Datasets:

IntelliProcure
/

SwissSPARK

configs:
  - config_name: default
    data_files:
      - split: train
        path: train.json
      - split: eval
        path: eval.json
      - split: test
        path: test.json
    default: true
license: cc-by-4.0
task_categories:
  - text-classification
language:
  - de
tags:
  - legal
pretty_name: Dataset for a Swiss Sustainable Procurement Analysis & Reporting Kit
size_categories:
  - n<1K

⚠️ 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.

Dataset Summary

Language: German (de)
Task: Binary text classification (positive/negative) + multi-label classification (sector-specific guidelines, criterion types)
Domain: Public procurement, sustainability criteria
Size: Multiple splits with stratified sampling across 4 independent iterations

Supported Tasks

Sustainability Criteria Detection: Identify whether text segments contain sustainability requirements
Sector-Specific Classification: Classify texts according to sector-specific guidelines (catalogs)
Criterion Type Classification: Determine the legal context of criteria (e.g., award criteria, selection criteria, technical specifications)

Dataset Structure

Data Fields

project_id (string): Unique identifier for the procurement project
simap_version (string): Version of SIMAP platform
filename (string): Original document filename
project_filename (string): Project-specific filename
catalog (string): Sector-specific guideline (e.g., "Road_Transport", "Print_Services", "ICT", "Food", etc.)
scope_of_action_id (string): Specific criterion ID within the catalog
criterion_type (string): Legal context type (e.g., "cc-award_criterion", "cc-selection_criteria", "cc-terms_and_conditions")
text (string): The original text segment
source (string): Data source ("annotations" or "feedback")
pos_neg (string): Label indicating "positive" (contains sustainability criterion) or "negative" (general procurement text)
score (float/string): Similarity score for negative examples (or "undefined" for positive examples)
status (string): Validation status
most_similar_references_sentences (list/string): Most similar reference sentences for negative sampling
context_window_1 (string): First context window (1 surrounding sentence)
context_window_2 (string): Second context window (2 surrounding sentences)
cc-ac_weight (string): Weight information for award criteria
text_cleaned (string): Normalized and cleaned text
split_1, split_2, split_3, split_4 (string): Four independent stratified splits ("train", "eval", "test")

Data Splits

The dataset provides 4 independent stratified random splits, each with:

50% Training
25% Evaluation
25% Test

Stratification ensures consistent distribution across:

scope_of_action_id (criterion ID)
criterion_type (criterion type)
pos_neg (positive/negative label)
weights (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:

Expert Annotations (INCEpTION Platform): CFT documents manually labeled by experts on the UIMA INCEpTION platform with precise mapping to catalog IDs and criterion types
Feedback Data: 220 examples validated by experts from an automated NLP pipeline, including weight information for award criteria and correctness evaluations

Data Processing Pipeline

The generation pipeline includes:

Positive Example Extraction: Direct extraction from expert-annotated documents
Negative Sampling:
- Hard Negatives: Sentences with high semantic similarity (cosine similarity) but no sustainability criteria
- Random Negatives: Random sentences for standard administrative language
Contextual Windowing: Creates context windows (1 or 2 surrounding sentences) with label leakage prevention
Text Cleaning: Normalization of line breaks, removal of excess whitespace
Deduplication: Based on project_id, filename, and cleaned text

Annotations

Annotations were created by domain experts familiar with:

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

Annotation categories include:

Award criteria (Zuschlagskriterien)
Selection criteria (Eignungskriterien)
Technical specifications (Technische Spezifikationen)
Terms and conditions (Vertragsbedingungen)
Evidence requirements (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

Citation

If you use this dataset, please cite:

@dataset{swiss_sustainable_procurement_2026,
  title={Swiss Sustainable Public Procurement - Sustainability Criteria Detection Dataset},
  author={[Organization Name]},
  year={2026},
  url={[Dataset URL if applicable]}
}

License

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

Contact

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