| import logging |
|
|
| import pytest |
|
|
| from mteb.abstasks.TaskMetadata import TaskMetadata |
| from mteb.overview import get_tasks |
|
|
| |
| _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}" |
| ) |
|
|
