Water Conflict Classifier
Collection
Models and datasets for the classification of news and event headlines aligned with the Water Conflict Chronology by the Pacific Institute
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5 items
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Updated
Datasets:
version
string | timestamp
string | base_model
string | train_size
int64 | test_size
int64 | full_train_size
float64 | batch_size
int64 | num_epochs
int64 | sample_size
float64 | sampling_strategy
string | test_split
float64 | num_iterations
float64 | f1_micro
float64 | f1_macro
float64 | f1_samples
float64 | accuracy
float64 | hamming_loss
float64 | trigger_precision
float64 | trigger_recall
float64 | trigger_f1
float64 | trigger_support
int64 | casualty_precision
float64 | casualty_recall
float64 | casualty_f1
float64 | casualty_support
int64 | weapon_precision
float64 | weapon_recall
float64 | weapon_f1
float64 | weapon_support
int64 | model_repo
string | dataset_repo
string | dataset_version
string | notes
float64 | training_type
string | n_trials
float64 | search_sample_size
float64 | search_models
bool | best_hyperparameters
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
v1.0
|
2025-11-27T05:17:07.315790
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 2,937
| 64
| 1
| 1,200
|
undersampling
| 0.15
| null | 0.884026
| 0.814339
| 0.7158
| 0.851638
| 0.06808
| 0.914286
| 0.924855
| 0.91954
| 173
| 0.903361
| 0.922747
| 0.912951
| 233
| 0.674419
| 0.557692
| 0.610526
| 52
|
baobabtech/water-conflict-classifier
|
baobabtech/water-conflict-training-data
| null | null | null | null | null | null | null |
v1.0
|
2025-11-27T19:20:51.778713
|
sentence-transformers/all-MiniLM-L6-v2
| 1,200
| 519
| 2,937
| 64
| 1
| 1,200
|
undersampling
| 0.15
| null | 0.873085
| 0.785678
| 0.703918
| 0.83815
| 0.074502
| 0.891429
| 0.901734
| 0.896552
| 173
| 0.883534
| 0.944206
| 0.912863
| 233
| 0.71875
| 0.442308
| 0.547619
| 52
|
baobabtech/water-conflict-classifier-minilm
|
baobabtech/water-conflict-training-data
| null | null | null | null | null | null | null |
v1.0
|
2025-11-27T20:28:51.973089
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 2,937
| 64
| 1
| 1,200
|
undersampling
| 0.15
| null | 0.884026
| 0.814339
| 0.7158
| 0.851638
| 0.06808
| 0.914286
| 0.924855
| 0.91954
| 173
| 0.903361
| 0.922747
| 0.912951
| 233
| 0.674419
| 0.557692
| 0.610526
| 52
|
baobabtech/water-conflict-classifier
|
baobabtech/water-conflict-training-data
| null | null | null | null | null | null | null |
v1.0
|
2025-11-27T20:53:26.770653
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 2,937
| 64
| 1
| 1,200
|
undersampling
| 0.15
| null | 0.884026
| 0.814339
| 0.7158
| 0.851638
| 0.06808
| 0.914286
| 0.924855
| 0.91954
| 173
| 0.903361
| 0.922747
| 0.912951
| 233
| 0.674419
| 0.557692
| 0.610526
| 52
|
baobabtech/water-conflict-classifier
| null | null | null | null | null | null | null | null |
v1.1
|
2025-11-27T21:03:58.453990
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 2,937
| 64
| 1
| 1,200
|
undersampling
| 0.15
| null | 0.884026
| 0.814339
| 0.7158
| 0.851638
| 0.06808
| 0.914286
| 0.924855
| 0.91954
| 173
| 0.903361
| 0.922747
| 0.912951
| 233
| 0.674419
| 0.557692
| 0.610526
| 52
|
baobabtech/water-conflict-classifier
| null | null | null | null | null | null | null | null |
v1.2
|
2025-11-30T00:15:31.781887
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| null | 64
| 1
| null |
undersampling
| null | 20
| 0.878723
| 0.809403
| 0.716442
| 0.83815
| 0.073218
| 0.91954
| 0.924855
| 0.92219
| 173
| 0.919492
| 0.93133
| 0.925373
| 233
| 0.5
| 0.692308
| 0.580645
| 52
|
baobabtech/water-conflict-classifier
| null | null | null | null | null | null | null | null |
v2.0
|
2025-11-30T00:32:52.657728
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| null | 64
| 1
| null |
undersampling
| null | 20
| 0.862327
| 0.814176
| 0.704817
| 0.816956
| 0.082852
| 0.888889
| 0.873563
| 0.881159
| 174
| 0.890756
| 0.909871
| 0.900212
| 233
| 0.57971
| 0.769231
| 0.661157
| 52
|
baobabtech/water-conflict-classifier
| null | null | null | null | null | null | null | null |
v2.1
|
2025-11-30T01:09:00.509893
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 1,719
| 64
| 1
| 1,200
|
undersampling
| 0.30192
| 20
| 0.862327
| 0.814176
| 0.704817
| 0.816956
| 0.082852
| 0.888889
| 0.873563
| 0.881159
| 174
| 0.890756
| 0.909871
| 0.900212
| 233
| 0.57971
| 0.769231
| 0.661157
| 52
|
baobabtech/water-conflict-classifier
| null |
d2.0
| null | null | null | null | null | null |
v2.2
|
2025-11-30T01:39:22.540112
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 1,719
| 16
| 3
| 1,200
|
undersampling
| 0.30192
| 20
| 0.86875
| 0.823141
| 0.709377
| 0.822736
| 0.080925
| 0.888889
| 0.873563
| 0.881159
| 174
| 0.876984
| 0.948498
| 0.91134
| 233
| 0.564103
| 0.846154
| 0.676923
| 52
|
baobabtech/water-conflict-classifier
| null |
d2.0
| null | null | null | null | null | null |
v2.3
|
2025-11-30T06:32:31.012425
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 1,719
| 32
| 4
| 1,200
|
undersampling
| 0.30192
| 20
| 0.870021
| 0.822135
| 0.708992
| 0.822736
| 0.07964
| 0.875
| 0.885057
| 0.88
| 174
| 0.890244
| 0.939914
| 0.914405
| 233
| 0.575342
| 0.807692
| 0.672
| 52
|
baobabtech/water-conflict-classifier
| null |
d2.0
| null | null | null | null | null | null |
v2.4
|
2025-11-30T08:39:53.600475
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 1,719
| 16
| 3
| null |
undersampling
| 0.30192
| 20
| 0.794297
| 0.72961
| 0.65896
| 0.691715
| 0.129737
| 0.843023
| 0.833333
| 0.83815
| 174
| 0.854772
| 0.88412
| 0.869198
| 233
| 0.354545
| 0.75
| 0.481481
| 52
|
baobabtech/water-conflict-classifier
| null |
d2.0
| null |
optuna
| 50
| 200
| false
|
{'body_learning_rate': 1.4291965748432987e-06, 'head_learning_rate': 0.010793429167673138, 'num_epochs': 3, 'batch_size': 16, 'num_iterations': 20, 'seed': 72, 'max_iter': 153, 'solver': 'lbfgs', 'sampling_strategy': 'undersampling'}
|
v2.5
|
2025-12-02T02:13:03.385824
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 1,719
| 32
| 2
| 1,200
|
oversampling
| 0.30192
| 20
| 0.865608
| 0.810628
| 0.70745
| 0.818882
| 0.081567
| 0.895349
| 0.885057
| 0.890173
| 174
| 0.888889
| 0.927039
| 0.907563
| 233
| 0.549296
| 0.75
| 0.634146
| 52
|
baobabtech/water-conflict-classifier
| null |
d2.0
| null |
standard
| null | null | null | null |
v2.6
|
2025-12-02T02:40:51.360905
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 1,719
| 16
| 3
| 1,200
|
undersampling
| 0.30192
| null | 0.86875
| 0.823141
| 0.709377
| 0.822736
| 0.080925
| 0.888889
| 0.873563
| 0.881159
| 174
| 0.876984
| 0.948498
| 0.91134
| 233
| 0.564103
| 0.846154
| 0.676923
| 52
|
baobabtech/water-conflict-classifier
| null |
d2.0
| null |
standard
| null | null | null | null |
v2.7
|
2025-12-02T03:07:10.699513
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 1,719
| 64
| 2
| 1,200
|
undersampling
| 0.30192
| 20
| 0.852321
| 0.798252
| 0.699294
| 0.809249
| 0.089917
| 0.90303
| 0.856322
| 0.879056
| 174
| 0.880658
| 0.918455
| 0.89916
| 233
| 0.506173
| 0.788462
| 0.616541
| 52
|
baobabtech/water-conflict-classifier
| null |
d2.0
| null |
standard
| null | null | null | null |
v2.8
|
2025-12-02T04:01:31.981874
|
BAAI/bge-small-en-v1.5
| 1,200
| 519
| 1,719
| 64
| 1
| 1,200
|
undersampling
| 0.30192
| 20
| 0.852972
| 0.798679
| 0.702762
| 0.801541
| 0.090559
| 0.884393
| 0.87931
| 0.881844
| 174
| 0.884298
| 0.918455
| 0.901053
| 233
| 0.494118
| 0.807692
| 0.613139
| 52
|
baobabtech/water-conflict-classifier
| null |
d2.0
| null |
standard
| null | null | null | null |
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:
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
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:
@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
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