texts into vectors
embeddings = encode([
    "4 декабря 2000 года",
    "Давно такого не читала, очень хорошо пишешь!",
    "Я тогда не понимала, что происходит, не понимаю и сейчас.",
    "London is the capital of Great Britain.",
# torch.Size([4, 768])

# now try to recover the texts from the vectors
out = decoder.generate(
for tokens in out:
    print(dec_tokenizer.decode(tokens, skip_special_tokens=True))
# После 4 декабря 2000 года
# Не так давно, это многое читала!
# Я не понимала того, что происходит сейчас тогда, дальше.
# Британская столица Англии.
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