Write full dataset card: task, splits, sources, annotations, model results, limitations

README.md

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 ---
 license: apache-2.0
+pretty_name: Open Relief — 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
 ---
+
+# Open Relief
+
+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, and match sample IDs before comparing against any
+other reported number (e.g. a naive last-known-phase persistence baseline scores
+0.735 macro-F1 on the full 2,230-example test set, ahead of the 256-example fine-tuned
+number above on a different cohort). 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.