---
license: cc-by-nc-4.0
task_categories:
- table-question-answering
tags:
- finance
- xbrl
- information-extraction
- semantic-alignment
---

# FinTagging: An LLM-ready Benchmark for Extracting and Structuring Financial Information

FinTagging is the first full-scope, table-aware XBRL benchmark designed to evaluate the structured information extraction and semantic alignment capabilities of large language models (LLMs) in the context of XBRL-based financial reporting.  It decomposes the XBRL tagging problem into two subtasks:

- **FinNI:** Financial entity extraction.
- **FinCL:** Taxonomy-driven concept alignment.

FinTagging requires models to jointly extract facts from both unstructured text and structured tables and align them with the full 10k+ US-GAAP taxonomy.

[Paper](https://huggingface.co/papers/2505.20650) | [Evaluation Framework](https://github.com/The-FinAI/FinBen)

This repository contains the original benchmark dataset without preprocessing.  Annotated data (`benchmark_ground_truth_pipeline.json`) is provided in the "annotation" folder.  For preprocessed datasets suitable for specific model architectures, please see the linked datasets in the Github README.

**Datasets:**

* **FinNI-eval:** Evaluation set for FinNI subtask.
* **FinCL-eval:** Evaluation set for FinCL subtask.
* **FinTagging_BIO:** BIO-format dataset for token-level tagging.