Add dataset card

README.md

@@ -1,3 +1,113 @@
 ---
 license: apache-2.0
+language:
+- en
+task_categories:
+- question-answering
+- table-question-answering
+tags:
+- time-series
+- natural-language-queries
+- benchmark
+- training-set
+size_categories:
+- n<1K
+pretty_name: NLQTSBench-train
 ---
+
+# NLQTSBench-train
+
+Training-split companion to
+[`mrtan/NLQTSBench`](https://huggingface.co/datasets/mrtan/NLQTSBench).
+220 natural-language-query / time-series tasks sampled from the same
+candidate pool as the NLQTSBench test set, then filtered to be **disjoint
+by `id`** from the test split.
+
+Intended use: cold-start training of methodology skills for the
+[Sonar-TS](https://github.com/...) framework. **Do not** use this split
+for benchmarking — it overlaps the test set's *task templates* even
+though no individual task `id` appears in both.
+
+## Files
+
+```
+NLQTSBench-train/
+├── tasks.json          220 task records (one JSON list)
+└── ts_data/
+    └── <id>.csv        220 raw time-series CSVs (one per task)
+```
+
+## Schema
+
+Each entry in `tasks.json` has these fields:
+
+| Field          | Type           | Description                                                                 |
+|----------------|----------------|-----------------------------------------------------------------------------|
+| `id`           | str            | Unique task id, e.g. `L1_T1_Global_Aggregation_00014`.                      |
+| `level`        | int (1–4)      | Reasoning level (paper Table 2).                                            |
+| `level_name`   | str            | Human-readable level name (`Basic Operations`, …).                          |
+| `category`     | str            | Higher-level category (`Atomic Retrieval`, `Pattern Recognition`, …).       |
+| `subtask`      | str            | One of 10 sub-task labels (see counts below — Composite Trend is absent).   |
+| `question`     | str            | Natural-language query, includes the expected answer format.                |
+| `answer`       | str            | Ground-truth answer as a string (display form).                             |
+| `ground_truth` | varies         | Ground-truth value in its native Python type (number / list / dict).        |
+| `eval_metric`  | str            | One of `rel_acc`, `hit`, `iou`, `set_f1`, `report`.                         |
+| `channel`      | str / list     | Channel name(s) referenced by the question.                                 |
+| `ts_data_path` | str            | Relative path to the matching CSV under `ts_data/`.                         |
+| `meta`         | dict           | Generation metadata (template args, source dataset).                        |
+
+Each `ts_data/<id>.csv` is a wide table whose first column is `timestamp`
+and remaining columns are numeric channels.
+
+## Sub-task distribution
+
+| Sub-task                | N   |
+|-------------------------|-----|
+| Sliding Window          | 38  |
+| Interval Discovery      | 33  |
+| Temporal Localization   | 31  |
+| Global Aggregation      | 28  |
+| Causal Anomaly          | 18  |
+| Subsequence Matching    | 16  |
+| Contextual Anomaly      | 14  |
+| Insight Synthesis       | 14  |
+| Periodicity Detection   | 14  |
+| Shape Identification    | 14  |
+| **Composite Trend**     | **0** |
+| **Total**               | **220** |
+
+**Composite Trend is intentionally absent**: its candidate pool was
+fully consumed by the test split with no disjoint tasks left over.
+
+## Disjointness with the test split
+
+Tasks were sampled with a distinct seed and then any `id` that also
+appears in [`mrtan/NLQTSBench`](https://huggingface.co/datasets/mrtan/NLQTSBench)
+was dropped. The remaining 220 task `id`s do **not** overlap with the
+test set.
+
+## Loading
+
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(repo_id="mrtan/NLQTSBench-train",
+                  repo_type="dataset",
+                  local_dir="NLQTSBench-train")
+
+import json, pandas as pd
+tasks = json.load(open("NLQTSBench-train/tasks.json"))
+df    = pd.read_csv(f"NLQTSBench-train/{tasks[0]['ts_data_path']}")
+```
+
+For the Sonar-TS framework, just run:
+
+```bash
+python -m cold_start.download_train_data
+```
+
+which pulls this dataset and builds per-task SQLite + SAX feature tables
+locally under `cold_start/train_data/`.
+
+## License
+
+Apache-2.0, matching the test-split licence.