---
pretty_name: Ishigaki-Bench
license: cc-by-4.0
configs:
- config_name: IFC2X3
  default: true
  data_files: metadata/IFC2X3.json
- config_name: IFC4
  data_files: metadata/IFC4.json
- config_name: IFC4X3_ADD2
  data_files: metadata/IFC4X3_ADD2.json
---

> [!NOTE]
> This result was produced as part of the "[GENIAC (Generative AI Accelerator Challenge) Project](https://www.meti.go.jp/policy/mono_info_service/geniac/index.html)" which is promoted by the Ministry of Economy, Trade and Industry and the New Energy and Industrial Technology Development Organization (NEDO), with the aim of strengthening Japan’s capabilities in generative AI development.

This dataset is an evaluation dataset for the CSV-to-IDS task used to evaluate the Ishigaki-IDS model. It evaluates whether an LLM can generate appropriate IDS from CSV files used for IFC data checking.

[Coming Soon] A high-difficulty benchmark created by experts and grounded more closely in real use cases is also planned for release at a later date.

- The technical blog covering the model overview, training, and evaluation is available [here](https://zenn.dev/onestruction/articles/487404c8762cc6)
- The released model is available [here](https://huggingface.co/ONESTRUCTION/Ishigaki-IDS-8B)

**Features**

- Created in collaboration with BIM/IDS experts
- Enables systematic evaluation across three axes: understanding of IDS, IFC, and Japanese/English
- Covers four construction domains: Common, Architectural, Structural, and Equipment
- Supports three versions: IFC2X3, IFC4, and IFC4X3_ADD2

## Data Specification

This dataset was created by expanding 100 base items across three IFC versions: IFC2X3, IFC4, and IFC4X3_ADD2. Each version has the same structure and is designed so that the distribution of categories and conditions is as even as possible. As a result, the dataset contains 100 samples per IFC version, for a total of 300 samples overall.

```json
{
  "id": "001",
  "entity": "IFCPROJECT",
  "category": "Common",
  "language": "EN",
  "ids_header": true,
  "ifc_content": true,
  "input_csv": "data/input_csv/IFC2X3/IFC2X3_001_IFCPROJECT.csv",
  "output_ids": "data/output_ids/IFC2X3/IFC2X3_001_IFCPROJECT.ids"
}
```

## Description of Each Field

- `id`

  A unique ID for the data.

- `entity`

  The IFC Entity targeted by the data.

- `category`

  A category indicating which construction domain the content belongs to. There are four types: `Common`, `Architectural`, `Structural`, and `Equipment`.

- `language`

  The language used. English is represented as `EN`, and Japanese as `JP`.

- `ids_header`

  Indicates whether IDS vocabulary is used in the input header. If `true`, IDS vocabulary is used; if `false`, non-IDS vocabulary is used.

- `ifc_content`

  Indicates whether IFC vocabulary is used in the data content. If `true`, IFC vocabulary is used; if `false`, general expressions are used.

- `input_csv`

  The file path where the input data for the model is stored.

- `output_ids`

  The file path where the ground-truth IDS data is stored.

## Breakdown by Version

The number of samples for each category and condition in each IFC version is as follows.

| Category | ids_header | ifc_content | language | Count per version |
| --- | --- | --- | --- | --- |
| Common | true | true | EN | 4 |
| Common | true | true | JP | 4 |
| Common | true | false | EN | 3 |
| Common | true | false | JP | 3 |
| Common | false | true | EN | 3 |
| Common | false | true | JP | 3 |
| Common | false | false | EN | 3 |
| Common | false | false | JP | 3 |
| Architectural | true | true | EN | 3 |
| Architectural | true | true | JP | 3 |
| Architectural | true | false | EN | 4 |
| Architectural | true | false | JP | 3 |
| Architectural | false | true | EN | 4 |
| Architectural | false | true | JP | 3 |
| Architectural | false | false | EN | 3 |
| Architectural | false | false | JP | 3 |
| Structural | true | true | EN | 3 |
| Structural | true | true | JP | 3 |
| Structural | true | false | EN | 3 |
| Structural | true | false | JP | 3 |
| Structural | false | true | EN | 3 |
| Structural | false | true | JP | 4 |
| Structural | false | false | EN | 4 |
| Structural | false | false | JP | 3 |
| Equipment | true | true | EN | 3 |
| Equipment | true | true | JP | 3 |
| Equipment | true | false | EN | 3 |
| Equipment | true | false | JP | 3 |
| Equipment | false | true | EN | 3 |
| Equipment | false | true | JP | 2 |
| Equipment | false | false | EN | 2 |
| Equipment | false | false | JP | 3 |

**Benchmark Score**

The generated IDS is evaluated using the "[IDS-Audit-Tool(ver 1.0.96)](https://github.com/buildingSMART/IDS-Audit-tool/tree/e2c96c2358973a17e3f3fae6b8dba86b8385f857)" provided by buildingSMART. Because IDS is a specialized standard in the construction and BIM domain, and also a relatively new standard released at 2024, it is an area that general foundation models have not yet been able to handle sufficiently. Even under such conditions, this model demonstrates strong performance. Please refer to the detailed evaluation results for more information.

![スコア画像_EN](https://cdn-uploads.huggingface.co/production/uploads/64f4340087c1d7666428c2f2/zjKWeLKpb0Q3CfytuWR_b.png)

**Evaluation Method**

The source code will be shared on GitHub at a later date.

**Developers**

- [Koyo Hidaka](https://x.com/HidakaKoyo)

- [Ryo Kanazawa](https://x.com/k_another_wa)

*In alphabetical order

**License**

Creative Commons Attribution 4.0 International (CC BY 4.0)

**Citation**

```bibtex
@misc{idsbench2026,
  title={ONESTRUCTION/IDS-Bench},
  author={ONESTRUCTION Inc.},
  year={2026},
  url={https://huggingface.co/ONESTRUCTION/IDS-Bench}
}
```