Add GMRID v3 dataset (train + test splits) with categories and dataset card

README.md

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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}
+}
+```

categories.json

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+{
+    "Weather": [
+        "Flooding",
+        "Severe Winds",
+        "Weather Advisory",
+        "Tropical Cyclone",
+        "Storm",
+        "Ice Storm",
+        "Earthquake",
+        "Tornado",
+        "Typhoon",
+        "Landslide",
+        "Water",
+        "Hurricane",
+        "Wildfire",
+        "Blizzard",
+        "Hail"
+    ],
+    "Worker Strike": [
+        "Mine Workers Strike",
+        "Production Halt",
+        "Protest",
+        "Riot",
+        "Port Strike",
+        "General Strike",
+        "Civil Service Strike",
+        "Civil Unrest Advisory",
+        "Cargo Transportation Strike",
+        "Energy Sector Strike"
+    ],
+    "Administrative Issue": [
+        "Port Congestion",
+        "Police Operations",
+        "Roadway Closure",
+        "Disruption",
+        "Cargo",
+        "Industrial Action",
+        "Port Disruption",
+        "Cargo Disruption",
+        "Power Outage",
+        "Port Closure",
+        "Maritime Advisory",
+        "Train Delays",
+        "Ground Transportation Advisory",
+        "Public Transportation Disruption",
+        "Trade Regulation",
+        "Customs Regulation",
+        "Regulatory Advisory",
+        "Industry Directives",
+        "Security Advisory",
+        "Public Holidays",
+        "Customs Delay",
+        "Public Health Advisory",
+        "Detention",
+        "Aviation Advisory",
+        "Waterway Closure",
+        "Plant Closure",
+        "Border Closure",
+        "Delay",
+        "Industrial zone shutdown",
+        "Trade Restrictions",
+        "Closure",
+        "Truck Driving Ban",
+        "Insolvency",
+        "Environmental Regulations",
+        "Postal Disruption",
+        "Travel Warning"
+    ],
+    "Human Error": [
+        "Workplace Accident",
+        "Individuals in Focus",
+        "Military Operations",
+        "Flight Delays",
+        "Cancellations",
+        "Political Info",
+        "Political Event"
+    ],
+    "Cyber Attack": [
+        "Network Disruption",
+        "Ransomware",
+        "Data breach",
+        "Phishing"
+    ],
+    "Terrorism": [
+        "Bombing",
+        "Warehouse Theft",
+        "Public Safety",
+        "Security",
+        "Organized Crime",
+        "Piracy",
+        "Kidnap",
+        "Shooting",
+        "Robbery",
+        "Cargo theft",
+        "Bomb Detonation",
+        "Terror Attack",
+        "Outbreak Of War",
+        "Militant Action"
+    ],
+    "Accident": [
+        "Hazmat Response",
+        "Maritime Accident",
+        "Vehicle Accident",
+        "Death",
+        "Injury",
+        "Non-industrial Fire",
+        "Chemical Spill",
+        "Industrial Fire",
+        "Fuel Disruption",
+        "Airline Incident",
+        "Crash",
+        "Explosion",
+        "Train Accident",
+        "Derailment",
+        "Sewage Disruption",
+        "Barge Accident",
+        "Bridge Collapse",
+        "Structure Collapse",
+        "Airport Accident",
+        "Force Majeure",
+        "Telecom Outage"
+    ],
+    "Others": [
+        "Miscellaneous Events",
+        "Miscellaneous Strikes",
+        "Outbreak of disease"
+    ]
+}