Add dataset files and documentation

Add train/validation/test CSV splits (400 examples total, 200 per class), dataset card (README.md), and metadata (dataset_info.json).

Binary classification dataset derived from ibm-research/watsonxDocsQA.
Labels: conceptual (0), how-to (1). Split: 70/15/15, stratified.

README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- en
+license:
+- other
+pretty_name: Watsonx Docs Document Type Classification
+size_categories:
+- n<1K
+source_datasets:
+- ibm-research/watsonxDocsQA
+task_categories:
+- text-classification
+task_ids:
+- document-classification
+---
+
+# Watsonx Docs Document Type Classification
+
+This dataset is a balanced binary document-level classification subset derived
+from `ibm-research/watsonxDocsQA`.
+
+## Task
+
+Classify IBM Watsonx documentation pages by their dominant user-facing purpose:
+
+- `conceptual`: documents primarily used to understand or look up information.
+- `how-to`: documents primarily used to complete a procedure or fix a problem.
+
+## Splits
+
+| Split | conceptual | how-to | Total |
+|---|---:|---:|---:|
+| train | 140 | 140 | 280 |
+| validation | 30 | 30 | 60 |
+| test | 30 | 30 | 60 |
+
+## Fields
+
+- `doc_id`: original document ID from the source dataset.
+- `url`: source documentation URL.
+- `title`: documentation page title.
+- `text`: model input text, constructed as `title + "\n" + first 800 words of document`. The title is preserved in full; the document body is truncated to keep inputs manageable for embedding-based classifiers.
+- `label`: string label, either `conceptual` or `how-to`.
+- `label_id`: numeric label ID, where `conceptual = 0` and `how-to = 1`.
+- `split`: dataset split.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+data_files = {
+    "train": "train.csv",
+    "validation": "validation.csv",
+    "test": "test.csv",
+}
+
+dataset = load_dataset("csv", data_files=data_files)
+```
+
+## Curation Notes
+
+IBM technical documentation has traditionally been structured around DITA
+(Darwin Information Typing Architecture), which classifies documents into four
+types: `task`, `concept`, `reference`, and `troubleshooting`. This dataset
+adapts that taxonomy into two classes: `conceptual` merges `concept` and
+`reference` (both primarily information-seeking); `how-to` merges `task` and
+`troubleshooting` (both action- or fix-oriented). The binary schema was chosen
+because `troubleshooting` was too rare to form a reliable standalone class, and
+`reference` and `concept` were difficult to separate consistently.
+
+Annotation followed a semi-automatic process. Labelling rules were first defined
+based on IBM Writing Style guidelines, then applied by a heuristic script to
+generate candidate labels. Each candidate was assigned a confidence tier:
+`title_high` (strong title signal), `body_medium` (body-text signal only, no
+strong title match), or `default_low` (no strong signal in either title or
+body). All tiers except `body_medium` how-to rows were manually reviewed. The
+`body_medium` how-to subset (333 rows) was left unreviewed because the remaining
+manually checked data was sufficient to construct a balanced 400-example
+dataset; retaining unreviewed borderline rows would have introduced noise
+without benefit.
+
+Rows marked `X` during manual review were removed because the source document
+was incomplete or too ambiguous to label reliably. Rows marked `?` were
+interpreted as belonging to the opposite binary class.
+
+The final subset contains 400 examples, sampled with random seed `42` after
+manual correction and filtering.
+
+License follows the terms of the source dataset `ibm-research/watsonxDocsQA`.
+Please refer to the original dataset for licensing details.

dataset_info.json

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+{
+  "dataset_name": "watsonx-docs-document-type",
+  "version": "1.0.0",
+  "date_created": "2026-05",
+  "source_dataset": "ibm-research/watsonxDocsQA",
+  "task": "binary document-level technical documentation type classification",
+  "labels": {
+    "conceptual": 0,
+    "how-to": 1
+  },
+  "text_field": "text",
+  "text_construction": "title + '\\n' + first 800 words of document",
+  "label_field": "label",
+  "label_id_field": "label_id",
+  "random_seed": 42,
+  "source_rows": 1144,
+  "annotation_process": {
+    "step_1": "heuristic pre-annotation using title and body patterns based on IBM Writing Style guidelines",
+    "step_2": "human review of all rows except suggested_how_to_body_medium subset",
+    "confidence_tiers": {
+      "title_high": "strong title signal",
+      "body_medium": "body-text signal only, no strong title match",
+      "default_low": "no strong signal in either title or body"
+    },
+    "manual_markers": {
+      "empty": "accept heuristic label",
+      "?": "flip to opposite binary class",
+      "X": "remove document"
+    }
+  },
+  "excluded": {
+    "suggested_how_to_body_medium": {
+      "count": 333,
+      "reason": "not manually reviewed; weak body-only heuristic signal considered unreliable"
+    },
+    "final_label_X": {
+      "count": 17,
+      "reason": "document was incomplete, low-quality, or too ambiguous to label reliably"
+    }
+  },
+  "usable_after_exclusion": {
+    "how-to": 231,
+    "conceptual": 563
+  },
+  "selected": {
+    "conceptual": 200,
+    "how-to": 200
+  },
+  "splits": {
+    "train": {
+      "conceptual": 140,
+      "how-to": 140,
+      "total": 280
+    },
+    "validation": {
+      "conceptual": 30,
+      "how-to": 30,
+      "total": 60
+    },
+    "test": {
+      "conceptual": 30,
+      "how-to": 30,
+      "total": 60
+    }
+  }
+}