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xendalm/
ru-summary-quality-metric

Name: ru-summary-quality-metric
Availability: InStock

safetensors

summarization-evaluation

summarization-quality

ai-forever/ruT5-large

finetune

apache-2.0

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ru-summary-quality-metric

This model is a fine-tuned version of ai-forever/ruT5-large, was trained for binary quality assessment of Russian summaries when paired with their original texts.

Important: model uses a non-standard approach, adapting a Seq2Seq model for a binary classification task. It was trained to predict a specific token as the target sequence. This approach directly follows the methodology used by the authors of the original SEAHORSE paper.

Data and Training Metric

The model was fine-tuned on SEAHORSE dataset. SEAHORSE is a multilingual dataset for summarization evaluation with human annotations across 6 different quality metrics (Q1-Q6).

This specific model focuses on Q6 Conciseness metric. According to SEAHORSE paper authors, Q6 is considered one of the most high-level and challenging quality metrics.

Training Data: ru and en subsets of training split, filtered for conciseness labels.
Evaluation Data: only ru subset of validation and test splits.

Evaluation Results

Test set	Pearson Correlation	ROC AUC
All	0.479	0.792
≥ 20 summary words	0.459	0.781

Usage

Input format: "текст:\n {} саммари:\n {}"

Target sequences:

ZERO_TOKEN='▁0' for label 0 (not concise)
ONE_TOKEN='▁1' for label 1 (concise)

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import numpy as np

model_name = "xendalm/ru-summary-quality-metric"

tokenizer = AutoTokenizer.from_pretrained(model_name)
zero_token_id = tokenizer('▁0', add_special_tokens=False).input_ids[0]
one_token_id = tokenizer('▁1', add_special_tokens=False).input_ids[0]

model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
device = "cuda"
model.to(device)
model.eval()

def predict_conciseness_score(text, summary, tokenizer, model, device, zero_token_id, one_token_id):
    input_text = f"текст:\n {text} саммари:\n {summary}"

    inputs = tokenizer(input_text, return_tensors="pt", max_length=2048, truncation=True, padding=True)
    inputs = {k: v.to(device) for k, v in inputs.items()}

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=1,
            num_beams=1,
            do_sample=False,
            return_dict_in_generate=True,
            output_scores=True
        )

        first_token_logits = outputs.scores[0].squeeze(0)

        logit_0 = first_token_logits[zero_token_id]
        logit_1 = first_token_logits[one_token_id]

        probability_of_one = torch.sigmoid(logit_1 - logit_0).item()

    return probability_of_one

xendalm/ru-summary-quality-metric

ru-summary-quality-metric

Data and Training Metric

Evaluation Results

Usage

Links

xendalm/
ru-summary-quality-metric