---
language:
- en
license: mit
pretty_name: CoastAdapt-KB Zero-Shot Hierarchical Events Dataset
size_categories:
- 1K<n<10K
task_categories:
- text-classification
task_ids:
- multi-label-classification
- zero-shot-classification
tags:
- climate-change
- climate-adaptation
- climate-mitigation
- hierarchical-classification
- multi-label-classification
- zero-shot-classification
- event-extraction
configs:
- config_name: default
  data_files:
  - split: test
    path: test.jsonl
---

# CoastAdapt-KB Zero-Shot Hierarchical Events Dataset

## Dataset Summary

This dataset is prepared from consolidated climate change solution extraction results. It is designed for zero-shot hierarchical multi-label text classification over climate adaptation and mitigation event records.

Each example contains a natural-language input text plus one or more hierarchical label paths. The labels organize climate-related solution details into a taxonomy with phase, domain, and category levels, such as `Long-term -> Mitigation and clean energy -> Pollution control and clean energy promotion`.

## Supported Tasks

- Zero-shot text classification
- Hierarchical text classification
- Multi-label classification
- Climate adaptation and mitigation solution analysis

## Dataset Structure

Each JSONL row contains:

- `text`: classification input text built from solution details and document context.
- `label_paths`: one or more hierarchical labels.
- `solution_detail_items`: normalized `solution_details` items with their mapped label paths.
- `labels`: flattened labels for models that do not consume hierarchical 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.

## Dataset Statistics

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

## Suggested 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.

Example loading code:

```python
from datasets import load_dataset

dataset = load_dataset("MapleBi/CoastAdapt-KB")
test_data = dataset["test"]
```

## Intended Use

This dataset is intended for research and benchmarking of zero-shot, hierarchical, and multi-label classification methods in the climate adaptation and mitigation domain. It can also be used to study how language models map climate event descriptions to structured solution taxonomies.

## Limitations and Biases

The dataset reflects the coverage, wording, and geographic distribution of the underlying source records. Some regions, actors, or climate solution types may be overrepresented or underrepresented. Labels are derived from a normalized taxonomy and source annotations, so ambiguous or emerging climate actions may not always fit cleanly into a single category.

Users should avoid treating the labels as exhaustive ground truth for policy evaluation or real-world climate impact assessment. Model predictions trained or evaluated on this dataset should be reviewed by domain experts before being used in decision-making workflows.

## License

This dataset is released under the MIT license.