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---
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. |