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Datasets:
habedi
/
nearest-neighbors-datasets

Size:
1M<n<10M
Tags:
vector-search
nearest-neighbors
high-dimensional-data
License:
Dataset card Data Studio Files Community
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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 100 new columns ({'1.2582', '0.4187', '0.37829', '-0.091543', '0.48095', '0.090771', '0.39147', '0.67575', '-0.17456', '-0.35084', '0.48402', '0.10017', '0.041849', '0.1277', '-0.39926', '-0.11333', '-0.3253', '-0.075256', '0.17528', '0.1307', '0.26044', '-0.22439', '0.46444', '0.80842', '-0.20821', '-0.059798', '0.75348', '0.054169', '-0.037454', '0.68257', '0.18824', '0.22325', '-0.3336', '0.71263', '-0.071786', '-0.56016', '0.36488', '-0.55831', '0.015313', '0.48424', '-0.038519', '0.38433', '-0.11399', '0.67198', '-0.053462', '0.19716', '0.18809', '0.70502', '0.9895', '-0.036091', '-0.22608', '0.65093', '-0.014555', '-0.58722', '0.62255', '0.25586', '-0.53529', '0.53571', '-0.16484', '0.14877', '0.31399', '0.52522', '0.50097', '-0.44104', '-0.53573', '0.055773', '0.94071', '-0.34122', '-0.13286', '0.24266', '-0.16537', '0.016964', '-0.42795', '-0.20804', '-0.7607', '0.3222', '0.13582', '-0.54722', '0.034206', '0.50237', '-0.2539', '-0.24654', '-0.35431', '0.11503', '-0.021106', '-0.20822', '-0.61738', '-0.39613', '0.2286', '0.71104', '-0.17863', '0.55068', '0.30917', '-0.27053', '0.036634', '0.0080131', '-0.0072514', '0.4292', '-0.42664', '-0.52445'}) and 784 missing columns ({'0.0.130', '213.0.3', '0.0.1', '0.0.126', '216.0.2', '227.0.1', '211.0.4', '0.0.326', '0.0.85', '188.0.1', '181.0.1', '0.0.273', '0.0.349', '224.0.2', '0.0.87', '0.0.318', '167.0.1', '3.0.3', '215.0.6', '41.0', '228.0.6', '0.0.340', '1.0.7', '0.0.125', '234.0', '0.0.242', '0.0.54', '205.0.1', '3.0.2', '198.0.2', '0.0.306', '0.0.189', '185.0.1', '6.0.2', '163.0', '225.0.2', '205.0.3', '0.0.201', '218.0.7', '54.0', '0.0.44', '0.0.235', '0.0.22', '229.0.1', '0.0.25', '1.0.9', '240.0', '222.0.5', '154.0', '196.0.2', '0.0.231', '0.0.150', '180.0', '4.0.3', '0.0.90', '193.0.4', '232.0.2', '0.0.162', '0.0.285', '243.0', '219.0.4', '225.0', '234.0.2', '0.0.254', '230.0.1', '0.0.174', '240.0.1', '0.0.202', '0.0.56', '0.0.137', '216.0.3', '0.0.240', '221.0.8', '215.0.3', '195.0', '145.0', '176.0.3', '0.0.227', '0.0.274', '222.0.8', '183.0.2', '229.0', '69.0', '0.0.102', '218.0.3', '0.0.233', '175.0.1', '228.0.7', '234.0.1', '244.0', '209.0.5', '0.0.310', '0.0.259', '0.0.293', '44.0', '0.0.122', '2.0.1', '0.0.238', '0.0.219', '223.0.12', '0.0.195', '156.0.1', '224.0.5', '188.0.3', '0.0.199', '169.0', '0.0.88', '0.0.191', '2.0', '0.0.220', '209.0.1', '192.0.1', '0.0.290', '0.0.288', '212.0.4', '173.0', '0.0.31', '0.0.34', '0.0.192', '0.0.175', '44.0.1', '136.0', '0.0.140', '161.0', '0.0.325', '0.0.3', '0.0.75', '123.0.1', '219.0.3', '0.0.86', '220.0.4', '212.0.3', '0.0.104', '224.0.3', '0.0.243', '213.0.8', '0.0.329', '209.0.4', '130.0', '213.0.7', '191.0.2', '99.0', '0.0.284', '0.0.27'
...
', '218.0.9', '235.0', '223.0.11', '0.0.245', '0.0.42', '188.0.4', '218.0.5', '106.0', '167.0', '248.0', '0.0.18', '194.0.1', '0.0.106', '0.0.92', '127.0.1', '6.0.1', '0.0.298', '244.0.2', '0.0.127', '0.0.159', '219.0.2', '0.0.212', '211.0.3', '218.0.6', '189.0.1', '12.0', '170.0', '203.0.1', '0.0.139', '200.0.2', '198.0.1', '210.0.4', '0.0.99', '202.0.2', '0.0.107', '0.0.29', '215.0', '0.0.167', '200.0.1', '0.0.260', '122.0', '72.0.1', '0.0.16', '0.0.142', '0.0.71', '185.0.2', '0.0.67', '198.0.3', '221.0.6', '221.0.2', '73.0.1', '0.0.320', '206.0.2', '237.0', '0.0.272', '228.0.5', '176.0.1', '204.0.5', '0.0.196', '233.0.2', '0.0.113', '0.0.144', '207.0.1', '0.0.341', '0.0.148', '15.0', '57.0', '0.0.218', '208.0.3', '0.0.52', '0.0.147', '216.0.6', '221.0.9', '216.0.1', '212.0', '0.0.136', '211.0.5', '223.0.2', '0.0.53', '0.0.58', '0.0.70', '206.0', '1.0.5', '56.0', '249.0', '0.0.346', '0.0.239', '205.0.4', '0.0.24', '190.0', '221.0.4', '0.0.185', '146.0', '210.0.5', '0.0.118', '0.0.256', '0.0.297', '0.0.78', '0.0.63', '217.0.2', '220.0', '0.0.163', '0.0.209', '240.0.2', '0.0.48', '7.0', '206.0.3', '0.0.250', '0.0.38', '208.0.4', '0.0.223', '0.0.276', '0.0.13', '0.0.51', '224.0.1', '219.0', '64.0', '204.0.4', '0.0.315', '0.0.280', '0.0.312', '0.0.213', '0.0.149', '0.0.308', '0.0.69', '213.0.6', '202.0.1', '1.0.1', '134.0', '0.0.55', '217.0.5', '207.0', '0.0.7', '204.0.7', '0.0.116', '211.0', '194.0.2', '0.0.20', '0.0.184', '0.0.151', '239.0.1', '0.0.111', '229.0.5', '0.0.337'}).

This happened while the csv dataset builder was generating data using

hf://datasets/habedi/nearest-neighbors-datasets/glove-100-angular/train.csv (at revision 59a77df276545051fa0919910346655fd4fed3cf)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              -0.11333: double
              0.48402: double
              0.090771: double
              -0.22439: double
              0.034206: double
              -0.55831: double
              0.041849: double
              -0.53573: double
              0.18809: double
              -0.58722: double
              0.015313: double
              -0.014555: double
              0.80842: double
              -0.038519: double
              0.75348: double
              0.70502: double
              -0.17863: double
              0.3222: double
              0.67575: double
              0.67198: double
              0.26044: double
              0.4187: double
              -0.34122: double
              0.2286: double
              -0.53529: double
              1.2582: double
              -0.091543: double
              0.19716: double
              -0.037454: double
              -0.3336: double
              0.31399: double
              0.36488: double
              0.71263: double
              0.1307: double
              -0.24654: double
              -0.52445: double
              -0.036091: double
              0.55068: double
              0.10017: double
              0.48095: double
              0.71104: double
              -0.053462: double
              0.22325: double
              0.30917: double
              -0.39926: double
              0.036634: double
              -0.35431: double
              -0.42795: double
              0.46444: double
              0.25586: double
              0.68257: double
              -0.20821: double
              0.38433: double
              0.055773: double
              -0.2539: double
              -0.20804: double
              0.52522: double
              -0.11399: double
              -0.3253: double
              -0.44104: double
              0.17528: double
              0.62255: double
              0.50237: double
              -0.7607: double
              -0.071786: double
              0.0080131: double
              -0.13286: double
              0.50097: double
              0.18824: double
              -0.54722: double
              -0.42664: double
              0.4292: double
              0.14877: double
              -0.0072514: double
              -0.16484: double
              -0.059798: double
              0.9895: double
              -0.61738: double
              0.054169: double
              0.48424: double
              -0.35084: double
              -0.27053: double
              0.37829: double
              0.11503: double
              -0.39613: double
              0.24266: double
              0.39147: double
              -0.075256: double
              0.65093: double
              -0.20822: double
              -0.17456: double
              0.53571: double
              -0.16537: double
              0.13582: double
              -0.56016: double
              0.016964: double
              0.1277: double
              0.94071: double
              -0.22608: double
              -0.021106: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 11873
              to
              {'0.0': Value(dtype='float64', id=None), '0.0.1': Value(dtype='float64', id=None), '0.0.2': Value(dtype='float64', id=None), '0.0.3': Value(dtype='float64', id=None), '0.0.4': Value(dtype='float64', id=None), '0.0.5': Value(dtype='float64', id=None), '0.0.6': Value(dtype='float64', id=None), '0.0.7': Value(dtype='float64', id=None), '0.0.8': Value(dtype='float64', id=None), '0.0.9': Value(dtype='float64', id=None), '0.0.10': Value(dtype='float64', id=None), '0.0.11': Value(dtype='float64', id=None), '0.0.12': Value(dtype='float64', id=None), '0.0.13': Value(dtype='float64', id=None), '0.0.14': Value(dtype='float64', id=None), '0.0.15': Value(dtype='float64', id=None), '0.0.16': Value(dtype='float64', id=None), '0.0.17': Value(dtype='float64', id=None), '0.0.18': Value(dtype='float64', id=None), '0.0.19': Value(dtype='float64', id=None), '0.0.20': Value(dtype='float64', id=None), '0.0.21': Value(dtype='float64', id=None), '0.0.22': Value(dtype='float64', id=None), '0.0.23': Value(dtype='float64', id=None), '0.0.24': Value(dtype='float64', id=None), '0.0.25': Value(dtype='float64', id=None), '0.0.26': Value(dtype='float64', id=None), '0.0.27': Value(dtype='float64', id=None), '0.0.28': Value(dtype='float64', id=None), '0.0.29': Value(dtype='float64', id=None), '0.0.30': Value(dtype='float64', id=None), '0.0.31': Value(dtype='float64', id=None), '0.0.32': Value(dtype='float64', id=None), '0.0.33': Value(dtype='float64', id=None), '0.0.34': Value(dtype='float64', id=None), '0.0.3
              ...
              ne), '0.0.317': Value(dtype='float64', id=None), '0.0.318': Value(dtype='float64', id=None), '0.0.319': Value(dtype='float64', id=None), '0.0.320': Value(dtype='float64', id=None), '0.0.321': Value(dtype='float64', id=None), '0.0.322': Value(dtype='float64', id=None), '0.0.323': Value(dtype='float64', id=None), '0.0.324': Value(dtype='float64', id=None), '0.0.325': Value(dtype='float64', id=None), '0.0.326': Value(dtype='float64', id=None), '0.0.327': Value(dtype='float64', id=None), '0.0.328': Value(dtype='float64', id=None), '0.0.329': Value(dtype='float64', id=None), '0.0.330': Value(dtype='float64', id=None), '0.0.331': Value(dtype='float64', id=None), '0.0.332': Value(dtype='float64', id=None), '0.0.333': Value(dtype='float64', id=None), '0.0.334': Value(dtype='float64', id=None), '0.0.335': Value(dtype='float64', id=None), '0.0.336': Value(dtype='float64', id=None), '0.0.337': Value(dtype='float64', id=None), '0.0.338': Value(dtype='float64', id=None), '0.0.339': Value(dtype='float64', id=None), '0.0.340': Value(dtype='float64', id=None), '0.0.341': Value(dtype='float64', id=None), '0.0.342': Value(dtype='float64', id=None), '0.0.343': Value(dtype='float64', id=None), '0.0.344': Value(dtype='float64', id=None), '0.0.345': Value(dtype='float64', id=None), '0.0.346': Value(dtype='float64', id=None), '0.0.347': Value(dtype='float64', id=None), '0.0.348': Value(dtype='float64', id=None), '0.0.349': Value(dtype='float64', id=None), '0.0.350': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1433, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 989, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 100 new columns ({'1.2582', '0.4187', '0.37829', '-0.091543', '0.48095', '0.090771', '0.39147', '0.67575', '-0.17456', '-0.35084', '0.48402', '0.10017', '0.041849', '0.1277', '-0.39926', '-0.11333', '-0.3253', '-0.075256', '0.17528', '0.1307', '0.26044', '-0.22439', '0.46444', '0.80842', '-0.20821', '-0.059798', '0.75348', '0.054169', '-0.037454', '0.68257', '0.18824', '0.22325', '-0.3336', '0.71263', '-0.071786', '-0.56016', '0.36488', '-0.55831', '0.015313', '0.48424', '-0.038519', '0.38433', '-0.11399', '0.67198', '-0.053462', '0.19716', '0.18809', '0.70502', '0.9895', '-0.036091', '-0.22608', '0.65093', '-0.014555', '-0.58722', '0.62255', '0.25586', '-0.53529', '0.53571', '-0.16484', '0.14877', '0.31399', '0.52522', '0.50097', '-0.44104', '-0.53573', '0.055773', '0.94071', '-0.34122', '-0.13286', '0.24266', '-0.16537', '0.016964', '-0.42795', '-0.20804', '-0.7607', '0.3222', '0.13582', '-0.54722', '0.034206', '0.50237', '-0.2539', '-0.24654', '-0.35431', '0.11503', '-0.021106', '-0.20822', '-0.61738', '-0.39613', '0.2286', '0.71104', '-0.17863', '0.55068', '0.30917', '-0.27053', '0.036634', '0.0080131', '-0.0072514', '0.4292', '-0.42664', '-0.52445'}) and 784 missing columns ({'0.0.130', '213.0.3', '0.0.1', '0.0.126', '216.0.2', '227.0.1', '211.0.4', '0.0.326', '0.0.85', '188.0.1', '181.0.1', '0.0.273', '0.0.349', '224.0.2', '0.0.87', '0.0.318', '167.0.1', '3.0.3', '215.0.6', '41.0', '228.0.6', '0.0.340', '1.0.7', '0.0.125', '234.0', '0.0.242', '0.0.54', '205.0.1', '3.0.2', '198.0.2', '0.0.306', '0.0.189', '185.0.1', '6.0.2', '163.0', '225.0.2', '205.0.3', '0.0.201', '218.0.7', '54.0', '0.0.44', '0.0.235', '0.0.22', '229.0.1', '0.0.25', '1.0.9', '240.0', '222.0.5', '154.0', '196.0.2', '0.0.231', '0.0.150', '180.0', '4.0.3', '0.0.90', '193.0.4', '232.0.2', '0.0.162', '0.0.285', '243.0', '219.0.4', '225.0', '234.0.2', '0.0.254', '230.0.1', '0.0.174', '240.0.1', '0.0.202', '0.0.56', '0.0.137', '216.0.3', '0.0.240', '221.0.8', '215.0.3', '195.0', '145.0', '176.0.3', '0.0.227', '0.0.274', '222.0.8', '183.0.2', '229.0', '69.0', '0.0.102', '218.0.3', '0.0.233', '175.0.1', '228.0.7', '234.0.1', '244.0', '209.0.5', '0.0.310', '0.0.259', '0.0.293', '44.0', '0.0.122', '2.0.1', '0.0.238', '0.0.219', '223.0.12', '0.0.195', '156.0.1', '224.0.5', '188.0.3', '0.0.199', '169.0', '0.0.88', '0.0.191', '2.0', '0.0.220', '209.0.1', '192.0.1', '0.0.290', '0.0.288', '212.0.4', '173.0', '0.0.31', '0.0.34', '0.0.192', '0.0.175', '44.0.1', '136.0', '0.0.140', '161.0', '0.0.325', '0.0.3', '0.0.75', '123.0.1', '219.0.3', '0.0.86', '220.0.4', '212.0.3', '0.0.104', '224.0.3', '0.0.243', '213.0.8', '0.0.329', '209.0.4', '130.0', '213.0.7', '191.0.2', '99.0', '0.0.284', '0.0.27'
              ...
              ', '218.0.9', '235.0', '223.0.11', '0.0.245', '0.0.42', '188.0.4', '218.0.5', '106.0', '167.0', '248.0', '0.0.18', '194.0.1', '0.0.106', '0.0.92', '127.0.1', '6.0.1', '0.0.298', '244.0.2', '0.0.127', '0.0.159', '219.0.2', '0.0.212', '211.0.3', '218.0.6', '189.0.1', '12.0', '170.0', '203.0.1', '0.0.139', '200.0.2', '198.0.1', '210.0.4', '0.0.99', '202.0.2', '0.0.107', '0.0.29', '215.0', '0.0.167', '200.0.1', '0.0.260', '122.0', '72.0.1', '0.0.16', '0.0.142', '0.0.71', '185.0.2', '0.0.67', '198.0.3', '221.0.6', '221.0.2', '73.0.1', '0.0.320', '206.0.2', '237.0', '0.0.272', '228.0.5', '176.0.1', '204.0.5', '0.0.196', '233.0.2', '0.0.113', '0.0.144', '207.0.1', '0.0.341', '0.0.148', '15.0', '57.0', '0.0.218', '208.0.3', '0.0.52', '0.0.147', '216.0.6', '221.0.9', '216.0.1', '212.0', '0.0.136', '211.0.5', '223.0.2', '0.0.53', '0.0.58', '0.0.70', '206.0', '1.0.5', '56.0', '249.0', '0.0.346', '0.0.239', '205.0.4', '0.0.24', '190.0', '221.0.4', '0.0.185', '146.0', '210.0.5', '0.0.118', '0.0.256', '0.0.297', '0.0.78', '0.0.63', '217.0.2', '220.0', '0.0.163', '0.0.209', '240.0.2', '0.0.48', '7.0', '206.0.3', '0.0.250', '0.0.38', '208.0.4', '0.0.223', '0.0.276', '0.0.13', '0.0.51', '224.0.1', '219.0', '64.0', '204.0.4', '0.0.315', '0.0.280', '0.0.312', '0.0.213', '0.0.149', '0.0.308', '0.0.69', '213.0.6', '202.0.1', '1.0.1', '134.0', '0.0.55', '217.0.5', '207.0', '0.0.7', '204.0.7', '0.0.116', '211.0', '194.0.2', '0.0.20', '0.0.184', '0.0.151', '239.0.1', '0.0.111', '229.0.5', '0.0.337'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/habedi/nearest-neighbors-datasets/glove-100-angular/train.csv (at revision 59a77df276545051fa0919910346655fd4fed3cf)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

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End of preview.

Nearest Neighbors Search Datasets

The datasets listed below are used in Hann library.

Index Dataset Dimensions Train Size Test Size Neighbors Distance Original Source
1 GloVe (25d) 25 1,183,514 10,000 100 Cosine HDF5 (121MB)
2 GloVe (50d) 50 1,183,514 10,000 100 Cosine HDF5 (235MB)
3 GloVe (100d) 100 1,183,514 10,000 100 Cosine HDF5 (463MB)
4 GloVe (200d) 200 1,183,514 10,000 100 Cosine HDF5 (918MB)
5 Last.fm 65 292,385 50,000 100 Cosine HDF5 (135MB)
6 MNIST 784 60,000 10,000 100 Euclidean HDF5 (217MB)
7 Fashion-MNIST 784 60,000 10,000 100 Euclidean HDF5 (217MB)
8 SIFT 128 1,000,000 10,000 100 Euclidean HDF5 (501MB)

Acknowledgement

These are a subset of datasets from the ANN-Benchmarks project.

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