Datasets:
Tasks:
Text Classification
Formats:
csv
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1K - 10K
License:
File size: 15,985 Bytes
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],
"conformsTo": "http://mlcommons.org/croissant/1.1",
"name": "GMRID",
"description": "\n\t\n\t\t\n\t\tGMRID v3: Global Maritime and Supply-Chain Risk Intelligence Dataset\n\t\n\nAll 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.\n\n\t\n\t\t\n\t\tOverview\n\t\n\nGMRID v3 is a supply-chain disruption news classification dataset. Each row is a real-world incident report… See the full description on the dataset page: https://huggingface.co/datasets/windchimeran/GMRID.",
"alternateName": [
"windchimeran/GMRID",
"GMRID v3: Global Maritime and Supply-Chain Risk Intelligence Dataset"
],
"creator": {
"@type": "Person",
"name": "Ranran Haoran Zhang",
"url": "https://huggingface.co/windchimeran"
},
"keywords": [
"text-classification",
"multi-class-classification",
"English",
"other",
"1K - 10K",
"csv",
"Tabular",
"Text",
"Datasets",
"pandas",
"Polars",
"Croissant",
"🇺🇸 Region: US",
"supply-chain",
"logistics",
"news-classification",
"disruption-detection"
],
"license": "https://choosealicense.com/licenses/other/",
"url": "https://huggingface.co/datasets/windchimeran/GMRID",
"dct:conformsTo": [
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],
"rai:dataLimitations": "English-only incident reports covering global supply-chain disruptions. The class distribution is heavily imbalanced: Weather (366 test samples) and Administrative Issue (333) dominate, while Cyber Attack (4), Others (5), and Human Error (9) are severely underrepresented. Models evaluated on this dataset will have unreliable per-class metrics for the tail categories. The temporal coverage spans primarily 2019 and surrounding years; disruption patterns shift over time (e.g., pandemic-era disruptions are underrepresented). Geographic coverage may skew toward regions with English-language reporting infrastructure.",
"rai:dataBiases": "Selection bias: incidents are drawn from English-language news and maritime intelligence feeds, underrepresenting disruptions in regions with limited English media coverage. Label bias: the 8 coarse categories collapse diverse incident types (e.g., 'Human Error' groups workplace accidents, political events, and flight delays), which can introduce label noise. The gpt-4o_label column contains AI-generated labels that may reflect GPT-4o's own biases and should not be treated as ground truth. Annotator demographics and inter-annotator agreement are not documented in the source repository.",
"rai:personalSensitiveInformation": "Contains geographic information (regions, port names, latitude/longitude coordinates). Incident reports may reference named organizations, government bodies, and public institutions. No individual-level personally identifiable information (names, contact details, health data) was identified in the dataset. The data concerns public events reported in news sources.",
"rai:dataUseCases": "Intended to measure the ability of language models to classify supply-chain disruption events from news text into standardized risk categories. Primary use cases: (1) benchmarking LLM classification accuracy across inference frameworks (construct: framework correctness, not model capability), (2) evaluating few-shot classification performance on domain-specific taxonomy, (3) supply-chain risk monitoring research. Not intended for real-time operational decision-making without human review.",
"rai:dataSocialImpact": "Positive: enables systematic comparison of LLM inference frameworks on a real-world classification task, supporting transparency in framework selection for edge deployment. Supports supply-chain resilience research by providing a standardized evaluation benchmark. Negative: detailed disruption event data could theoretically be used to identify supply-chain vulnerabilities for exploitation, though this information is already publicly available through news sources. Automated classification systems built on this data should not replace human judgment in critical logistics decisions.",
"rai:hasSyntheticData": "The gpt-4o_label column contains synthetic labels generated by GPT-4o. All other columns (headlines, details, human-assigned labels, metadata) are derived from real-world incident reports. The synthetic labels are provided for comparison purposes and are not the primary classification target.",
"prov:wasDerivedFrom": "GMRID v3 dataset from https://github.com/inflaton/llms-at-edge (IJCNN 2025, Paper ID 1443). Original data sourced from global supply-chain disruption incident reports. This Hugging Face repository is an unmodified mirror of the train and test CSV files from the source repository.",
"prov:wasGeneratedBy": "Collection: real-world supply-chain disruption incident reports aggregated from news and maritime intelligence sources. Annotation: incidents labeled with fine-grained Category labels, then mapped to 8 coarse Summarized_label classes via the category taxonomy defined in categories.json. The mapping groups 100+ subcategories into 8 parent classes. Preprocessing: headline and details text concatenated into Headline_Details; Details_cleaned contains lowercased, stopword-removed text. Additional GPT-4o labels generated as a reference baseline. No modifications were applied to the source data for this mirror.",
"datePublished": "2026-05-07",
"version": "1.0.0",
"citeAs": "@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}}"
} |