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Add all 126 datasets introduced in https://arxiv.org/abs/2410.10127.

PS This is an updated pull request, as the previous one is outdated.

Code Quality

  • Code Formatted: Format the code using make lint to maintain consistent style.

Documentation

  • Updated Documentation: Add or update documentation to reflect the changes introduced in this PR.

Testing

  • New Tests Added: Write tests to cover new functionality. Validate with make test-with-coverage.
  • x ] Tests Passed: Run tests locally using make test or make test-with-coverage to ensure no existing functionality is broken.

Adding datasets checklist

Reason for dataset addition: ...

  • I have run the following models on the task (adding the results to the pr). These can be run using the mteb -m {model_name} -t {task_name} command.
    • sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
    • intfloat/multilingual-e5-small
  • I have checked that the performance is neither trivial (both models gain close to perfect scores) nor random (both models gain close to random scores).
  • If the dataset is too big (e.g. >2048 examples), considering using self.stratified_subsampling() under dataset_transform()
  • I have filled out the metadata object in the dataset file (find documentation on it here).
  • Run tests locally to make sure nothing is broken using make test.
  • Run the formatter to format the code using make lint.

Adding a model checklist

  • I have filled out the ModelMeta object to the extent possible
  • I have ensured that my model can be loaded using
    • mteb.get_model(model_name, revision) and
    • mteb.get_model_meta(model_name, revision)
  • I have tested the implementation works on a representative set of tasks.

@sunnweiwei
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Also, add benchmarks to include these datasets

__all__ = []


TASK2SPLIT = {'Competition-Math': ['queries'], 'ProofWiki_Proof': ['queries'], 'ProofWiki_Reference': ['queries'], 'Stacks_Proof': ['queries'], 'Stacks_Reference': ['queries'], 'Stein_Proof': ['queries'], 'Stein_Reference': ['queries'], 'Trench_Proof': ['queries'], 'Trench_Reference': ['queries'], 'TAD': ['queries'], 'TAS2': ['queries'], 'StackMathQA': ['queries'], 'APPS': ['queries'], 'CodeEditSearch': ['queries'], 'CodeSearchNet': ['queries'], 'Conala': ['queries'], 'HumanEval-X': ['queries'], 'LeetCode': ['queries'], 'MBPP': ['queries'], 'RepoBench': ['queries'], 'TLDR': ['queries'], 'SWE-Bench-Lite': ['astropy__astropy_12544_queries', 'astropy__astropy_13158_queries', 'astropy__astropy_13162_queries', 'astropy__astropy_13398_queries', 'astropy__astropy_13438_queries', 'astropy__astropy_14439_queries', 'astropy__astropy_14701_queries', 'astropy__astropy_14966_queries', 'astropy__astropy_7441_queries', 'astropy__astropy_8707_queries', 'django__django_11501_queries', 'django__django_12091_queries', 'django__django_13192_queries', 'django__django_13218_queries', 'django__django_13884_queries', 'django__django_14441_queries', 'django__django_15481_queries', 'django__django_15869_queries', 'django__django_16901_queries', 'django__django_17065_queries', 'matplotlib__matplotlib_20518_queries', 'matplotlib__matplotlib_23314_queries', 'matplotlib__matplotlib_23913_queries', 'matplotlib__matplotlib_24627_queries', 'matplotlib__matplotlib_24849_queries', 'matplotlib__matplotlib_25027_queries', 'matplotlib__matplotlib_25238_queries', 'matplotlib__matplotlib_25404_queries', 'matplotlib__matplotlib_25430_queries', 'matplotlib__matplotlib_25746_queries', 'mwaskom__seaborn_2389_queries', 'mwaskom__seaborn_2576_queries', 'mwaskom__seaborn_2766_queries', 'mwaskom__seaborn_2813_queries', 'mwaskom__seaborn_2853_queries', 'mwaskom__seaborn_2946_queries', 'mwaskom__seaborn_2979_queries', 'mwaskom__seaborn_2996_queries', 'mwaskom__seaborn_3202_queries', 'mwaskom__seaborn_3407_queries', 'pallets__flask_4045_queries', 'pallets__flask_4074_queries', 'pallets__flask_4160_queries', 'pallets__flask_4169_queries', 'pallets__flask_4544_queries', 'pallets__flask_4575_queries', 'pallets__flask_4642_queries', 'pallets__flask_4992_queries', 'pallets__flask_5014_queries', 'pallets__flask_5063_queries', 'psf__requests_1537_queries', 'psf__requests_1713_queries', 'psf__requests_1733_queries', 'psf__requests_1766_queries', 'psf__requests_2193_queries', 'psf__requests_2466_queries', 'psf__requests_2821_queries', 'psf__requests_3362_queries', 'psf__requests_5414_queries', 'psf__requests_863_queries', 'pydata__xarray_4339_queries', 'pydata__xarray_4767_queries', 'pydata__xarray_4827_queries', 'pydata__xarray_4911_queries', 'pydata__xarray_4966_queries', 'pydata__xarray_5033_queries', 'pydata__xarray_5682_queries', 'pydata__xarray_6135_queries', 'pydata__xarray_6461_queries', 'pydata__xarray_7391_queries', 'pylint_dev__pylint_4398_queries', 'pylint_dev__pylint_4604_queries', 'pylint_dev__pylint_5175_queries', 'pylint_dev__pylint_5446_queries', 'pylint_dev__pylint_5613_queries', 'pylint_dev__pylint_6358_queries', 'pylint_dev__pylint_6412_queries', 'pylint_dev__pylint_6556_queries', 'pylint_dev__pylint_8281_queries', 'pylint_dev__pylint_8757_queries', 'pytest_dev__pytest_10371_queries', 'pytest_dev__pytest_11047_queries', 'pytest_dev__pytest_11148_queries', 'pytest_dev__pytest_5356_queries', 'pytest_dev__pytest_6680_queries', 'pytest_dev__pytest_7158_queries', 'pytest_dev__pytest_7352_queries', 'pytest_dev__pytest_9064_queries', 'pytest_dev__pytest_9279_queries', 'scikit_learn__scikit_learn_10198_queries', 'scikit_learn__scikit_learn_10803_queries', 'scikit_learn__scikit_learn_10949_queries', 'scikit_learn__scikit_learn_11333_queries', 'scikit_learn__scikit_learn_11635_queries', 'scikit_learn__scikit_learn_12827_queries', 'scikit_learn__scikit_learn_12834_queries', 'scikit_learn__scikit_learn_13302_queries', 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['queries'], 'WnCw': ['queries'], 'WnWi': ['queries'], 'WoW': ['queries'], 'zsRE': ['queries'], 'AILA2019-Case': ['queries'], 'AILA2019-Statutes': ['queries'], 'BSARD': ['queries'], 'BillSum': ['queries'], 'CUAD': ['GOOSEHEADINSURANCE_queries', 'GRANTIERRAENERGY_queries', 'HarpoonTherapeutics_queries', 'Monsanto_Company_queries'], 'GerDaLIR': ['queries'], 'LeCaRDv2': ['queries'], 'LegalQuAD': ['queries'], 'REGIR-EU2UK': ['queries'], 'REGIR-UK2EU': ['queries'], 'ArguAna': ['queries'], 'CQADupStack': ['CQADupStack_Android_queries', 'CQADupStack_English_queries', 'CQADupStack_Gaming_queries', 'CQADupStack_Gis_queries', 'CQADupStack_Math_queries', 'CQADupStack_Physics_queries', 'CQADupStack_Programmers_queries', 'CQADupStack_Stats_queries', 'CQADupStack_Tex_queries', 'CQADupStack_Unix_queries', 'CQADupStack_WebMasters_queries', 'CQADupStack_Wordpress_queries'], 'FiQA': ['queries'], 'NFCorpus': ['queries'], 'Quora': ['queries'], 'SciDocs': ['queries'], 'SciFact': ['queries'], 'TopiOCQA': ['queries'], 'Touche': ['queries'], 'Trec-Covid': ['queries'], 'ACORDAR': ['queries'], 'CPCD': ['queries'], 'ChroniclingAmericaQA': ['queries'], 'Monant': ['queries'], 'NTCIR': ['queries'], 'PointRec': ['queries'], 'ProCIS-Dialog': ['queries'], 'ProCIS-Turn': ['queries'], 'QuanTemp': ['queries'], 'WebTableSearch': ['queries'], 'CARE': ['queries'], 'MISeD': ['Bmr006_queries', 'Bro027_queries', 'covid4_queries', 'covid9_queries', 'education4_queries'], 'SParC': ['chinook_1_queries', 'college_2_queries', 'store_1_queries'], 'SParC-SQL': ['chinook_1_queries', 'college_2_queries', 'store_1_queries'], 'Spider': ['chinook_1_queries', 'college_2_queries', 'store_1_queries'], 'Spider-SQL': ['chinook_1_queries', 'college_2_queries', 'store_1_queries'], 'LitSearch': ['queries'], 'CAsT_2019': ['queries'], 'CAsT_2020': ['queries'], 'CAsT_2021': ['queries'], 'CAsT_2022': ['queries'], 'Core_2017': ['queries'], 'Microblog_2011': ['queries'], 'Microblog_2012': ['queries'], 'Microblog_2013': ['queries'], 'Microblog_2014': ['queries'], 'PrecisionMedicine_2017': ['queries'], 'PrecisionMedicine_2018': ['queries'], 'PrecisionMedicine_2019': ['queries'], 'PrecisionMedicine-Article_2019': ['queries'], 'PrecisionMedicine-Article_2020': ['queries'], 'CliniDS_2014': ['queries'], 'CliniDS_2015': ['queries'], 'CliniDS_2016': ['queries'], 'ClinicalTrials_2021': ['queries'], 'ClinicalTrials_2022': ['queries'], 'ClinicalTrials_2023': ['queries'], 'DD_2015': ['queries'], 'DD_2016': ['queries'], 'DD_2017': ['queries'], 'FairRanking_2020': ['queries'], 'FairRanking_2021': ['queries'], 'FairRanking_2022': ['queries'], 'Genomics-AdHoc_2004': ['queries'], 'Genomics-AdHoc_2005': ['queries'], 'Genomics-AdHoc_2006': ['queries'], 'Genomics-AdHoc_2007': ['queries'], 'TREC-Legal_2011': ['queries'], 'NeuCLIR-Tech_2023': ['queries'], 'NeuCLIR_2022': ['queries'], 'NeuCLIR_2023': ['queries'], 'ProductSearch_2023': ['queries'], 'ToT_2023': ['queries'], 'ToT_2024': ['queries'], 'FoodAPI': ['queries'], 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In mteb we've already have apps and CodeEditSearch, NFcorpus. Can you compare your tasks with existing? Also, I think these dayasets have different citations and you should include them

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