catalogue/__init__.py,sha256=bkxVOM9UARk881FSGMlLwCEfJVBw4t2PymODsxDbMfM,104
catalogue/dataset_loaders/__init__.py,sha256=Ajf6qWgMxtotTS7Mkv6WMeOogHxLG8S89EFaxtaaF5M,506
catalogue/dataset_loaders/auto_csv.py,sha256=KN4nUrPBpcm2EkA43KYppKKkCV0K-g9WTAVs1b0_UFs,5154
catalogue/dataset_loaders/custom_csv.py,sha256=voCrkA_95JCAcGsVfGClt5J_R6oa-Kxb8crUePrqNyA,6211
catalogue/dataset_loaders/data_csv_rankings.py,sha256=-NFUrGnTHMQmrUrIA4chIaX3GAljDyP_IkJrB8htQSw,1746
catalogue/dataset_loaders/data_researchers.py,sha256=cVEuo3GGIo3Iq_8Mr5JjhwdRJc6UmNkRuntn22UN5zU,5346
catalogue/dataset_loaders/graph.py,sha256=bDhPLhAETMWqFYz3sFyQf8RCRyKvfD6fOdPJ2ce861A,600
catalogue/dataset_loaders/image_pairs.py,sha256=M4_CndPhptEtCRQzvAUwveyjjtiZrcYsFTq91SWMF1o,2580
catalogue/dataset_loaders/images.py,sha256=DpyI9apGS4fNNaoHvZugTab-lgi9XhOi-ALqCPUT5Oo,2169
catalogue/dataset_loaders/uci_csv.py,sha256=BJaeBuguig25yk5uRMqUkfJbfphdsGLqpWdyIGgazRA,2377
catalogue/metrics/__init__.py,sha256=jOe1z-qaPWb6H_H72lIMNwSA7KleomhQEfegocnRMEA,658
catalogue/metrics/augmentation_report.py,sha256=D7DBvmnK61UOpwJ0mEaDh56XcK2JK8KrrqfA5A2hPpM,31863
catalogue/metrics/bias_scan.py,sha256=DZTz5wtFXMG2ahniwvirNLykWzlm9l7Prm6hsZuKGRA,3943
catalogue/metrics/image_bias_analysis.py,sha256=vQ_ZzTGwQPwhIhJB060wDNdaMSi7FeRrKQD94_fUH7s,5996
catalogue/metrics/interactive_report.py,sha256=aGaReivaFJEqjXeObe0VWje1y56R_0qISbn-_S4yAJE,2825
catalogue/metrics/interactive_sklearn_report.py,sha256=ONqRWJs4VBpvShzh48qQeBkyqnup3ktGY6F-ewCULM4,11037
catalogue/metrics/model_card.py,sha256=x0QyUxZ90A0WZLL3D1pGAxqr3qFMaqN0S-Xsn0OGcbU,9139
catalogue/metrics/multi_objective_report.py,sha256=zqK66u37PdIGdmcNJV_4V7Ik-YVyALctNW0REkbaegw,6178
catalogue/metrics/networks_layouts.py,sha256=zuagvDdTNbTXQJmGglFXaAgDN5y_KEoH51q3xH41iVI,10272
catalogue/metrics/optimal_transport.py,sha256=Z_4i8AOqHl1dvoBpimmlVSJaf9_0RYIpMpT-CeGL0NM,6166
catalogue/metrics/ranking_fairness.py,sha256=rkrxladkRcDSPikM627HDTrDuzzxj58j6NgF7RwMbok,27640
catalogue/metrics/xai_analysis.py,sha256=X8diPfMStqJbX4PqG8j1niwtdIGS2si1SPQION9VSrQ,4276
catalogue/metrics/xai_analysis_embeddings.py,sha256=GHv-dOPXwMBRN7weyuTBzgOP_aui8uIc5PIG9fuUpxE,4406
catalogue/model_loaders/__init__.py,sha256=S5sMFsxx5WNtpvLewdFxhWSOjSkxyhOtaamS3qAqisQ,464
catalogue/model_loaders/compute_rankings.py,sha256=ndGylnEGen05oqDh0m8hDOpPDDxovRTWy1Z42fteV-k,687
catalogue/model_loaders/compute_researcher_ranking.py,sha256=zC9HgIQ6KPGDgEXtE6dgrfyOFpjtlYLQQlrOcPbdBks,9207
catalogue/model_loaders/fair_node_ranking.py,sha256=z2qSCL6Xt5ISJ74tbpCncXiJBrGbcuebGzgbxaMMjbg,2579
catalogue/model_loaders/no_model.py,sha256=STPodYK9hrt77s1tcazCSWeZ91i2MkMUsEG7t0MWN2E,287
catalogue/model_loaders/onnx.py,sha256=4dAy4svckWMzx1F-TDZwTldDqa6bpd4i2CL3HaIFXV4,1963
catalogue/model_loaders/onnx_ensemble.py,sha256=J8SHr-LL9xdT2UIE64Unv97VdRzEbKakh5RiEWvVqqM,3245
catalogue/model_loaders/pytorch.py,sha256=RQgE2bYuN1SM9tiOatFUzDdnD5C24oKzKBJW-WTXGRg,1781
catalogue/model_loaders/pytorch2onnx.py,sha256=vi4IUUD2UzYmjtKfB8AqqcNjhp2CAXiVxRkonXDJjgY,2302
mammoth/__init__.py,sha256=1xB0laPe4Gq_kHtEvedqpOYHc-eXt7vfIl-jWU0Cbow,214
mammoth/custom_kfp.py,sha256=nqOnJVAl6zcSNQqdOSzfdR3MNyPvVYb9unibtl1L4oI,3533
mammoth/externals.py,sha256=EkQ6XBMpjtyuBnPTzvpSWDX3wMnn8Te3JTGeAyMZr2A,1564
mammoth/integration.py,sha256=-cQMYzajBB-pVZLkn6rIPPrvRs9JWRyXv1O7270LaNA,12402
mammoth/integration_callback.py,sha256=8CYexCgQREBW2fQY2Ouex2cO-paAToWr339jJftRCKg,725
mammoth/testing.py,sha256=cGNLT4mJA2LeI5Vqdp1N4pMtcY1Vo2-AwuEgTbjxV2U,939
mammoth/datasets/__init__.py,sha256=jEhuaXWtPO2D2wAhJG4ekPOIMlBeQSD7EfazbBM0B5o,288
mammoth/datasets/csv.py,sha256=c7v6Pt7872GoMJtUYK61KC8BKk-NOTTGt8WBkPqY74Q,1435
mammoth/datasets/dataset.py,sha256=m16q2UZCPFeinvgYQzgGL5oB4VM8gla4Wf9qhy5A3kQ,202
mammoth/datasets/graph.py,sha256=epPRXcZW_9e19Vcj9M2DUZsydxfJjoJu4e5FHhNe1ew,635
mammoth/datasets/graph_csh.py,sha256=d5BLJ4BBEej4NCU4zw0TiNgigicz6xUjQN3Jc2-e460,4140
mammoth/datasets/image.py,sha256=wDC8EbbwoP5zvBK5HCYVcMtY85XTF5x1a6tfymvAykQ,2651
mammoth/datasets/image_pairs.py,sha256=t90RcgGb5Jd2xj0URcaoGMe_oh8Q-N-4-VlU5ZjKmh8,2793
mammoth/datasets/backend/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
mammoth/datasets/backend/onnx_implementations.py,sha256=LV6bHFCM3MN6BHcuLfOoW-z_ifP2dBRcioRejAr5Otg,7599
mammoth/datasets/backend/onnx_transforms.py,sha256=4EDNcBsXzUL1ijtJnmSNXPMvik_seZ8_xHaIVHtdjTI,1705
mammoth/datasets/backend/torch_implementations.py,sha256=MfXpT78gYWxc3FJb72gWzG-QY0TKgnUGLLoabGl1ies,3664
mammoth/exports/HTML.py,sha256=Vc1ONIyKLTmeUYXipnNu_PYI6Rsa3Z6m6fpe7YlJzck,2962
mammoth/exports/__init__.py,sha256=go1QqOKQqA1SF9F6gtTRy8rCtdaSLRbNdVkEHHKhQJQ,107
mammoth/exports/markdown.py,sha256=J27srGoKdHxZs1z-rnp3N0CXW5li3yNC4IV9Ffef9fY,2961
mammoth/models/__init__.py,sha256=SWWpbA2Ztg0OHzMbm5TxeoYZmB03ih1Gq7jTwIihwg4,403
mammoth/models/empty.py,sha256=TLQ1X-0K7GzAol5-slYsYpeTeH3N74n8_-h10cXtNxI,217
mammoth/models/model.py,sha256=K5qdhpZVar-P_bYyvPvwiRa-oQQvhxmnygWPI97aI7s,43
mammoth/models/node_ranking.py,sha256=bMzoUF59djANM0AZha9CxhhXzJg1VZUzTNE39d9qRmQ,862
mammoth/models/onnx.py,sha256=fMT2b4aFSXuEsTct78KYp_8Uk2r7USHsrRBikaDK0Xk,1420
mammoth/models/onnx_ensemble.py,sha256=m5GZrMYbDvlc1e3YdIKgGkDm1EBcpxl9ElTG1_D3JcA,1716
mammoth/models/predictor.py,sha256=CkcHZpkCO_-KlGk3HWWIjs2FF2_kH45VKrw-iipQn9s,226
mammoth/models/pytorch.py,sha256=4qegtkPjbvYpw6nBLc_YInMBsHv9nGU6XW3R0CHIxwA,3365
mammoth/models/pytorch2onnx.py,sha256=WvvuWGVYXLaL3zbN92E0_iZZaVf9Tv8I6afu8zms-EI,2677
mammoth/models/researcher_ranking.py,sha256=TEslXYVS0y_eYsaL8JqJvDzh812PT-PRJsWwGk1Nja8,431
mammoth_commons-0.0.49.dist-info/LICENSE,sha256=yj1M8actae3Ccx0z5pIutH7vRrWv3c7nkgSN7ZnwIJU,546
mammoth_commons-0.0.49.dist-info/METADATA,sha256=aLyDi-Y0kbcPWSiWzFAit-wZvKqP6NQkGPS-N50w10Y,2450
mammoth_commons-0.0.49.dist-info/WHEEL,sha256=beeZ86-EfXScwlR_HKu4SllMC9wUEj_8Z_4FJ3egI2w,91
mammoth_commons-0.0.49.dist-info/top_level.txt,sha256=RXydj81_clxj5KiPQok7gPd7XnVl2vyzHsxhjYIUIEI,18
mammoth_commons-0.0.49.dist-info/RECORD,,
