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windchimeran/GMRID

GMRID v3: Global Maritime and Supply-Chain Risk Intelligence Dataset All authorship and attribution belong to the original creators. This is a mirror of the dataset from inflaton/llms-at-edge hosted on Hugging Face for accessibility. The original repository does not specify a license; please contact the authors for licensing terms before commercial use. Overview GMRID v3 is a supply-chain disruption news classification dataset. Each row is a real-world incident… See the full description on the dataset page: https://huggingface.co/datasets/windchimeran/GMRID.

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Dataset Card

GMRID v3: Global Maritime and Supply-Chain Risk Intelligence Dataset

All authorship and attribution belong to the original creators. This is a mirror of the dataset from inflaton/llms-at-edge hosted on Hugging Face for accessibility. The original repository does not specify a license; please contact the authors for licensing terms before commercial use.

Overview

GMRID v3 is a supply-chain disruption news classification dataset. Each row is a real-world incident report (headline + details) labeled with one of 8 disruption categories. The dataset was introduced in:

LLMs at the Edge: Performance and Efficiency Evaluation with Ollama on Diverse Hardware IJCNN 2025 (Paper ID: 1443) GitHub: inflaton/llms-at-edge

Task

Single-label classification into 8 categories:

Category	Train	Test
Weather	—	366
Administrative Issue	—	333
Accident	—	191
Worker Strike	—	178
Terrorism	—	60
Human Error	—	9
Others	—	5
Cyber Attack	—	4

The label column is Summarized_label. A finer-grained Category column provides subcategories (e.g., "Flooding" under Weather, "Port Congestion" under Administrative Issue). The mapping is defined in categories.json.

Splits

Split	Rows
Train	4,594
Test	1,147

Columns

Column	Description
`id`	Unique row identifier
`Headline`	Short incident headline
`Details`	Full incident description
`Severity`	Severity level (Critical, Moderate, etc.)
`Region`	Geographic region
`Datetime`	Incident timestamp
`lat`, `lon`	Coordinates (when available)
`maritime_label`	Whether the incident is maritime-related
`found_ports`	Ports mentioned in the text
`contains_port_info`	Boolean: port info present
`if_labeled`	Whether the row was manually labeled
`Headline_Details`	Concatenated headline + details
`Year`, `Month`, `Week`	Temporal features
`Details_cleaned`	Preprocessed/cleaned details text
`Category`	Fine-grained incident category
`Summarized_label`	Coarse 8-class label (primary target)
`gpt-4o_label`	GPT-4o predicted label (for reference)

Evaluation Metric

Per the original paper: weighted F1 over the 8-class Summarized_label. Macro-F1, exact-match, and per-class P/R/F1 are also commonly reported.

Citation

If you use this dataset, please cite the original work:

bibtex

@inproceedings{llms_at_edge_2025,
  title={LLMs at the Edge: Performance and Efficiency Evaluation with Ollama on Diverse Hardware},
  booktitle={International Joint Conference on Neural Networks (IJCNN)},
  year={2025},
  note={Paper ID: 1443},
  url={https://github.com/inflaton/llms-at-edge}
}