| ---
|
| language:
|
| - ru
|
| license: mit
|
| size_categories:
|
| - 5K<n<10K
|
| task_categories:
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| - question-answering
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| pretty_name: RusFinQABenchmark
|
| tags:
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| - russian
|
| - finance
|
| - llm-evaluation
|
| - chain-of-thought
|
| - benchmarking
|
| ---
|
|
|
| # RusFinQABenchmark — Evaluation Results
|
|
|
| This dataset contains **evaluation results** for 8 open-weight large language models on the **RuFinQA** benchmark.
|
|
|
| ## 📊 Overview
|
|
|
| - **Total evaluated records:** 8,100
|
| - **Models:** 8
|
| - **Domains:** 17
|
| - **Topics:** 172
|
| - **Levels:** 3
|
|
|
| ## 🤖 Models Evaluated
|
|
|
| | Model | Records |
|
| |-------|---------|
|
| | **llama3.2:3b** | 1,100 |
|
| | **phi4-mini:3.8b** | 1,000 |
|
| | **qwen2.5:7b-instruct** | 1,000 |
|
| | **mistral:7b-instruct** | 1,000 |
|
| | **deepseek-r1:7b** | 1,000 |
|
| | **gemma3:4b** | 1,000 |
|
| | **llama3.1:8b** | 1,000 |
|
| | **aya-expanse:8b** | 1,000 |
|
|
|
| ---
|
|
|
| ## 📖 Data Sources, Licensing & Legal Notice
|
|
|
| ### Data Origin
|
|
|
| This dataset contains **model-generated outputs** and **evaluation metrics** produced by running open-weight large language models on the RuFinQA benchmark. The underlying questions and gold solutions are derived from the RuFinQA dataset.
|
|
|
| ### Ownership & Rights
|
|
|
| - **The evaluation results, metrics, and model generations** are released under the **MIT License**.
|
| - **The underlying benchmark questions and gold solutions** are subject to the original licensing terms of RuFinQA.
|
| - We **do not claim ownership** of the model outputs or the original financial texts used in the benchmark.
|
|
|
| ### Notice‑and‑Takedown Policy
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|
|
| We respect intellectual property rights. If you are a copyright owner and believe that your content appears in this dataset without proper authorization, please contact us. We will promptly remove the disputed entries upon verification.
|
|
|
| 📧 **Contact for takedown requests**: `marabov@kpfu.ru`
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| ⏱️ **Response time**: Within 14 business days.
|
|
|
| ---
|
|
|
| ## 📈 Key Performance Metrics (aggregated)
|
|
|
| | Metric | Mean | Std |
|
| |--------|------|-----|
|
| | `final_answer_match` | 0.62 | 0.37 |
|
| | `recall` | 0.71 | 0.29 |
|
| | `precision` | 0.68 | 0.31 |
|
| | `bertscore` | 0.83 | 0.11 |
|
| | `rouge1` | 0.58 | 0.22 |
|
| | `rougeL` | 0.54 | 0.23 |
|
|
|
| ---
|
|
|
| ## 📚 Domain Distribution
|
|
|
| | Domain (RU) | Domain (EN) | Records |
|
| |-------------|-------------|---------|
|
| | Ценные бумаги | Securities | 835 |
|
| | Финансовое регулирование | Financial Regulation | 659 |
|
| | Налоги | Taxation / Taxes | 555 |
|
| | Аннуитеты и вклады | Annuities and Deposits | 508 |
|
| | Финансовые рынки | Financial Markets | 507 |
|
| | Личные финансы | Personal Finance | 504 |
|
| | Процентные ставки | Interest Rates | 475 |
|
| | Кредиты и займы | Loans and Borrowing | 475 |
|
| | ESG и устойчивое финансирование | ESG and Sustainable Finance | 459 |
|
| | Крипто-финансы | Crypto Finance | 459 |
|
| | Слияния и поглощения (M&A) | Mergers and Acquisitions (M&A) | 456 |
|
| | Финансовые коэффициенты | Financial Ratios | 456 |
|
| | Управление рисками | Risk Management | 448 |
|
| | Амортизация | Depreciation / Amortization | 400 |
|
| | Инвестиционные проекты | Investment Projects | 360 |
|
| | Страхование и актуарные расчёты | Insurance and Actuarial Calculations | 272 |
|
| | Корпоративные финансы | Corporate Finance | 272 |
|
|
|
| ---
|
|
|
| ## 📊 Level Distribution
|
|
|
| | Level | Records |
|
| |-------|---------|
|
| | Intermediate | 3,582 |
|
| | Basic | 2,610 |
|
| | Advanced | 1,908 |
|
|
|
| ---
|
|
|
| ## 📝 Data Structure
|
|
|
| Each record contains:
|
|
|
| | Field | Type | Description |
|
| |-------|------|-------------|
|
| | `id` | string | Task identifier |
|
| | `level` | string | Basic / Intermediate / Advanced |
|
| | `domain` | string | Financial domain |
|
| | `topic` | string | Specific topic |
|
| | `model` | string | Model name |
|
| | `question` | string | Question (Russian) |
|
| | `solution` | string | Gold solution |
|
| | `steps` | list | Gold reasoning steps |
|
| | `final_answer` | float | Correct answer |
|
| | `model_generation` | string | Raw model output |
|
| | `recall` | float | Hard recall |
|
| | `precision` | float | Hard precision |
|
| | `final_answer_match` | int | Correct final answer (0/1) |
|
| | `fuzzy_*` | float | Fuzzy metrics |
|
| | `soft_*` | float | Soft metrics |
|
| | `dtw_*` | float | DTW metrics |
|
| | `bertscore` | float | BERTScore |
|
| | `rouge*` | float | ROUGE scores |
|
|
|
| ---
|
|
|
| ## 🚀 Usage
|
|
|
| ```python
|
| from datasets import load_dataset
|
|
|
| dataset = load_dataset("arabovs-ai-lab/RusFinQABenchmark", split="train")
|
| print(dataset[0])
|
| ```
|
|
|
| ### Example Record
|
|
|
| ```json
|
| {
|
| "id": "arith_COMP_0001_2025_roa",
|
| "level": "Intermediate",
|
| "domain": "Финансовые коэффициенты",
|
| "topic": "Рентабельность активов (ROA)",
|
| "model": "llama3.2:3b",
|
| "question": "Рассчитай рентабельность активов (ROA) для компании...",
|
| "solution": "ROA = 44.691 / 633.696 = 0.0705 (7.05%)",
|
| "steps": [...],
|
| "final_answer": 0.0705,
|
| "model_generation": "ROA = 44.69 / 633.70 = 0.0705",
|
| "recall": 0.92,
|
| "precision": 0.88,
|
| "final_answer_match": 1,
|
| "bertscore": 0.91,
|
| "rouge1": 0.84
|
| }
|
| ```
|
|
|
| ### Analyzing Results
|
|
|
| ```python
|
| import pandas as pd
|
| from datasets import load_dataset
|
|
|
| dataset = load_dataset("arabovs-ai-lab/RusFinQABenchmark", split="train")
|
|
|
| # Convert to DataFrame
|
| df = pd.DataFrame(dataset)
|
|
|
| # Calculate accuracy per model
|
| model_acc = df.groupby('model')['final_answer_match'].mean().sort_values(ascending=False)
|
| print(model_acc)
|
|
|
| # Filter by domain
|
| df_esg = df[df['domain'] == 'ESG и устойчивое финансирование']
|
| print(f"ESG domain accuracy: {df_esg['final_answer_match'].mean():.3f}")
|
| ```
|
|
|
| ---
|
|
|
| ## 📄 License
|
|
|
| MIT License — applies to evaluation results, metrics, and metadata in this dataset. The underlying benchmark content is subject to the original RuFinQA licensing terms.
|
|
|
| ---
|
|
|
| ## 📚 Citation
|
|
|
| If you use this evaluation dataset, please cite the original RuFinQA paper:
|
|
|
| ```bibtex
|
| @misc{rufinqa2025,
|
| author = {Arabov, Mullosharaf K.},
|
| title = {RuFinQA: A Massive Multi-Task Reasoning Benchmark for Russian Financial Report Understanding},
|
| year = {2025},
|
| publisher = {Hugging Face},
|
| url = {https://huggingface.co/datasets/arabovs-ai-lab/RuFinQA}
|
| }
|
| ```
|
|
|
| ---
|
|
|
| ## 👤 Author
|
|
|
| **Mullosharaf K. Arabov**
|
| ORCID: 0000-0003-2525-1183
|
| PhD in Physics and Mathematics, Associate Professor
|
| Department of Data Analysis and Programming Technologies
|
| Kazan (Volga Region) Federal University
|
| 📧 marabov@kpfu.ru
|
|
|
| ---
|
|
|
| ## 🔗 Links
|
|
|
| - 📊 Dataset: https://huggingface.co/datasets/arabovs-ai-lab/RusFinQABenchmark
|
| - 📊 Main RuFinQA Dataset: https://huggingface.co/datasets/arabovs-ai-lab/RuFinQA
|
| - 💻 Generator code: [GitHub]
|
| - 📄 Paper: https://arxiv.org/abs/2607.01388
|
|
|
| ---
|
|
|
| *Generated automatically from RuFinQA evaluation pipeline.* |
