Datasets:
Tasks:
Text Classification
Formats:
csv
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1K - 10K
License:
File size: 4,296 Bytes
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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}
}
```
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