Datasets:

tjw
/

hmi-transcripts

# NOTICE — MetaFLOS HMI Transcripts

Dataset: **MetaFLOS HMI Transcripts** (Manufacturing Human-Machine Interaction Dialogue Dataset)
Author / rights holder: **Tzer-jen Wei**
License: **CC BY 4.0** (see `LICENSE`)

This file is informational. It records provenance, upstream terms, and
safety advisories. **Nothing here adds conditions to the CC BY 4.0 grant** —
CC BY 4.0 §2(a)(5)(B) forbids downstream restrictions, and none are intended.

---

## 1. Provenance

All 30 scenarios / 325 turns are **synthetic**, produced by `gemma3:27b`
via `scripts/gen_hmi_dataset.py` (seed scenario → LLM transcript → role and
non-empty validation). No real conversation, factory event, or personal data
is contained.

## 2. Gemma (Google) — verified position

Verified against the Gemma Terms of Use (last modified 2026-04-01) and the
Gemma Prohibited Use Policy (last modified 2024-02-21):

- **Gemma Terms §1.1(e)**: "For clarity, Outputs are not deemed Model
  Derivatives."
- **Gemma Terms §3.3**: "Google claims no rights in Outputs you generate
  using Gemma."

This dataset consists solely of Outputs. The redistribution conditions of
**Gemma Terms §3.1** — enforceable pass-through of use restrictions, delivery
of the Gemma Agreement, NOTICE file — attach to Gemma and to Model
Derivatives, and therefore **do not attach to this dataset**. The author
generated it in compliance with the Gemma Prohibited Use Policy, as §3.2
requires of the party operating the model.

**If you fine-tune on this dataset**: Gemma Terms §1.1(e) defines "Model
Derivatives" to include a model created by "methods based on the generation
of synthetic data Outputs by Gemma for training that model" so as to cause it
"to perform similarly to Gemma". A model you distill or fine-tune with that
effect may itself be a Gemma Model Derivative and become subject to the full
Gemma Terms. Training a *different* base model for *manufacturing-domain*
capability is a different case. Assess your own situation before distributing.

- https://ai.google.dev/gemma/terms
- https://ai.google.dev/gemma/prohibited_use_policy

## 3. Scenario inspiration sources

20 of the 30 scenarios **name** a public academic dataset as the origin of the
defect **category** that inspired the scenario (e.g. "surface scratch on a
metal nut, MVTec AD `metal_nut` class").

**No text, image, annotation, or other expressive content from any of these
sources is reproduced, adapted, or redistributed here** — only factual
category and domain references are used. Accordingly, no ShareAlike or other
copyleft obligation of those sources attaches to this dataset.

| Source | Holder | Scenarios |
|---|---|---|
| MVTec AD | MVTec Software GmbH | 6 |
| IRWoZ 1.0 / 2.0 | Aalborg University | 4 |
| NEU-DET | Northeastern University | 3 |
| DeepPCB | Tang et al. | 3 |
| KIDE4I | Tekniker Foundation | 2 |
| ICNLSP 2023 corpus | respective authors | 1 |
| KolektorSDD | Kolektor Group / UL FRI | 1 |
| Self-authored | Tzer-jen Wei | 10 |

If you obtain and use any of these upstream datasets **directly**, you must do
so under that dataset's own license, which this notice neither grants nor
alters.

## 4. Safety advisory — not engineering guidance

The parameter values, setpoints, tolerances, and corrective actions in the
dialogues are **synthetic illustrations**, not validated engineering guidance
for any real machine. They have not been checked against any real process.

Machine identifiers (`NX-220`, `EL-180`, …) and all numeric parameters are
fictional scenario settings and correspond to no real production line,
equipment configuration, or trade secret.

**The author strongly advises** against using this dataset — or any model
trained on it — to drive, recommend, or automate real machine control,
process parameters, maintenance decisions, or safety interlocks without
independent qualification by a responsible engineer.

## 5. Transparency advisory

When quoting or redistributing these dialogues, please identify them as
LLM-generated synthetic text rather than records of real human-machine
interaction. See also EU AI Act transparency obligations for synthetic
content, where applicable to you.

## 6. Attribution

CC BY 4.0 §3(a)(1) requires attribution. Suggested form:

> MetaFLOS HMI Transcripts, by Tzer-jen Wei, licensed under CC BY 4.0.
> https://huggingface.co/datasets/tjw/hmi-transcripts

```bibtex
@misc{metaflos_hmi_transcripts_2026,
  title  = {MetaFLOS HMI Transcripts: A Manufacturing Human-Machine
            Interaction Dialogue Dataset},
  author = {Wei, Tzer-jen},
  year   = {2026},
  note   = {30 scenarios / 325 turns, synthetically generated with gemma3:27b},
  url    = {https://huggingface.co/datasets/tjw/hmi-transcripts}
}
```

---

# 中文說明

本檔案為**告知性質**，記錄資料來源、上游條款與使用建議。
**其內容不對 CC BY 4.0 之授權附加任何條件** —— CC BY 4.0 §2(a)(5)(B) 禁止
下游附加限制，本文件亦無此意圖。

**1. 資料來源**：30 情境／325 輪全為 `gemma3:27b` 生成之合成內容，無真實
對話、真實工廠事件或個人資料。

**2. Gemma**：依 Gemma 使用條款 §1.1(e)「Outputs 不視為 Model Derivatives」
及 §3.3「Google 不主張 Outputs 之權利」，本資料集全部為 Outputs，故 §3.1 之
散布條件不及於本資料集。著作人已依 §3.2 於生成時遵守 Gemma 禁止用途政策。
**惟若您以本資料集微調出表現近似 Gemma 之模型**，該模型本身可能構成 Model
Derivative 而受完整 Gemma 條款拘束；若是在其他 base model 上微調製造領域能力，
則屬另一情形。請於散布前自行評估。

**3. 情境靈感來源**：30 個情境中有 20 個標註了啟發該情境之瑕疵**類別**所出自
的公開學術資料集。**未重製或改作任何來源之文字、影像或標註**，僅使用事實性
類別指涉，故不生 ShareAlike 義務。來源對照見上方英文表格。若您**直接取用**
該等來源，仍須依其各自授權。

**4. 安全提醒**：對話中的參數值為**合成示例**，非經驗證之工程指引；機台代號與
數值均為虛構情境設定。著作人**強烈建議**：未經負責工程師獨立驗證，不要將本
資料集或以其訓練之模型用於驅動真實機台控制、製程參數、維修決策或安全連鎖。

**5. 透明性提醒**：引用或再散布時，請敘明其為 LLM 生成之合成文本。

**6. 姓名標示**：CC BY 4.0 §3(a)(1) 要求標示。建議格式見上方英文段落。

	# NOTICE — MetaFLOS HMI Transcripts

	Dataset: MetaFLOS HMI Transcripts (Manufacturing Human-Machine Interaction Dialogue Dataset)
	Author / rights holder: Tzer-jen Wei
	License: CC BY 4.0 (see `LICENSE`)

	This file is informational. It records provenance, upstream terms, and
	safety advisories. Nothing here adds conditions to the CC BY 4.0 grant —
	CC BY 4.0 §2(a)(5)(B) forbids downstream restrictions, and none are intended.

	---

	## 1. Provenance

	All 30 scenarios / 325 turns are synthetic, produced by `gemma3:27b`
	via `scripts/gen_hmi_dataset.py` (seed scenario → LLM transcript → role and
	non-empty validation). No real conversation, factory event, or personal data
	is contained.

	## 2. Gemma (Google) — verified position

	Verified against the Gemma Terms of Use (last modified 2026-04-01) and the
	Gemma Prohibited Use Policy (last modified 2024-02-21):

	- Gemma Terms §1.1(e): "For clarity, Outputs are not deemed Model
	Derivatives."
	- Gemma Terms §3.3: "Google claims no rights in Outputs you generate
	using Gemma."

	This dataset consists solely of Outputs. The redistribution conditions of
	Gemma Terms §3.1 — enforceable pass-through of use restrictions, delivery
	of the Gemma Agreement, NOTICE file — attach to Gemma and to Model
	Derivatives, and therefore do not attach to this dataset. The author
	generated it in compliance with the Gemma Prohibited Use Policy, as §3.2
	requires of the party operating the model.

	If you fine-tune on this dataset: Gemma Terms §1.1(e) defines "Model
	Derivatives" to include a model created by "methods based on the generation
	of synthetic data Outputs by Gemma for training that model" so as to cause it
	"to perform similarly to Gemma". A model you distill or fine-tune with that
	effect may itself be a Gemma Model Derivative and become subject to the full
	Gemma Terms. Training a different base model for manufacturing-domain
	capability is a different case. Assess your own situation before distributing.

	- https://ai.google.dev/gemma/terms
	- https://ai.google.dev/gemma/prohibited_use_policy

	## 3. Scenario inspiration sources

	20 of the 30 scenarios name a public academic dataset as the origin of the
	defect category that inspired the scenario (e.g. "surface scratch on a
	metal nut, MVTec AD `metal_nut` class").

	**No text, image, annotation, or other expressive content from any of these
	sources is reproduced, adapted, or redistributed here** — only factual
	category and domain references are used. Accordingly, no ShareAlike or other
	copyleft obligation of those sources attaches to this dataset.

	\| Source \| Holder \| Scenarios \|
	\|---\|---\|---\|
	\| MVTec AD \| MVTec Software GmbH \| 6 \|
	\| IRWoZ 1.0 / 2.0 \| Aalborg University \| 4 \|
	\| NEU-DET \| Northeastern University \| 3 \|
	\| DeepPCB \| Tang et al. \| 3 \|
	\| KIDE4I \| Tekniker Foundation \| 2 \|
	\| ICNLSP 2023 corpus \| respective authors \| 1 \|
	\| KolektorSDD \| Kolektor Group / UL FRI \| 1 \|
	\| Self-authored \| Tzer-jen Wei \| 10 \|

	If you obtain and use any of these upstream datasets directly, you must do
	so under that dataset's own license, which this notice neither grants nor
	alters.

	## 4. Safety advisory — not engineering guidance

	The parameter values, setpoints, tolerances, and corrective actions in the
	dialogues are synthetic illustrations, not validated engineering guidance
	for any real machine. They have not been checked against any real process.

	Machine identifiers (`NX-220`, `EL-180`, …) and all numeric parameters are
	fictional scenario settings and correspond to no real production line,
	equipment configuration, or trade secret.

	The author strongly advises against using this dataset — or any model
	trained on it — to drive, recommend, or automate real machine control,
	process parameters, maintenance decisions, or safety interlocks without
	independent qualification by a responsible engineer.

	## 5. Transparency advisory

	When quoting or redistributing these dialogues, please identify them as
	LLM-generated synthetic text rather than records of real human-machine
	interaction. See also EU AI Act transparency obligations for synthetic
	content, where applicable to you.

	## 6. Attribution

	CC BY 4.0 §3(a)(1) requires attribution. Suggested form:

	> MetaFLOS HMI Transcripts, by Tzer-jen Wei, licensed under CC BY 4.0.
	> https://huggingface.co/datasets/tjw/hmi-transcripts

	```bibtex
	@misc{metaflos_hmi_transcripts_2026,
	title = {MetaFLOS HMI Transcripts: A Manufacturing Human-Machine
	Interaction Dialogue Dataset},
	author = {Wei, Tzer-jen},
	year = {2026},
	note = {30 scenarios / 325 turns, synthetically generated with gemma3:27b},
	url = {https://huggingface.co/datasets/tjw/hmi-transcripts}
	}
	```

	---

	# 中文說明

	本檔案為告知性質，記錄資料來源、上游條款與使用建議。
	其內容不對 CC BY 4.0 之授權附加任何條件 —— CC BY 4.0 §2(a)(5)(B) 禁止
	下游附加限制，本文件亦無此意圖。

	1. 資料來源：30 情境／325 輪全為 `gemma3:27b` 生成之合成內容，無真實
	對話、真實工廠事件或個人資料。

	2. Gemma：依 Gemma 使用條款 §1.1(e)「Outputs 不視為 Model Derivatives」
	及 §3.3「Google 不主張 Outputs 之權利」，本資料集全部為 Outputs，故 §3.1 之
	散布條件不及於本資料集。著作人已依 §3.2 於生成時遵守 Gemma 禁止用途政策。
	惟若您以本資料集微調出表現近似 Gemma 之模型，該模型本身可能構成 Model
	Derivative 而受完整 Gemma 條款拘束；若是在其他 base model 上微調製造領域能力，
	則屬另一情形。請於散布前自行評估。

	3. 情境靈感來源：30 個情境中有 20 個標註了啟發該情境之瑕疵類別所出自
	的公開學術資料集。未重製或改作任何來源之文字、影像或標註，僅使用事實性
	類別指涉，故不生 ShareAlike 義務。來源對照見上方英文表格。若您直接取用
	該等來源，仍須依其各自授權。

	4. 安全提醒：對話中的參數值為合成示例，非經驗證之工程指引；機台代號與
	數值均為虛構情境設定。著作人強烈建議：未經負責工程師獨立驗證，不要將本
	資料集或以其訓練之模型用於驅動真實機台控制、製程參數、維修決策或安全連鎖。

	5. 透明性提醒：引用或再散布時，請敘明其為 LLM 生成之合成文本。

	6. 姓名標示：CC BY 4.0 §3(a)(1) 要求標示。建議格式見上方英文段落。