| --- |
| license: apache-2.0 |
| pretty_name: OpenRelief — Food-Security Forecasting Dataset |
| task_categories: |
| - time-series-forecasting |
| - text-generation |
| tags: |
| - food-security |
| - ipc |
| - famine-early-warning |
| - time-series |
| - humanitarian |
| - llm-annotation |
| language: |
| - en |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # 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](https://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](https://github.com/luk-huebner/OpenRelief/blob/main/docs/SOURCES.md), |
| [DATASET_CARD.md](https://github.com/luk-huebner/OpenRelief/blob/main/docs/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](https://github.com/luk-huebner/OpenRelief/blob/main/docs/examples/annotation-atlas/README.md) |
| (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](https://github.com/luk-huebner/OpenRelief/blob/main/docs/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](https://github.com/luk-huebner/OpenRelief/blob/main/docs/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](https://github.com/luk-huebner/OpenRelief/blob/main/docs/DATASET_CARD.md#access-reuse-and-reproducibility) |
| for the full statement. |
|
|
