---
language:
- en
license: other
license_name: unknown
license_link: https://github.com/inflaton/llms-at-edge
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: "GMRID v3: Global Maritime and Supply-Chain Risk Intelligence Dataset"
size_categories:
- 1K<n<10K
tags:
- supply-chain
- logistics
- news-classification
- disruption-detection
source_datasets: []
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: Headline
    dtype: string
  - name: Details
    dtype: string
  - name: Severity
    dtype: string
  - name: Region
    dtype: string
  - name: Datetime
    dtype: string
  - name: lat
    dtype: string
  - name: lon
    dtype: string
  - name: maritime_label
    dtype: string
  - name: found_ports
    dtype: string
  - name: contains_port_info
    dtype: string
  - name: if_labeled
    dtype: string
  - name: Headline_Details
    dtype: string
  - name: Year
    dtype: int64
  - name: Month
    dtype: int64
  - name: Week
    dtype: int64
  - name: Details_cleaned
    dtype: string
  - name: Category
    dtype: string
  - name: Summarized_label
    dtype: string
  - name: gpt-4o_label
    dtype: string
  splits:
  - name: train
    num_examples: 4594
  - name: test
    num_examples: 1147
configs:
- config_name: default
  data_files:
  - split: train
    path: GMRID_v3-train.csv
  - split: test
    path: GMRID_v3-test.csv
---

# 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](https://github.com/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](https://github.com/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}
}
```