Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
sample_id: string
country: string
cutoff: timestamp[s]
target_month: string
true_phase: int64
predicted_phase: int64
baseline_phase: int64
last_available_phase: struct<n: int64, accuracy: double, macro_f1_present_classes: double, macro_f1_all_five: double, phas (... 217 chars omitted)
child 0, n: int64
child 1, accuracy: double
child 2, macro_f1_present_classes: double
child 3, macro_f1_all_five: double
child 4, phase_recall: struct<1: double, 2: double, 3: double, 4: double, 5: double>
child 0, 1: double
child 1, 2: double
child 2, 3: double
child 3, 4: double
child 4, 5: double
child 5, invalid_rate: double
child 6, ordinal_mae_valid_only: double
child 7, secondary_deterioration: struct<n: int64, f1: double, recall: double, definition: string>
child 0, n: int64
child 1, f1: double
child 2, recall: double
child 3, definition: string
training_majority: struct<n: int64, accuracy: double, macro_f1_present_classes: double, macro_f1_all_five: double, phas (... 217 chars omitted)
child 0, n: int64
child 1, accuracy: double
child 2, macro_f1_present_classes: double
child 3, macro_f1_all_five: double
child 4, phase_recall: struct<1: double, 2: double, 3: double, 4: double, 5: double>
child 0, 1: double
child 1, 2: double
child 2, 3: double
child 3, 4: double
child 4, 5: double
child 5, invalid_rate: double
child 6, ordinal_mae_valid_only: double
child 7, secondary_deterioration: struct<n: int64, f1: double, recall: double, definition: string>
child 0, n: int64
child 1, f1: double
child 2, recall: double
child 3, definition: string
to
{'training_majority': {'n': Value('int64'), 'accuracy': Value('float64'), 'macro_f1_present_classes': Value('float64'), 'macro_f1_all_five': Value('float64'), 'phase_recall': {'1': Value('float64'), '2': Value('float64'), '3': Value('float64'), '4': Value('float64'), '5': Value('float64')}, 'invalid_rate': Value('float64'), 'ordinal_mae_valid_only': Value('float64'), 'secondary_deterioration': {'n': Value('int64'), 'f1': Value('float64'), 'recall': Value('float64'), 'definition': Value('string')}}, 'last_available_phase': {'n': Value('int64'), 'accuracy': Value('float64'), 'macro_f1_present_classes': Value('float64'), 'macro_f1_all_five': Value('float64'), 'phase_recall': {'1': Value('float64'), '2': Value('float64'), '3': Value('float64'), '4': Value('float64'), '5': Value('float64')}, 'invalid_rate': Value('float64'), 'ordinal_mae_valid_only': Value('float64'), 'secondary_deterioration': {'n': Value('int64'), 'f1': Value('float64'), 'recall': Value('float64'), 'definition': Value('string')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
sample_id: string
country: string
cutoff: timestamp[s]
target_month: string
true_phase: int64
predicted_phase: int64
baseline_phase: int64
last_available_phase: struct<n: int64, accuracy: double, macro_f1_present_classes: double, macro_f1_all_five: double, phas (... 217 chars omitted)
child 0, n: int64
child 1, accuracy: double
child 2, macro_f1_present_classes: double
child 3, macro_f1_all_five: double
child 4, phase_recall: struct<1: double, 2: double, 3: double, 4: double, 5: double>
child 0, 1: double
child 1, 2: double
child 2, 3: double
child 3, 4: double
child 4, 5: double
child 5, invalid_rate: double
child 6, ordinal_mae_valid_only: double
child 7, secondary_deterioration: struct<n: int64, f1: double, recall: double, definition: string>
child 0, n: int64
child 1, f1: double
child 2, recall: double
child 3, definition: string
training_majority: struct<n: int64, accuracy: double, macro_f1_present_classes: double, macro_f1_all_five: double, phas (... 217 chars omitted)
child 0, n: int64
child 1, accuracy: double
child 2, macro_f1_present_classes: double
child 3, macro_f1_all_five: double
child 4, phase_recall: struct<1: double, 2: double, 3: double, 4: double, 5: double>
child 0, 1: double
child 1, 2: double
child 2, 3: double
child 3, 4: double
child 4, 5: double
child 5, invalid_rate: double
child 6, ordinal_mae_valid_only: double
child 7, secondary_deterioration: struct<n: int64, f1: double, recall: double, definition: string>
child 0, n: int64
child 1, f1: double
child 2, recall: double
child 3, definition: string
to
{'training_majority': {'n': Value('int64'), 'accuracy': Value('float64'), 'macro_f1_present_classes': Value('float64'), 'macro_f1_all_five': Value('float64'), 'phase_recall': {'1': Value('float64'), '2': Value('float64'), '3': Value('float64'), '4': Value('float64'), '5': Value('float64')}, 'invalid_rate': Value('float64'), 'ordinal_mae_valid_only': Value('float64'), 'secondary_deterioration': {'n': Value('int64'), 'f1': Value('float64'), 'recall': Value('float64'), 'definition': Value('string')}}, 'last_available_phase': {'n': Value('int64'), 'accuracy': Value('float64'), 'macro_f1_present_classes': Value('float64'), 'macro_f1_all_five': Value('float64'), 'phase_recall': {'1': Value('float64'), '2': Value('float64'), '3': Value('float64'), '4': Value('float64'), '5': Value('float64')}, 'invalid_rate': Value('float64'), 'ordinal_mae_valid_only': Value('float64'), 'secondary_deterioration': {'n': Value('int64'), 'f1': Value('float64'), 'recall': Value('float64'), 'definition': Value('string')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
OpenRelief
A retrospective research dataset and benchmark pairing district-level food-security histories with national context, built to study FEWS NET IPC phase forecasting and time-series-to-language supervision — teaching a language model to connect historical signals into a written, evidence-cited rationale rather than just a number.
Built for the Temporal AI Challenge. Source code, frontend and full documentation: github.com/luk-huebner/OpenRelief.
Task
Given six months of history across 21 channels (food-security indices, prior IPC assessments, shipping, conflict, rainfall, staple prices), predict the FEWS NET IPC phase (1 Minimal → 5 Famine) three calendar months ahead, with a generated rationale, cross-domain hypotheses and recommended actions.
Dataset at a glance
| Field | Value |
|---|---|
| Dataset version | 8142c89de89cb862117d6a814be515b9f00312bb960e33cf7e994a99ed124bd3 |
| Train | 9,065 examples / 16 countries (cutoffs through Dec 2022) |
| Validation | 2,503 examples / 16 countries (Jan–Jul 2023 cutoffs) |
| Test | 2,230 examples / 14 countries (Aug–Dec 2023 cutoffs, target exactly +3 months) |
| Channels | 21 (six monthly observations each, plus observed-value masks) |
| Test label support | Phase 1: 854 · Phase 2: 976 · Phase 3: 395 · Phase 4: 5 · Phase 5: 0 |
| Annotation coverage | 2,781 / 9,065 training records (30.7%) — partial, not complete |
Prepared arrays live under artifacts/dataset/ and artifacts/multimodal/
(inputs.jsonl, targets.jsonl, manifest.json). Loaders verify dataset checksums
against the version above before use.
Sources
- HFID — district panel: FEWS NET IPC labels, normalized FCS/rCSI.
- IMF PortWatch — national shipping/port-call volumes.
- ACLED — national conflict events and fatalities.
- CHIRPS — country-average rainfall.
- WFP — national staple-food price series, with commodity/unit metadata.
National covariates are aggregated to country level; only FCS/rCSI and the IPC label are at district (admin2) resolution. The benchmark assumes one-month release lags for HFID/PortWatch/ACLED/WFP and two months for CHIRPS — it does not establish what could actually have been forecast in real time. Full provenance: SOURCES.md, DATASET_CARD.md.
Annotations
gpt-5.6-terra generated structured training supervision for 2,781 of the 9,065
training examples: precursor claims (with channel citations), cross-domain hypotheses,
a written rationale, driver-cited recommended actions, and an uncertainty/confidence
note. The teacher was given the true future IPC phase — this is retrospective
supervision, not independent forecasting reasoning, and hypotheses are not proven
causes. Unannotated training examples train on an empty rationale/action target
(phase-only supervision).
Three fully worked examples — one deterioration, one persistent crisis, one
improvement, each with its input charts, generated hypotheses, verbatim rationale and
recommended actions — are in the
annotation atlas
(exact examples: docs/examples/annotation-atlas/examples.json). It is an editorial
illustration of three selected cases, not a representative quality sample.
Model results (this repo's gpu-results/)
OpenTSLM/llama-3.2-1b-tsqa-sp (Llama 3.2 1B backbone), fine-tuned with LoRA
(rank 16, alpha 32) on the 2,781 annotated training examples.
Fine-tuning is not optional. The untouched pretrained checkpoint, evaluated on the
full 2,230-example test set with no LoRA at all, produced 0% valid JSON output —
it never attempts the task's output format. See gpu-results/pretrained-fulltest/.
Source ablation (256-example eval cohort, one source's channels dropped at a time — see caveat below):
| Run | Macro-F1 | Accuracy |
|---|---|---|
| All sources (baseline) | 0.7199 | 0.7344 |
| Drop conflict (ACLED) | 0.7428 | 0.7617 |
| Drop rainfall (CHIRPS) | 0.7286 | 0.7500 |
| Drop prices (WFP) | 0.7300 | 0.7500 |
| Drop shipping (PortWatch) | 0.5826 | 0.7852 |
Dropping PortWatch (shipping) is the only ablation that meaningfully hurts the model — a 0.14 macro-F1 drop, far outside the noise band of the other three. Full writeup: FINDING-portwatch-signal.md.
Caveat: the ablation table above is a 256-example cohort with only one phase-4 example and no phase-5 examples in the full test set at all — do not read this as demonstrated famine prediction. The clearer, full-test-set wins are the pretrained-vs-fine-tuned gap above and the PortWatch ablation signal; match sample IDs before comparing across cohorts. See STATUS.md for the full, currently-known comparison table.
The fine-tuned LoRA adapter (gpu-results/all-sources/best_model.pt, SHA-256
a10343caa152d1c3aa55b6dc9b40e11603067babeac44909001d1597a3e84e10) is deployed as a
live inference endpoint that powers the project's demo frontend.
Repository layout
artifacts/dataset/ prepared inputs.jsonl / targets.jsonl / manifest.json
artifacts/multimodal/ multimodal-formatted version of the same splits
artifacts/annotation-cache/ cached per-example teacher annotation responses
artifacts/*.manifest.json acquisition manifests per source connector
gpu-results/ fine-tuning + ablation runs (see table above)
all-sources/ baseline checkpoint, losses, benchmark, best_model.pt
ablation-{acled,chirps,wfp,portwatch}/ one-source-dropped reruns
pretrained-fulltest/ untouched-checkpoint baseline, full test set
manifest.json git commit + image digest + launch command per run
refrences/ background reading + one raw source CSV
Limitations
- National-level signals (shipping, conflict, rainfall, price) do not prove district-level exposure; only FCS/rCSI and the IPC label are district-resolution.
- FCS/rCSI normalization direction is undocumented — numeric movement is not given a severity interpretation.
- No phase-5 test examples exist and only five phase-4 examples — this dataset does not support a claim of demonstrated famine prediction.
- Ablation effects are associative, measured against removed input channels, not causal claims.
- Historical publication timestamps and revisions are unavailable; assumed release lags are a modeling choice, not an observed fact.
License and reuse
Repository code is Apache-2.0. Access to an upstream source here does not imply unrestricted redistribution rights over that source's original data — retain IMF PortWatch terms, ACLED terms of use, WFP source metadata, CHIRPS attribution and geoBoundaries attribution as applicable to each channel. See DATASET_CARD.md for the full statement.
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