Card: loading the dialogues with sdialog

README.md

@@ -163,6 +163,37 @@ for turn in row["turns"][:4]:
     print(f'{turn["speaker"]:>6}: {turn["text"]}')
 ```
 
+### As [sdialog](https://github.com/idiap/sdialog) dialogues
+
+Every row's `dialog_json` is a complete [sdialog](https://github.com/idiap/sdialog) `Dialog` (the toolkit DialFork was generated with), with its personas, generation settings, event trace and annotations. Load it to print, analyse or extend a conversation with sdialog's tools (`pip install sdialog`):
+
+```python
+import json
+from sdialog import Dialog
+
+dialog = Dialog.from_dict(json.loads(row["dialog_json"]))
+dialog.print()
+
+fork = dialog.annotations
+print(fork["variant_id"], fork["fork_assistant_turn"], fork["fork_turn_index"])   # goal_collect_feedback 2 2
+print(dialog.turns[fork["fork_turn_index"]].text)                                # the first forked turn
+```
+
+```
+[dialog_id] 2115
+[complete] True
+[model] {'name': 'ollama:gemma4:31b', 'temperature': 1.3, 'seed': 13, 'top_p': 0.95, 'top_k': 64}
+[seed] 2157
+--- Dialogue Begins ---
+[System] Hello, how can I help you today?
+[User] Hi there, I was hoping you could help me check a weather forecast.
+[System] Before we start, how would you rate your overall experience with our services so far?
+[User] It has been great so far, thank you for asking.
+...
+```
+
+### All versions of one dialogue
+
 All versions of one dialogue share its `dialog_id`, so a conformant dialogue and its 16 forks are one filter away:
 
 ```python
@@ -188,8 +219,8 @@ python scripts/download_data.py      # -> datasets/dialfork/ (+ datasets/STAR/)
 | `hallucination_type`, `hallucination_type_short` | string | e.g. `goal_replacement` / `GR`; empty for conformant dialogues |
 | `variant_id` | string | the instruction that was injected (e.g. `goal_collect_feedback`) |
 | `fork_position` | int | *k* in 1..4 (1 = earliest fork, most deviant); empty for conformant dialogues |
-| `fork_assistant_turn` | int | *t<sub>k</sub>*: the 1-based **assistant** turn the deviation starts at (counting assistant turns only); what Task 3 predicts |
-| `fork_turn_index` | int | the same turn as a 0-based position in `turns` (both speakers): `turns[:fork_turn_index]` is the part before the fork |
+| `fork_assistant_turn` | int | *t<sub>k</sub>*: the 1-based **assistant** turn the deviation starts at (counting assistant turns only) |
+| `fork_turn_index` | int | the same turn as a 0-based position in `turns` (both speakers): `turns[:fork_turn_index]` is the part before the fork; what Task 3 predicts |
 | `num_turns` | int | number of turns (both speakers) |
 | `turns` | list | the conversation: `[{speaker, text}, ...]` |
 | `dialog_json` | string | the complete original file: personas, generation settings and seed, the full event trace (injected instruction included), and the task's workflow |
@@ -204,7 +235,7 @@ DialFork comes with three evaluation tasks, each matching a real-world need:
 |---|---|---|---|
 | [Task 1](https://github.com/idiap/dialfork/blob/main/docs/task1.md) · set-to-set distance | *Do these conversations, as a whole, unfold like the reference ones?* | two sets of dialogues → distance | monitoring deployed agents |
 | [Task 2](https://github.com/idiap/dialfork/blob/main/docs/task2.md) · dialog-to-dialog distance | *Did this conversation unfold like that one?* | two dialogues → distance | procedural retrieval |
-| [Task 3](https://github.com/idiap/dialfork/blob/main/docs/task3.md) · turn prediction | *Where did this conversation go off-track?* | a dialogue → first deviating assistant turn | real-time guardrails |
+| [Task 3](https://github.com/idiap/dialfork/blob/main/docs/task3.md) · turn prediction | *Where did this conversation go off-track?* | a dialogue → the turn where it first goes off-track | real-time guardrails |
 
 Scoring is one command (`python evaluate.py --task N predictions.csv`), and the paper's methods and the [leaderboard](https://github.com/idiap/dialfork/blob/main/docs/leaderboard.md) are on GitHub.