docs: Path A — show how to filter or selectively load PR vs IF via class column / TASK_INVENTORY.json

README.md

@@ -307,6 +307,35 @@ ds = load_dataset("parquet",
                   split="train")
 ```
 
+#### Filtering PR vs IF in Path A
+
+Every row carries a `class` column with value `"Pure_Reasoning"` or
+`"Instruction_Following"`. Two ways to use it:
+
+```python
+# (1) Load all 107 shards, filter in code (requires downloading all ~370 GB)
+from datasets import load_dataset
+ds = load_dataset("May-apple/VBVR-Reorganized", split="train")
+pr_only = ds.filter(lambda x: x["class"] == "Pure_Reasoning")
+if_only = ds.filter(lambda x: x["class"] == "Instruction_Following")
+
+# (2) Load only the PR shards (downloads only ~195 GB) — use TASK_INVENTORY.json:
+import json
+from datasets import load_dataset
+inv = json.load(open("TASK_INVENTORY.json"))  # ship from this repo
+pr_shards = [f"parquet/train__{e['shard']}.parquet" for e in inv["Pure_Reasoning"]]
+ds_pr = load_dataset("parquet", data_files=pr_shards, split="train")
+
+# Same for IF:
+if_shards = [f"parquet/train__{e['shard']}.parquet" for e in inv["Instruction_Following"]]
+ds_if = load_dataset("parquet", data_files=if_shards, split="train")
+```
+
+`TASK_INVENTORY.json` is shipped at the repo root specifically so you
+can do this kind of selective load without enumerating shards by hand.
+For the canonical authoritative source on which task is PR vs IF, see
+[TASK_INVENTORY.md](TASK_INVENTORY.md).
+
 ### Path B — materialize parquet → on-disk directory layout
 
 If your existing pipeline reads `first_frame.png` / `ground_truth.mp4` /