ruT5-base-pls

ruT5-base-pls

A fine-tuned version of ai-forever/ruT5-base (formerly sberbank-ai/ruT5-base) for text simplification on Russian text: given a complex sentence, the model generates a simpler paraphrase that preserves its meaning.

Training data

• RuSimpleSentEval
• RuAdapt

Training metrics

From train.logs:

⚠️ A BLEU score of exactly 100.0 is unusual for a generation task and typically indicates the reported score was computed on a degenerate case (e.g. reference == input, or evaluation on the training set itself) rather than genuine model quality — worth re-checking on a proper held-out test set before treating this as the model's real performance.

Usage

import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "r1char9/ruT5-base-pls"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

input_text = """Война Советского Союза против фашистской Германии и её союзников
              (Венгрии, Италии, Румынии, Словакии, Хорватии, Финляндии, Японии);
              составная часть Второй мировой войны 1939-1945 гг."""


def example(source, model, tokenizer):
    """
    Simplify a complex text with the model.
    :param source: complex input text
    :param model: the model
    :param tokenizer: the tokenizer
    :return: simplified text generated by the model
    """
    print(f"SOURCE: {source}")
    input_ids, attention_mask = tokenizer(source, return_tensors="pt").values()
    with torch.no_grad():
        output = model.generate(
            input_ids=input_ids.to(model.device),
            attention_mask=attention_mask.to(model.device),
            max_new_tokens=input_ids.size(1) * 2,
            min_length=0,
        )
    return tokenizer.decode(output.squeeze(0), skip_special_tokens=True)


example(input_text, model, tokenizer)

Limitations

• FKGL (Flesch–Kincaid Grade Level) of 31.931 is far higher than typical simplified-text targets (usually single digits) — worth confirming this metric was computed correctly before drawing conclusions from it.
• No license has been specified for this model card — add the appropriate license field before publishing, based on the licenses of the base model and the training datasets used.