water-conflict-classifier 0.1.13

SetFit-based multi-label classifier for water-related conflict events

Water Conflict Classifier

SetFit-based multi-label text classifier for identifying water-related conflict events in news headlines.

This folder contains the package source code. For usage instructions with the published package, see the PyPI page.

Project: Experimental research supporting the Pacific Institute's Water Conflict Chronology
Developer: Baobab Tech
License: CC BY-NC 4.0 (Non-Commercial)
PyPI Package: water-conflict-classifier

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Package Installation & Usage

Install from PyPI:

pip install water-conflict-classifier

Use the trained model:

from setfit import SetFitModel

model = SetFitModel.from_pretrained("baobabtech/water-conflict-classifier")
predictions = model.predict(["Taliban attack workers at dam"])
# Returns: [[1, 1, 1]]  # [Trigger, Casualty, Weapon]

The rest of this README is for developers who want to train their own model or modify the package.

Frugal AI: Training with Limited Data

This classifier demonstrates an intentional approach to building AI systems with limited data using SetFit - a framework for few-shot learning with sentence transformers. Rather than defaulting to massive language models (GPT, Claude, or 100B+ parameter models) for simple classification tasks, we fine-tune small, efficient models (e.g., BAAI/bge-small-en-v1.5 with ~33M parameters) on a focused dataset.

Why this matters: The industry has normalized using trillion-parameter models to classify headlines, answer simple questions, or categorize text - tasks that don't require world knowledge, reasoning, or generative capabilities. This is computationally wasteful and environmentally costly. A properly fine-tuned small model can achieve comparable or better accuracy while using a fraction of the compute resources.

Our approach:

• Train on ~600 examples (few-shot learning with SetFit)
• Deploy small parameter models (e.g., ~33M params) vs. 100B-1T parameter alternatives
• Achieve specialized task performance without the overhead of general-purpose LLMs
• Reduce inference costs and latency by orders of magnitude

This is not about avoiding large models altogether - they're invaluable for complex reasoning tasks. But for targeted classification problems with labeled data, fine-tuning remains the professional, responsible choice.

Package Structure

This is the source code for the water-conflict-classifier Python package, published to PyPI.

classifier/
├── __init__.py                         # Package marker
├── data_prep.py                        # Data loading & preprocessing
├── training_logic.py                   # Core training logic
├── evaluation.py                       # Model evaluation & metrics
├── model_card.py                       # Model card generation
├── train_setfit_headline_classifier.py # Local training script
├── pyproject.toml                      # Package configuration
├── setup.py                            # Build configuration
└── README.md                           # This file

Note: Scripts that use this package (like cloud training with HF Jobs) are in the ../scripts/ folder.

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Local Training

Train on your own hardware with local data files.

Setup

1. Install the package in development mode:

uv pip install -e .
# or with regular pip:
pip install -e .

This installs the modules (data_prep, training_logic, etc.) so they can be imported.

2. Prepare training data:

Training data should be in ../data/:

• ../data/positives.csv - Water conflict headlines with labels
• ../data/negatives.csv - Non-water conflict headlines

Generate negatives from ACLED (if needed):

cd ../scripts
python transform_prep_negatives.py

3. Train:

python train_setfit_headline_classifier.py

Model saved to ./water-conflict-classifier/

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Cloud Training

For training on HuggingFace Jobs (managed GPUs), see ../scripts/train_on_hf.py and the scripts README.

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Publishing to PyPI

See PUBLISHING.md for complete instructions on building and publishing the package.

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Data Sources

The training data combines:

• Positive Examples: Water conflict headlines from Pacific Institute Water Conflict Chronology
• Negative Examples: Two types for balanced training:
  1. Hard Negatives (~120): Water-related peaceful news (infrastructure, research, conservation) to prevent false positives
  2. ACLED Negatives (~600): Non-water conflict events from ACLED

Hard Negatives Strategy

Without hard negatives, the model learns "water mentioned → conflict" instead of "water + violence → conflict". Hard negatives are water-related headlines that lack violence or conflict:

• Water infrastructure projects (dams, treatment plants)
• Scientific water research and technology
• Water conservation initiatives and conferences
• Environmental water management

These are tagged with priority_sample=True in the dataset and are ALWAYS included in training (never diluted by sampling). This ensures the model correctly distinguishes peaceful water news from actual water conflicts.

Resources

• HF Jobs Guide
• UV Script Format (used in train_on_hf.py)
• SetFit Documentation
• Pacific Institute Water Conflict Chronology
• ACLED Data

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License

Copyright © 2025 Baobab Tech

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

You are free to use, share, and adapt this work for non-commercial purposes with appropriate attribution to Baobab Tech. For commercial licensing inquiries, please contact Baobab Tech.