dataset page: install line

README.md

@@ -120,21 +120,12 @@ traj.aux         # (T′, P) float32, dimensionless
 traj.time        # (T′,) float64, s
 ```
 
-## Splits
-
-The benchmark's fixed split assignment, by case id (the file name without `.h5`). `train` fits the model and `val` selects it; every other split is held out for reporting — `test_interp` sits inside the training parameter range, `test_extrap` beyond it, `probe` cases are off-grid stress tests. Files shipped outside these splits (`held_aside` in the manifest) are not part of the protocol.
-
-- **train** (88): `NB-I-320-Bullet-a-120`, `NB-I-320-Bullet-a-160`, `NB-I-320-Bullet-a-40`, `NB-I-320-Bullet-b-160`, `NB-I-320-Bullet-b-40`, `NB-I-320-Bullet-c-120`, `NB-I-320-Bullet-c-160`, `NB-I-320-Bullet-c-40`, `NB-I-320-Bullet-c-80`, `NB-I-320-Rectangular-a-120`, `NB-I-320-Rectangular-a-160`, `NB-I-320-Rectangular-a-40`, `NB-I-320-Rectangular-a-80`, `NB-I-320-Rectangular-b-160`, `NB-I-320-Rectangular-b-40`, `NB-I-320-Rectangular-b-80`, `NB-I-320-Rectangular-c-160`, `NB-I-320-Rectangular-c-40`, `NB-I-320-Rectangular-c-80`, `NB-I-320-Sphere-a-160`, `NB-I-320-Sphere-a-40`, `NB-I-320-Sphere-b-160`, `NB-I-320-Sphere-b-40`, `NB-I-320-Sphere-c-120`, `NB-I-320-Sphere-c-160`, `NB-I-320-Sphere-c-40`, `NB-I-320-Sphere-c-80`, `NB-I-480-Bullet-a-120`, `NB-I-480-Bullet-a-160`, `NB-I-480-Bullet-a-40`, `NB-I-480-Bullet-a-80`, `NB-I-480-Bullet-b-120`, `NB-I-480-Bullet-b-160`, `NB-I-480-Bullet-b-40`, `NB-I-480-Bullet-b-80`, `NB-I-480-Bullet-c-120`, `NB-I-480-Bullet-c-160`, `NB-I-480-Bullet-c-40`, `NB-I-480-Bullet-c-80`, `NB-I-480-Rectangular-a-120`, `NB-I-480-Rectangular-a-160`, `NB-I-480-Rectangular-a-40`, `NB-I-480-Rectangular-a-80`, `NB-I-480-Rectangular-b-160`, `NB-I-480-Rectangular-b-40`, `NB-I-480-Rectangular-c-120`, `NB-I-480-Rectangular-c-160`, `NB-I-480-Rectangular-c-40`, `NB-I-480-Rectangular-c-80`, `NB-I-480-Sphere-a-160`, `NB-I-480-Sphere-a-40`, `NB-I-480-Sphere-a-80`, `NB-I-480-Sphere-b-160`, `NB-I-480-Sphere-b-40`, `NB-I-480-Sphere-b-80`, `NB-I-480-Sphere-c-160`, `NB-I-480-Sphere-c-40`, `NB-I-480-Sphere-c-80`, `NB-I-640-Bullet-a-120`, `NB-I-640-Bullet-a-160`, `NB-I-640-Bullet-a-40`, `NB-I-640-Bullet-a-80`, `NB-I-640-Bullet-b-160`, `NB-I-640-Bullet-b-40`, `NB-I-640-Bullet-c-120`, `NB-I-640-Bullet-c-160`, `NB-I-640-Bullet-c-40`, `NB-I-640-Bullet-c-80`, `NB-I-640-Rectangular-a-160`, `NB-I-640-Rectangular-a-40`, `NB-I-640-Rectangular-a-80`, `NB-I-640-Rectangular-b-120`, `NB-I-640-Rectangular-b-160`, `NB-I-640-Rectangular-b-40`, `NB-I-640-Rectangular-b-80`, `NB-I-640-Rectangular-c-120`, `NB-I-640-Rectangular-c-160`, `NB-I-640-Rectangular-c-40`, `NB-I-640-Sphere-a-120`, `NB-I-640-Sphere-a-160`, `NB-I-640-Sphere-a-40`, `NB-I-640-Sphere-b-120`, `NB-I-640-Sphere-b-160`, `NB-I-640-Sphere-b-40`, `NB-I-640-Sphere-b-80`, `NB-I-640-Sphere-c-160`, `NB-I-640-Sphere-c-40`, `NB-I-640-Sphere-c-80`
-- **val** (8): `NB-I-320-Bullet-b-80`, `NB-I-320-Sphere-a-120`, `NB-I-320-Sphere-a-80`, `NB-I-480-Rectangular-b-80`, `NB-I-480-Sphere-c-120`, `NB-I-640-Bullet-b-120`, `NB-I-640-Rectangular-a-120`, `NB-I-640-Rectangular-c-80`
-- **test_interp** (12): `NB-I-320-Bullet-a-80`, `NB-I-320-Bullet-b-120`, `NB-I-320-Rectangular-b-120`, `NB-I-320-Rectangular-c-120`, `NB-I-320-Sphere-b-120`, `NB-I-320-Sphere-b-80`, `NB-I-480-Rectangular-b-120`, `NB-I-480-Sphere-a-120`, `NB-I-480-Sphere-b-120`, `NB-I-640-Bullet-b-80`, `NB-I-640-Sphere-a-80`, `NB-I-640-Sphere-c-120`
-- **probe** (2): `S_100_800_V60_extrapolation`, `S_80_400_V140_intrapolation`
-
 ## Benchmark protocol
 
 This archive backs the **NotchBeam2D-Impact** benchmark in StructBench. Task: autoregressive transition (ADR-0026); auxiliary target `max_principal_strain` (dimensionless); 6 input frames, horizon frames [6, 250) of 502 scored (250 µs, ADR-0039); full-length diagnostic, scored at native output times; quantities of interest: midspan_deflection_peak, cracked_fraction. The full evaluation protocol and its rationale, the baseline recipes and checkpoints, and the current leaderboard live on the benchmark page in the code repository — <https://github.com/qilinli/StructBench/blob/main/docs/benchmarks/notch_beam_2d_impact.md> — so the numbers have a single home. To train a baseline on this archive:
 
 ```bash
-pip install git+https://github.com/qilinli/StructBench  # or: pip install -e . from a clone
+pip install git+https://github.com/qilinli/StructBench # or: pip install -e .
 structbench-train --mode train --config configs/notch_beam_2d_impact/cgn.toml \
     --data-root /path/to/this/folder --out runs/notch_beam_2d_impact-cgn
 ```