IntelliProcure/SwissSPARK

⚠️ Caution: The dataset is subject to continuous changes. We are currently actively developing it.

Dataset Card: Dataset for a Swiss Sustainable Procurement Analysis & Reporting Kit

Dataset Description

This dataset is designed to train and evaluate models for detecting sustainability criteria in Swiss public procurement documents (Call for Tenders, CFT). The dataset classifies text segments based on whether they contain specific sustainability requirements according to official Swiss guidelines.

These annotations are based on a specific snapshot of the sustainability procurement criteria catalogs available at IntelliProcure/sustainability_criteria. However, to ensure consistency and reproducibility with the SwissSPARK annotations, users are advised to use this specific snapshot of the criteria catalogs: IntelliProcure/SwissSPARK_Catalogs. Newer versions of the catalogs may contain changes that differ from the guidelines used during the annotation of this dataset.

Dataset Summary

• Language: German (de)
• Task: Multi-label classification (sustainability criteria detection, procurement criterion classification, sector-specific classification)
• Domain: Public procurement, sustainability criteria
• Size: Multiple splits with stratified sampling

Supported Tasks

• Sustainability Criteria Detection: Identify whether text segments contain sustainability requirements (via area_of_action_id mapping)
• Sector-Specific Classification: Classify texts according to sector-specific guidelines (catalog)
• Procurement Criterion Classification: Determine the legal context of criteria (e.g., technical_specification, award_criterion, selection_criterion, terms_and_conditions, evidence_requirement)

Dataset Structure

Data Fields

• project_id (int): Unique identifier for the procurement project
• simap_version (string): Version of SIMAP platform (e.g., "simap")
• filename (string): Original document filename
• source (string): Data source ("annotations" or "feedback")
• text (string): The original text segment
• text_cleaned (string): Normalized and cleaned text
• catalog (string): Sector-specific guideline (e.g., "Road_Transport", "Print_Services", "ICT", "Food", etc.)
• area_of_action_id (string): Specific criterion ID within the catalog (e.g., "HF-001", or "not present" for negative examples)
• procurement_criterion (string): Legal context type (e.g., "technical_specification", "award_criterion", "selection_criterion", "terms_and_conditions", "evidence_requirement", or "not present")
• self_declaration (string): Indicates if it's a self-declaration ("True"/"False" as string, or "not present")
• evidence (string): Evidence requirements (or "not present")
• award_criterion_weight (string): Weight information for award criteria (or "not present")

Data Splits

The dataset provides a stratified random split:

• 50% Training
• 25% Evaluation
• 25% Test

Stratification ensures consistent distribution across:

• area_of_action_id (criterion ID)
• procurement_criterion (criterion type)
• pos_neg (positive/negative label - internal mapping)
• award_criterion_weight (award criteria weights)

Sector-Specific Guidelines (Catalogs)

The dataset includes sustainability criteria from multiple sectors:

• Road Transport
• Food
• ICT
• Furniture
• Construction (Buildings/Civil Engineering)
• Electricity
• Print Services
• Product Independent Criteria
• And others

Dataset Creation

Source Data

The dataset combines two primary sources:

1. Expert Annotations (INCEpTION Platform): CFT documents manually labeled by experts on the UIMA INCEpTION platform with precise mapping to catalog IDs (area_of_action_id) and criterion types (procurement_criterion)

2. Feedback Data: 220 examples validated by experts from an automated NLP pipeline, including weight information for award criteria (award_criterion_weight) and correctness evaluations

Data Processing Pipeline

The generation pipeline includes:

1. Positive Example Extraction: Direct extraction from expert-annotated documents
2. Negative Sampling:
   • Hard Negatives: Sentences with high semantic similarity (cosine similarity) but no sustainability criteria
   • Random Negatives: Random sentences for standard administrative language
3. Contextual Windowing: Creates context windows (1 or 2 surrounding sentences) with label leakage prevention (available in dataset_info.json but excluded from primary splits by default)
4. Text Cleaning: Normalization of line breaks, removal of excess whitespace (available in text_cleaned)
5. Deduplication: Based on project_id, simap_version, text_cleaned, filename, catalog, and area_of_action_id

Annotations

Annotations were created by domain experts familiar with:

• Swiss public procurement law
• Sustainability guidelines for public procurement
• Sector-specific requirements

Annotation categories (procurement_criterion) include:

• award_criterion (Zuschlagskriterien)
• selection_criterion (Eignungskriterien)
• technical_specification (Technische Spezifikationen)
• terms_and_conditions (Vertragsbedingungen)
• evidence_requirement (Nachweise)

Technical Specifications

• NLP Engine: spaCy (model: de_dep_news_trf) for German sentence segmentation
• Embeddings: SentenceTransformers (all-MiniLM-L6-v2) for semantic search and hard negative mining
• Environment: Python 3.10 via condavenv

Considerations

Social Impact

This dataset supports:

• Transparency: Automated detection of sustainability criteria in public procurement
• Efficiency: Reduces manual review time for procurement officers
• Sustainability: Promotes adherence to sustainability guidelines in Swiss public procurement

Limitations

• Language: Dataset is German-only, specific to Swiss German administrative language
• Domain Specificity: Trained on Swiss public procurement documents; may not generalize to other jurisdictions
• Context Dependency: Some criteria require understanding of broader document context
• Class Imbalance: Distribution between positive and negative examples should be monitored during training (mapped via area_of_action_id != "not present")

Citation

If you use this dataset, please cite:

@dataset{swiss_sustainable_procurement_2026,
  author       = { Veton Matoshi and Rahel Meili and Judith Binder and Sarah Hostettler and Tilia Ellendorff },
  title        = { SwissSPARK },
  year         = 2026,
  url          = { https://huggingface.co/datasets/IntelliProcure/SwissSPARK },
  doi          = { 10.57967/hf/7945 },
  publisher    = { Hugging Face }
}

License

[Please specify license information based on your organization's requirements]

Contact

For questions or feedback regarding this dataset, please contact [contact information].