| """Pure UAReviews intent adapter. No downloading, translation or training. |
| |
| The Hub exposes one physical train file containing THREE logical splits. Always |
| use source_split returned here, never the Hub container split. Keep test and |
| challenge exclusively for evaluation, including when deduplicating against train. |
| The caller must group duplicate texts across splits and give evaluation priority. |
| Human final labels are categorical judgments, not measured probabilities. Only |
| 20% of the source received the paper's three-annotator gold validation; the file |
| does not identify that subset, so no row is claimed to be independently verified. |
| """ |
|
|
| import unicodedata |
|
|
|
|
| SOURCE = { |
| "repo": "KSE-RESEARCH-Group/UAReviews", |
| "revision": "6f2ad474981e453fdf483dee1a6db060888bb212", |
| "license": "cc-by-4.0", |
| "file": "benchmark_v1_splits.jsonl", |
| "card": "https://huggingface.co/datasets/KSE-RESEARCH-Group/UAReviews/blob/" |
| "6f2ad474981e453fdf483dee1a6db060888bb212/README.md", |
| "paper": "https://aclanthology.org/2026.unlp-1.2/", |
| } |
| |
| |
| CANDIDATES = ( |
| "Gratitude / Positive Feedback", |
| "Complaint / Dissatisfaction", |
| "Question / Request for Help", |
| "Suggestion / Idea", |
| "Neutral Comment", |
| ) |
| KEYS = ("gratitude", "complaint", "question", "suggestion", "neutral") |
| SPLIT_COUNTS = {"train": 8106, "test": 1737, "challenge": 1737} |
| INSTRUCTIONS = ( |
| "Classify the primary communicative intent of this Ukrainian user review or " |
| "feedback comment using the five provided categories. Select one category." |
| ) |
|
|
|
|
| def adapt(record, expected_split=None): |
| """Return zero/one Choice task; fail closed on absent/unknown logical splits. |
| |
| expected_split optionally asserts a caller's logical partition; it must not |
| be passed as 'train' simply because HF loaded a physical train container. |
| Unknown labels are errors rather than silently reinterpreted classifications. |
| Source ratings, emotions, IDs, labels and provenance stay audit-only. |
| """ |
| if not isinstance(record, dict): |
| return [] |
| split = record.get("split") |
| if split not in SPLIT_COUNTS: |
| raise ValueError("UAReviews requires a train/test/challenge row-level split") |
| if expected_split is not None and expected_split != split: |
| raise ValueError("UAReviews row-level split differs from requested partition") |
| label = record.get("final_category") |
| if label not in CANDIDATES: |
| raise ValueError("Unknown UAReviews intent label: %r" % label) |
| content = record.get("content") |
| if not isinstance(content, str) or not content.strip(): |
| return [] |
| state = content.replace("\r\n", "\n").replace("\r", "\n") |
| group_key = " ".join(unicodedata.normalize("NFC", state).casefold().split()) |
| return [{ |
| "state": state, |
| "instructions": INSTRUCTIONS, |
| "candidates": list(CANDIDATES), |
| "keys": list(KEYS), |
| "target": [float(candidate == label) for candidate in CANDIDATES], |
| "kind": "choice", |
| "group_key": group_key, |
| "source_split": split, |
| "split": split, |
| "metadata": { |
| "source": SOURCE["repo"], |
| "source_revision": SOURCE["revision"], |
| "source_license": SOURCE["license"], |
| "source_card": SOURCE["card"], |
| "source_paper": SOURCE["paper"], |
| "source_file": SOURCE["file"], |
| "source_row_id": record.get("id"), |
| "source_split": split, |
| "evaluation_only": split != "train", |
| "source_origin": record.get("source"), |
| "language": "uk", |
| "label_method": "published_final_human_intent_annotation", |
| "target_semantics": "one_hot_categorical_annotation_not_calibrated_probability", |
| "independent_gold_subset_membership": "not_identified_in_source", |
| }, |
| }] |
|
|
