Fix task_categories + add modalities + Parquet splits for load_dataset()

README.md

@@ -4,8 +4,13 @@ language:
 - en
 pretty_name: CGM-JEPA Downstream Evaluation Splits
 task_categories:
+- tabular-classification
 - feature-extraction
-- other
+task_ids:
+- binary-classification
+modalities:
+- Time Series
+- Tabular
 tags:
 - continuous-glucose-monitor
 - cgm
@@ -14,19 +19,45 @@ tags:
 - metabolic-subphenotype
 - ogtt
 - healthcare
-- time-series-classification
+- time-series
+- subject-level-classification
 size_categories:
 - n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.parquet
+  - split: validation
+    path: validation.parquet
 ---
 
 # CGM-JEPA Downstream Evaluation Splits
 
 Labeled cohort splits used to evaluate CGM encoders on two binary metabolic outcomes — **insulin resistance** and **β-cell dysfunction** — in the paper *CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining*.
 
-> Downstream-only. For the unlabeled pretraining corpus (Stanford + Colas), see [`CRUISEResearchGroup/CGM-JEPA-Pretraining`](https://huggingface.co/datasets/CRUISEResearchGroup/CGM-JEPA-Pretraining). For pretrained encoder weights, see [`CRUISEResearchGroup/CGM-JEPA-Weights`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA-Weights).
+> Downstream-only. For the unlabeled pretraining corpus (Stanford + Colas), see [`CRUISEResearchGroup/CGM-JEPA-Pretraining`](https://huggingface.co/datasets/CRUISEResearchGroup/CGM-JEPA-Pretraining). For pretrained encoder weights, see [`CRUISEResearchGroup/CGM-JEPA`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA).
 
 ## Quick start
 
+### Option 1 — `datasets` library (recommended for analysis / fine-tuning)
+
+```python
+from datasets import load_dataset
+ds = load_dataset("CRUISEResearchGroup/CGM-JEPA-Downstream")
+# DatasetDict({
+#   train:      Dataset({features: ['subject', 'ctru_venous', 'ctru_cgm', ...,
+#                                   'ir_class', 'ir_regression',
+#                                   'beta_class', 'beta_regression'],
+#                       num_rows: 27}),
+#   validation: Dataset({..., num_rows: 17})
+# })
+```
+
+The two splits share a canonical 11-column schema (subject + 6 modality `Sequence(Value('float64'))` + 4 label fields). Modalities the train cohort doesn't have are `None` rather than empty — only `ctru_venous` is populated in the train split.
+
+### Option 2 — original nested JSON (used by the code repo's eval pipeline)
+
 ```bash
 huggingface-cli download CRUISEResearchGroup/CGM-JEPA-Downstream \
   --repo-type dataset --local-dir Dataset_Open
@@ -43,8 +74,10 @@ python scripts/run_all_eval.py
 
 | File | Subjects | Size | Role |
 |---|---|---|---|
-| `train_split.json` | 27 | ~45 KB | **Initial cohort** — used to train linear probes in the cohort-generalization regime |
-| `validation_split.json` | 17 | ~146 KB | **Validation cohort** — used as the test set across all regimes and as both train and test in the home-CGM in-domain regime |
+| `train.parquet` | 27 | ~30 KB | **Initial cohort** in `datasets`-friendly tabular form (one row per subject). |
+| `validation.parquet` | 17 | ~110 KB | **Validation cohort** in `datasets`-friendly tabular form. |
+| `train_split.json` | 27 | ~45 KB | Same data as `train.parquet`, in the nested JSON layout the code repo's `data_loaders/` expects. |
+| `validation_split.json` | 17 | ~146 KB | Same data as `validation.parquet`, nested JSON layout. |
 
 The two cohorts are **subject-disjoint by construction**: subjects appearing in both upstream groups were removed from the validation cohort during preprocessing.
 

train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:700f3feacac43e6b2a45d4acad91a66a8cdaab8ab77c034dea6cf5f89c9ebd63
+size 14126

validation.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0698a70d6ef93bc2d280126639c1a1fe72c99342c227c0a89fa1e1d02c43e91c
+size 40396