README.md

Zero-Shot Hierarchical Text Classification Dataset

This dataset is prepared from the consolidated climate change solution extraction results.

Task

Zero-shot hierarchical multi-label text classification. Each example contains:

• text: classification input text built from solution details and document context.
• label_paths: one or more hierarchical labels, e.g. Long-term -> Mitigation and clean energy -> Pollution control and clean energy promotion.
• solution_detail_items: normalized solution_details items with their mapped label paths.
• labels: flattened labels for models that do not consume paths directly.
• metadata: city, country, text type, actor, climate event, source URL, and raw labels.

Files

• test.jsonl: all evaluation examples for zero-shot classification.
• all.jsonl: same examples as test.jsonl, provided as a neutral full dataset file.
• dataset.csv: tabular version for quick inspection.
• taxonomy.json: normalized phase/domain/category hierarchy for both solution and solution_details.
• candidate_labels.txt: flat candidate label list.
• stats.json: dataset statistics and unmapped source labels.

Statistics

• Source file: consolidated_results_1022_events.csv
• Total examples: 2083
• Parent labels: 2
• Domain labels: 8
• Leaf labels: 50

Suggested Zero-Shot Usage

Use taxonomy.json as the candidate label space. For hierarchical prediction, first predict the phase (Long-term or Short-term), then the solution domain, then restrict final category candidates to that branch. For models that support multi-label classification directly, evaluate against label_paths or the flattened labels field.