import logging import pytest from mteb.abstasks.TaskMetadata import TaskMetadata from mteb.overview import get_tasks # Historic datasets without filled metadata. Do NOT add new datasets to this list. _HISTORIC_DATASETS = [ "AmazonReviewsClassification", "MasakhaNEWSClassification", "MassiveIntentClassification", "MassiveScenarioClassification", "MTOPDomainClassification", "MTOPIntentClassification", "NordicLangClassification", "ScalaClassification", "NoRecClassification", "NorwegianParliamentClassification", "PunjabiNewsClassification", "CBD", "PolEmo2.0-IN", "PolEmo2.0-OUT", "AllegroReviews", "PAC", "SweRecClassification", "TNews", "IFlyTek", "MultilingualSentiment", "JDReview", "OnlineShopping", "Waimai", "BlurbsClusteringP2P", "BlurbsClusteringS2S", "TenKGnadClusteringP2P", "TenKGnadClusteringS2S", "ArxivClusteringP2P", "ArxivClusteringS2S", "BigPatentClustering", "BiorxivClusteringP2P", "BiorxivClusteringS2S", "MedrxivClusteringP2P", "MedrxivClusteringS2S", "RedditClustering", "RedditClusteringP2P", "StackExchangeClustering", "StackExchangeClusteringP2P", "TwentyNewsgroupsClustering", "WikiCitiesClustering", "AlloProfClusteringP2P", "AlloProfClusteringS2S", "HALClusteringS2S", "MLSUMClusteringP2P", "MLSUMClusteringS2S", "MasakhaNEWSClusteringP2P", "MasakhaNEWSClusteringS2S", "SNLClustering", "VGClustering", "EightTagsClustering", "RomaniBibleClustering", "FloresClusteringS2S", "SpanishNewsClusteringP2P", "SwednClustering", "CLSClusteringS2S", "CLSClusteringP2P", "ThuNewsClusteringS2S", "ThuNewsClusteringP2P", "TV2Nordretrieval", "TwitterHjerneRetrieval", "GerDaLIR", "GerDaLIRSmall", "GermanDPR", "GermanQuAD-Retrieval", "LegalQuAD", "AILACasedocs", "AILAStatutes", "ArguAna", "ClimateFEVER", "CQADupstackAndroidRetrieval", "CQADupstackEnglishRetrieval", "CQADupstackGamingRetrieval", "CQADupstackGisRetrieval", "CQADupstackMathematicaRetrieval", "CQADupstackPhysicsRetrieval", "CQADupstackProgrammersRetrieval", "CQADupstackStatsRetrieval", "CQADupstackTexRetrieval", "CQADupstackUnixRetrieval", "CQADupstackWebmastersRetrieval", "CQADupstackWordpressRetrieval", "DBPedia", "FEVER", "FiQA2018", "HagridRetrieval", "HotpotQA", "LegalBenchConsumerContractsQA", "LegalBenchCorporateLobbying", "LegalSummarization", "LEMBNeedleRetrieval", "LEMBPasskeyRetrieval", "MSMARCO", "MSMARCOv2", "NarrativeQARetrieval", "NFCorpus", "NQ", "QuoraRetrieval", "SCIDOCS", "SciFact", "Touche2020", "TRECCOVID", "AlloprofRetrieval", "BSARDRetrieval", "SyntecRetrieval", "JaQuADRetrieval", "Ko-miracl", "Ko-StrategyQA", "MintakaRetrieval", "MIRACLRetrieval", "MultiLongDocRetrieval", "XMarket", "SNLRetrieval", "ArguAna-PL", "DBPedia-PL", "FiQA-PL", "HotpotQA-PL", "MSMARCO-PL", "NFCorpus-PL", "NQ-PL", "Quora-PL", "SCIDOCS-PL", "SciFact-PL", "TRECCOVID-PL", "SpanishPassageRetrievalS2P", "SpanishPassageRetrievalS2S", "SweFaqRetrieval", "T2Retrieval", "MMarcoRetrieval", "DuRetrieval", "CovidRetrieval", "CmedqaRetrieval", "EcomRetrieval", "MedicalRetrieval", "VideoRetrieval", "LeCaRDv2", "SprintDuplicateQuestions", "TwitterSemEval2015", "TwitterURLCorpus", "OpusparcusPC", "PawsX", "SICK-E-PL", "PpcPC", "CDSC-E", "PSC", "Ocnli", "Cmnli", "AskUbuntuDupQuestions", "MindSmallReranking", "SciDocsRR", "StackOverflowDupQuestions", "AlloprofReranking", "SyntecReranking", "MIRACLReranking", "T2Reranking", "MMarcoReranking", "CMedQAv1-reranking", "CMedQAv2-reranking", "GermanSTSBenchmark", "BIOSSES", "SICK-R", "STS12", "STS13", "STS14", "STS15", "STS16", "STSBenchmark", "FinParaSTS", "SICKFr", "KLUE-STS", "KorSTS", "STS17", "STS22", "STSBenchmarkMultilingualSTS", "SICK-R-PL", "CDSC-R", "RonSTS", "STSES", "ATEC", "BQ", "LCQMC", "PAWSX", "STSB", "AFQMC", "QBQTC", "SummEval", "SummEvalFr", "ArxivClusteringP2P.v2", "SwednClusteringP2P", "SwednClusteringS2S", "MalayalamNewsClassification", "TamilNewsClassification", "ArxivClusteringP2P.v3", "TenKGnadClusteringP2P.v2", "TenKGnadClusteringS2S.v2", ] def test_given_dataset_config_then_it_is_valid(): my_task = TaskMetadata( name="MyTask", dataset={ "path": "test/dataset", "revision": "1.0", }, description="testing", reference=None, type="Classification", category="s2s", eval_splits=["test"], eval_langs=["eng-Latn"], main_score="map", date=None, form=None, domains=None, license=None, task_subtypes=None, socioeconomic_status=None, annotations_creators=None, dialect=None, text_creation=None, bibtex_citation="", avg_character_length=None, n_samples=None, ) assert my_task.dataset["path"] == "test/dataset" assert my_task.dataset["revision"] == "1.0" def test_given_missing_dataset_path_then_it_throws(): with pytest.raises(ValueError): TaskMetadata( name="MyTask", description="testing", reference=None, type="Classification", category="s2s", eval_splits=["test"], eval_langs=["eng-Latn"], main_score="map", date=None, form=None, domains=None, license=None, task_subtypes=None, socioeconomic_status=None, annotations_creators=None, dialect=None, text_creation=None, bibtex_citation="", avg_character_length=None, n_samples=None, ) def test_given_missing_revision_path_then_it_throws(): with pytest.raises(ValueError): TaskMetadata( name="MyTask", dataset={ "path": "test/dataset", }, description="testing", reference=None, type="Classification", category="s2s", eval_splits=["test"], eval_langs=["eng-Latn"], main_score="map", date=None, form=None, domains=None, license=None, task_subtypes=None, socioeconomic_status=None, annotations_creators=None, dialect=None, text_creation=None, bibtex_citation="", avg_character_length=None, n_samples=None, ) def test_given_none_revision_path_then_it_logs_warning(caplog): with caplog.at_level(logging.WARNING): my_task = TaskMetadata( name="MyTask", dataset={"path": "test/dataset", "revision": None}, description="testing", reference=None, type="Classification", category="s2s", eval_splits=["test"], eval_langs=["eng-Latn"], main_score="map", date=None, form=None, domains=None, license=None, task_subtypes=None, socioeconomic_status=None, annotations_creators=None, dialect=None, text_creation=None, bibtex_citation="", avg_character_length=None, n_samples=None, ) assert my_task.dataset["revision"] is None warning_logs = [ record for record in caplog.records if record.levelname == "WARNING" ] assert len(warning_logs) == 1 assert ( warning_logs[0].message == "Revision missing for the dataset test/dataset. " "It is encourage to specify a dataset revision for reproducability." ) def test_unfilled_metadata_is_not_filled(): assert ( TaskMetadata( name="MyTask", dataset={ "path": "test/dataset", "revision": "1.0", }, description="testing", reference=None, type="Classification", category="s2s", eval_splits=["test"], eval_langs=["eng-Latn"], main_score="map", date=None, form=None, domains=None, license=None, task_subtypes=None, socioeconomic_status=None, annotations_creators=None, dialect=None, text_creation=None, bibtex_citation="", avg_character_length=None, n_samples=None, ).is_filled() is False ) def test_filled_metadata_is_filled(): assert ( TaskMetadata( name="MyTask", dataset={ "path": "test/dataset", "revision": "1.0", }, description="testing", reference="https://aclanthology.org/W19-6138/", type="Classification", category="s2s", eval_splits=["test"], eval_langs=["eng-Latn"], main_score="map", date=("2021-01-01", "2021-12-31"), form=["written"], domains=["Non-fiction"], license="mit", task_subtypes=["Thematic clustering"], socioeconomic_status="high", annotations_creators="expert-annotated", dialect=[], text_creation="found", bibtex_citation="Someone et al", avg_character_length={"train": 1}, n_samples={"train": 1}, ).is_filled() is True ) def test_all_metadata_is_filled(): all_tasks = get_tasks() unfilled_metadata = [] for task in all_tasks: if task.metadata.name not in _HISTORIC_DATASETS: if not task.metadata.is_filled(): unfilled_metadata.append(task.metadata.name) if unfilled_metadata: raise ValueError( f"The metadata of the following datasets is not filled: {unfilled_metadata}" )