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baobabtech/water-conflict-classifier-evals

Water Conflict Classifier Evaluation Metrics Evaluation metrics tracking the performance of the Water Conflict Classifier across multiple training iterations and model configurations. Dataset Summary This dataset contains evaluation results from training runs of the Water Conflict Classifier, a multi-label SetFit model that identifies water-related conflict events in news headlines. Each row represents one model version with comprehensive performance metrics… See the full description on the dataset page: https://huggingface.co/datasets/baobabtech/water-conflict-classifier-evals.

sourceHugging Facecc-by-nc-4.0updated 10mo agoView on Hugging Face

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Water Conflict Classifier Evaluation Metrics

Evaluation metrics tracking the performance of the Water Conflict Classifier across multiple training iterations and model configurations.

Dataset Summary

This dataset contains evaluation results from training runs of the Water Conflict Classifier, a multi-label SetFit model that identifies water-related conflict events in news headlines. Each row represents one model version with comprehensive performance metrics across three classification labels: Trigger, Casualty, and Weapon.

Related Links:

—🤗 Model Collection
—🐙 GitHub Repository
—📦 PyPI Package
—🌊 Pacific Institute Water Conflict Chronology

Dataset Structure

Fields

Field	Type	Description
`version`	string	Model version identifier (v1.0, v2.0, etc.)
`timestamp`	string	Training completion timestamp
`base_model`	string	Base embedding model used
`train_size`	int	Number of training examples
`test_size`	int	Number of test examples
`f1_micro`	float	Micro-averaged F1 score
`f1_macro`	float	Macro-averaged F1 score
`accuracy`	float	Overall accuracy
`trigger_*`	float	Precision/recall/F1 for Trigger label
`casualty_*`	float	Precision/recall/F1 for Casualty label
`weapon_*`	float	Precision/recall/F1 for Weapon label
`model_repo`	string	HuggingFace model repository

Model Versions

The dataset tracks performance across different configurations:

—Base models: BAAI/bge-small-en-v1.5, sentence-transformers/all-MiniLM-L6-v2
—Training strategies: undersampling for class balance
—Hyperparameter variations: batch size, epochs, sample size

Usage

python

from datasets import load_dataset

# Load the evaluation metrics
evals = load_dataset("baobabtech/water-conflict-classifier-evals")

# Compare model versions
import pandas as pd
df = pd.DataFrame(evals['train'])
print(df[['version', 'f1_macro', 'accuracy']].sort_values('f1_macro', ascending=False))

Citation

If you use this dataset or the Water Conflict Classifier in your research, please cite:

bibtex

@misc{baobab_water_conflict_classifier,
  author = {Mills, Olivier},
  title = {Water Conflict Classifier: Few-Shot Learning for Water-Related Conflict Event Detection},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/baobabtech/water-conflict-classifier}}
}

License

CC-BY-NC-4.0 (Non-commercial use only)

Contact

Olivier Mills Website: baobabtech.ai LinkedIn: oliviermills