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README.md

@@ -1,15 +1,53 @@
-# Zero-Shot Hierarchical Text Classification Dataset
+---
+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
+---
 
-This dataset is prepared from the consolidated climate change solution extraction results.
+# CoastAdapt-KB Zero-Shot Hierarchical Events Dataset
 
-## Task
+## Dataset Summary
 
-Zero-shot hierarchical multi-label text classification. Each example contains:
+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, e.g. `Long-term -> Mitigation and clean energy -> Pollution control and clean energy promotion`.
+- `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 paths directly.
+- `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
@@ -21,7 +59,7 @@ Zero-shot hierarchical multi-label text classification. Each example contains:
 - `candidate_labels.txt`: flat candidate label list.
 - `stats.json`: dataset statistics and unmapped source labels.
 
-## Statistics
+## Dataset Statistics
 
 - Source file: `consolidated_results_1022_events.csv`
 - Total examples: `2083`
@@ -29,6 +67,29 @@ Zero-shot hierarchical multi-label text classification. Each example contains:
 - Domain labels: `8`
 - Leaf labels: `50`
 
-## Suggested Zero-Shot Usage
+## 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.