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"dataset_id": "ELBennett/big-decisions",
"schema_version": "3.0.0",
"description": "A tokenizer-neutral collection of decision examples in a unified fixed-choice format. NOUL and SCORE decisions are represented as ordinary choices; option text, soft targets, ordinal ranks, source attribution, and split boundaries are retained.",
"format": "Apache Parquet with BEV envelope fields state, questions_json, domain, question_types, question_count, plus source/split provenance fields. In v3, questions_json uses criteria as a list of option strings, label as the selected option string, optional label_probs as an aligned list of weights, and optional choice_ranks as an aligned list of integer ranks.",
"training_note": "Tokenize state and choice text with the training model's tokenizer.",
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"cruxeval": 720,
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},
"sources": [
{
"id": "bev_decision",
"title": "BEV Decision Mix",
"group": "Decision-making",
"origin": "avbiswas/bev-decision",
"covers": "Bundled multi-domain choice, yes/no, and ordinal decisions.",
"notes": "",
"availability": "included",
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"test": 79889
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},
{
"id": "domain_decisions",
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"group": "Policy",
"origin": "altslate/certo-decisions-v2:domain_generator",
"covers": "Synthetic domain policy / approval decisions.",
"notes": "",
"availability": "included",
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"examples": 122438,
"packed": true,
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},
{
"id": "go_emotions",
"title": "GoEmotions",
"group": "Emotion classification",
"origin": "google-research-datasets/go_emotions",
"covers": "Fine-grained emotion classification over Reddit comments.",
"notes": "",
"availability": "included",
"examples_by_split": {
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"examples": 428892,
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},
{
"id": "executable_policies",
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"covers": "Synthetic executable policy compliance decisions.",
"notes": "",
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{
"id": "multinli",
"title": "MultiNLI",
"group": "Semantics and inference",
"origin": "nyu-mll/multi_nli",
"covers": "Three-way textual entailment (premise/hypothesis).",
"notes": "Related to index ANLI, but MultiNLI is the Certo source.",
"availability": "included",
"examples_by_split": {
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},
"examples": 239992,
"packed": true,
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},
{
"id": "dbpedia14",
"title": "DBpedia-14",
"group": "Topic classification",
"origin": "fancyzhx/dbpedia_14",
"covers": "14-way topic classification of Wikipedia abstracts.",
"notes": "",
"availability": "included",
"examples_by_split": {
"train": 91879,
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},
"examples": 99988,
"packed": true,
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},
{
"id": "known_posterior",
"title": "Known posterior",
"group": "Probability and belief",
"origin": "altslate/certo-decisions-v2:genworld",
"covers": "Synthetic calibrated posterior / belief-update decisions.",
"notes": "",
"availability": "included",
"examples_by_split": {
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},
"examples": 120000,
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},
{
"id": "clinc150",
"title": "CLINC150+OOS",
"group": "Intent classification",
"origin": "clinc/clinc_oos",
"covers": "150 banking/assistant intents plus out-of-scope.",
"notes": "",
"availability": "included",
"examples_by_split": {
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"dev": 2251
},
"examples": 28266,
"packed": true,
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},
{
"id": "boolq",
"title": "BoolQ",
"group": "Reading comprehension",
"origin": "google/boolq",
"covers": "Yes/no reading comprehension over Wikipedia passages.",
"notes": "",
"availability": "included",
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},
{
"id": "arc_easy",
"title": "ARC-Easy",
"group": "Exam",
"origin": "allenai/ai2_arc:ARC-Easy",
"covers": "Grade-school science multiple choice (easy split).",
"notes": "",
"availability": "included",
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{
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{
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{
"id": "winogrande",
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{
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{
"id": "forecastbench",
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{
"id": "finentity",
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{
"id": "isarcasm",
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"id": "api_bank",
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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