Update README.md

README.md

@@ -13,17 +13,6 @@ It contains a **~1 GB subset** derived from the full QHMat dataset, intended for
 ### Dataset Description
 
 This dataset is a **small, byte-limited preview** intended for **peer review, sanity checks, and pipeline testing**. It contains **45 crystal/material structures** as serialized graph/tensor records (Hamiltonian and overlap blocks, geometry, and metadata). 
-### Direct Use
-
-- Training or benchmarking **graph neural networks** and related models on **Hamiltonian / overlap tensor blocks** paired with **crystal structure** fields.
-- **Dataset loader development** and **integration tests** before downloading larger subsets.
-- **Reproducibility checks** during peer review (inspect manifests, decode samples).
-
-### Out-of-Scope Use
-
-- **Not** a guaranteed statistically representative sample of the full corpus (structure count is small).
-- **Not** for safety-critical or high-stakes decisions without domain validation.
-- **Not** a substitute for the full release when training production-scale models.
 
 ### Files in this release
 
@@ -50,6 +39,40 @@ After `pickle.loads` on a `.pkl` file, typical `Data` attributes include (names
 
 Use `torch_geometric` introspection or print `data.keys` after loading.
 
+
+### Minimal load example (single sample)
+
+```python
+import io
+import json
+import pickle
+import tarfile
+
+import torch  # noqa: F401 — needed for unpickling tensors inside Data
+
+tar_path = "train-000000.tar"
+with tarfile.open(tar_path, "r") as tar:
+    pkl_info = tar.getmember("00000000.pkl")
+    pkl_bytes = tar.extractfile(pkl_info).read()
+    meta = json.loads(tar.extractfile(tar.getmember("00000000.json")).read())
+
+data = pickle.loads(pkl_bytes)
+print(meta)   # {'lmdb_key': ..., 'value_bytes': ...}
+print(data)   # torch_geometric.data.Data
+```
+
+### Direct Use
+
+- Training or benchmarking **graph neural networks** and related models on **Hamiltonian / overlap tensor blocks** paired with **crystal structure** fields.
+- **Dataset loader development** and **integration tests** before downloading larger subsets.
+- **Reproducibility checks** during peer review (inspect manifests, decode samples).
+
+### Out-of-Scope Use
+
+- **Not** a guaranteed statistically representative sample of the full corpus (structure count is small).
+- **Not** for safety-critical or high-stakes decisions without domain validation.
+- **Not** a substitute for the full release when training production-scale models.
+
 ### Splits
 
 - **Single split:** all samples are shipped under the `train-*` shard prefix (preview bundle). There is **no** separate val/test split in this artifact.
@@ -76,24 +99,3 @@ This dataset is **not expected** to contain personal data. Identifiers such as `
 - Pin dependency versions in your training repo.
 - Prefer citing the **paper + dataset version + manifest checksum** once available.
 
-
-### Minimal load example (single sample)
-
-```python
-import io
-import json
-import pickle
-import tarfile
-
-import torch  # noqa: F401 — needed for unpickling tensors inside Data
-
-tar_path = "train-000000.tar"
-with tarfile.open(tar_path, "r") as tar:
-    pkl_info = tar.getmember("00000000.pkl")
-    pkl_bytes = tar.extractfile(pkl_info).read()
-    meta = json.loads(tar.extractfile(tar.getmember("00000000.json")).read())
-
-data = pickle.loads(pkl_bytes)
-print(meta)   # {'lmdb_key': ..., 'value_bytes': ...}
-print(data)   # torch_geometric.data.Data
-```