Point the viewer at the Parquet rendering; document both files

README.md

@@ -14,6 +14,11 @@ tags:
   - trajectories
   - reasoning
   - real-user-data
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: parquet/sessions.parquet
 ---
 
 # ACT-2.5B
@@ -60,6 +65,34 @@ determinable, by cause.
 An anonymized mirror of the experiment and synthesis code accompanies the paper under
 review.
 
+## Files
+
+| Path | Contents |
+|---|---|
+| `data/sessions.jsonl` | The sessions, one JSON object per line. This is the canonical file; `CHECKSUMS.sha256` covers it. |
+| `parquet/sessions.parquet` | The same 183 sessions rendered for the dataset viewer. `session_id`, `model`, `thinking_effort`, `n_messages` and `n_tools` are columns; `system`, `tools` and `messages` are serialized as JSON strings. |
+| `VERIFICATION.md`, `EXCLUSIONS.md`, `STATS.json` | The pre-release screening record. |
+| `consent/` | The authorization and the privacy policy, with identifying names replaced. |
+
+The viewer needs one schema per column, and two fields in the raw file do not have one:
+`system` is a list in 181 sessions and a string in 2, and its list elements carry
+`cache_control` in some sessions and not in others. Serializing those fields as JSON strings
+in the Parquet rendering keeps every byte while giving the viewer a stable schema. Round-trip
+was checked on all 183 sessions.
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+# the viewer rendering, nested fields as JSON strings
+ds = load_dataset("submit2331/ACT-2.5B", split="train")
+
+# the canonical file
+import json
+rows = [json.loads(l) for l in open("sessions.jsonl", encoding="utf-8")]
+```
+
 ## Licence
 
 ODC-BY.