Card: quickstart snippet, dev_mini, task-based truth-file descriptions

README.md

@@ -20,7 +20,24 @@ size_categories:
 
 CARD pairs synthetic retail scanner panels with marketing-copy product descriptions that carry the true substitution geometry. Demand is simulated from a known data-generating process; in half the cells, promotion depth responds to a hidden demand shock, so estimators that ignore endogeneity fit the observed data well and still get the counterfactuals wrong. True elasticities and counterfactual outcomes are hidden and used only for scoring.
 
-**Code, submission format, and scoring harness:** https://github.com/jean-jsj/CARD
+**Code, submission format, scoring harness, and example notebooks:** https://github.com/jean-jsj/CARD
+
+## Quickstart
+
+```python
+from huggingface_hub import snapshot_download
+import pandas as pd
+
+# ~18 MB starter slice (10 stores); use "dev/..." for the full ~1 GB cell
+cell = "complex_log_log_endogenous_seed001"
+snapshot_download(repo_id="jean-jsj/CARD", repo_type="dataset",
+                  allow_patterns=[f"dev_mini/{cell}/*"], local_dir="benchmark")
+
+train = pd.read_csv(f"benchmark/dev_mini/{cell}/public/transactions_train_public.csv")
+products = pd.read_csv(f"benchmark/dev_mini/{cell}/public/products_public.csv")
+```
+
+Then score a first submission in minutes with the [GitHub quickstart](https://github.com/jean-jsj/CARD#try-it-in-3-minutes) or the [notebook ladder](https://github.com/jean-jsj/CARD/tree/main/examples) (Colab-ready). A column-by-column schema is in the repo's [data dictionary](https://github.com/jean-jsj/CARD/blob/main/docs/DATA.md).
 
 ## The 2×2 grid
 
@@ -34,27 +51,29 @@ Every cell is the full market — 40 products, 731 stores — and covers 156 wee
 ## Layout
 
 ```
-dev/<cell_slug>/
+dev/<cell_slug>/                           # full cells (~1 GB each)
   public/    # everything a model may consume
     transactions_train_public.csv            # product, store, week, units, dollars,
                                              # price, promo_flag, promo_cost, supply_cost_proxy
     transactions_holdout_context_public.csv  # holdout weeks: prices/promos public, sales withheld
-    counterfactual_sweep_context_public.csv  # the 16 scored price interventions
+    counterfactual_sweep_context_public.csv  # the 16 scored price scenarios
     products_public.csv                      # product_id, product_text, brand_code
     stores_public.csv                        # store_id, market, chain
   hidden/    # DEV SEED ONLY: scoring truth for instant local scoring
-    transactions_full_hidden.csv             # Layer-1 truth (holdout sales)
-    elasticity_truth_hidden.csv              # Layer-2 truth (J x J elasticities)
-    counterfactual_sweep_truth_hidden.csv    # Layer-3 truth (counterfactual demand)
+    transactions_full_hidden.csv             # full 156-week panel; last 16 weeks = forecasting truth
+    elasticity_truth_hidden.csv              # the true J x J elasticity matrix
+    counterfactual_sweep_truth_hidden.csv    # the true counterfactual demand changes
   release/
     MANIFEST.json                            # per-file SHA-256
     scoring_params.json                      # scoring config (family, eval window)
     release_notes.md, DATASHEET.md
+
+dev_mini/<cell_slug>/                      # ~18 MB 10-store starter slices (log-log pair)
 ```
 
 **Data-access rule:** models consume `public/` files only. `hidden/` exists for local scoring on the dev seed, never as model input. Eval seeds (added later) ship public-only; their truth stays with the maintainer.
 
-A `reference/` tree holds the four reference models' submission-format predictions (`reference/<model>/<cell_slug>/`, one directory per corner of the instruments × text grid); their scores and descriptions live in the GitHub repo's `submissions/` directory. A completed datasheet is at `DATASHEET.md`.
+A `reference/` tree holds the four reference models' submission-format predictions (`reference/<model>/<cell_slug>/`, one directory per corner of the instruments × text grid); their scores and descriptions live in the GitHub repo's `submissions/` directory, and the estimator code is in its `baselines/` directory. A completed datasheet is at `DATASHEET.md`.
 
 ## Notes