FinRAGBench-V

https://github.com/zhaosuifeng/finragbench-v

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FinRAGBench-V is a comprehensive benchmark for visual retrieval-augmented generation in the...

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### Install FinRAGBench-V Environment

Source: https://github.com/zhaosuifeng/finragbench-v/blob/main/README.md

Commands to clone the repository and set up the required Python environment.

```bash
git clone https://github.com/zhaosuifeng/FinRAGBench-V.git
conda create --name finragbench python=3.11 -y
conda activate finragbench
pip install -r requirements.txt
pip install -e .
```

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### Run Retrieval Evaluation

Source: https://github.com/zhaosuifeng/finragbench-v/blob/main/README.md

Execute the multimodal retriever using specified configuration files.

```bash
cd ./FinRAGBench-V/retrieval
python eval_mm_retriever.py
```

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### Run Generation

Source: https://github.com/zhaosuifeng/finragbench-v/blob/main/README.md

Execute the generation script for the RGenCite baseline.

```bash
cd ./FinRAGBench-V/generation
python generate.py
```

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### Run Evaluation Scripts

Source: https://github.com/zhaosuifeng/finragbench-v/blob/main/README.md

Evaluate generation quality and citation accuracy.

```bash
cd ./FinRAGBench-V/eval
python eval_generation.py
python eval_citation.py
```

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### Process English Corpus

Source: https://github.com/zhaosuifeng/finragbench-v/blob/main/README.md

Steps to merge split archive parts and convert data into parquet format.

```bash
# Create folder
mkdir -p ./data/corpus/en
cd ./data/corpus/en

# Merge all parts into a single tar.gz
cat part_*.tar.gz > corpus_en.tar.gz

# Extract
tar -xzvf corpus_en.tar.gz

# Back to prepare_data
cd ../../../prepare_data

# Convert corpus and queries into parquet format
python generate_parquet.py
```

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### BibTeX Citation

Source: https://github.com/zhaosuifeng/finragbench-v/blob/main/README.md

Citation format for the FinRAGBench-V paper.

```bibtex
@article{zhao2025finragbench,
  title={FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain},
  author={Zhao, Suifeng and Jin, Zhuoran and Li, Sujian and Gao, Jun},
  journal={arXiv preprint arXiv:2505.17471},
  year={2025}
}
```

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