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This is a paraphraser for Russian sentences described in this Habr post.

It is recommended to use the model with the encoder_no_repeat_ngram_size argument:

from transformers import T5ForConditionalGeneration, T5Tokenizer
MODEL_NAME = 'cointegrated/rut5-base-paraphraser'
model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)

def paraphrase(text, beams=5, grams=4, do_sample=False):
    x = tokenizer(text, return_tensors='pt', padding=True).to(model.device)
    max_size = int(x.input_ids.shape[1] * 1.5 + 10)
    out = model.generate(**x, encoder_no_repeat_ngram_size=grams, num_beams=beams, max_length=max_size, do_sample=do_sample)
    return tokenizer.decode(out[0], skip_special_tokens=True)

print(paraphrase('Каждый охотник желает знать, где сидит фазан.'))
# Все охотники хотят знать где фазан сидит.
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Dataset used to train cointegrated/rut5-base-paraphraser