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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x93 in position 0: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/text/text.py", line 90, in _generate_tables
                  batch = f.read(self.config.chunksize)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1104, in read_with_retries
                  out = read(*args, **kwargs)
                File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
                  (result, consumed) = self._buffer_decode(data, self.errors, final)
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x93 in position 0: invalid start byte
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, 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 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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16670,19320,10201,40,31429,44,11112,6677,5524,11196,5495,7026,11352,5287,14088,7449,3,19197,27194,14,20677,7009,5275,5264,44,11112,6677,5524,24346,13282,5536,5259,5559,3
11475,55,5567,7346,14,16551,5264,17042,6264,5259,14296,14958,14,14296,10646,11781,17486,5258,7085,7421,10981,5261,5258,8739,16
6457,12435,7026,6672,14627,5406,5358,11292,5297,9006,7478,5518,16269,14,6457,17157,7026,16351,5258,6331,22502,5358,11292,11582,16,6881,40,9914,6791,9915,7026,10725,5801,5408,10670,5267,29567,14078,5264,5293,24775,17998,5374,16,6881,40,9914,9983,5526,7311,13719,5264,6843,5569,5827,10715,5267,13653,10166,16,26641,5876,8493,5607,7155,9915,7026,10725,16,6881,40,9914,35,2,37
3616,2591,3116,3690,2318,2591,411,600,870,2121,3136,703,316,1055,1055,616,323,4130,3028,377,5125,4792,411,926,3116,3690,2318,2591,411,3110,5111,3284,1846,421,303,5234,345,2121,4268,2198,597,2976,1042,314,323,4130,3028,377,971,5231,3616,172,2091,172,4960,926,316,1055,5232,331,285,173,5349,1544,3116,3690,2318,2591,411,377,4677,3323,3202,5329,1206,14,800,597,2976,3116,3690,2318,2591,411,1509,1073,419,5016,5355,7,173
8937,5269,12059,21866
6162,20765,6852,25081,14213,8,6092,23473,25428,5260,7843,5297,21523,10347,5260,5293,8727,10896,15,15911,6162,20765,6852,5295,10385,5836,8163,7361
44,17209,5257,15156,5602,14,17399,5257,14012,25695,5283,17399,43,9,26755,14710
2635,834,2655,1527,763,4101,2178,4857,4363,418,600,870,2121,285,1361,297,303,3028,377,1176,763,4101,3534,3393,3110,421,303,5234,609,2178,297,303,3019,2347,3534,3393,3110,3028,377,3550,5097,5234,3534,2693,2178,502,4248,5234,2178,5111,316,449,2297,3534,4368,5234,2178,793,1083,290,1429,172,841,1083,365,1429,3370,5234,345,863,1907,1351,1812,4130,2489,535,874,3283,1826,3646,1570,3110,3534,3393,5234,3719,4542,1562,293,879,1351,1812,3110,293,879,4920,2489,5234,1845,1956
2534,767,615,4512,628,303,2180,730,2178,4857,600,870,2831,3539,2180,730,28,6000,5754,15,20745,15,9038,14,1532,1544,3550,4765,3210,4759,172,2482,766,4759,14,2178,2198,15,2089,2198,1173,2650,3691,2069,14,1962,2650,3691,2069,14,1173,875,3691,2069,14,1173,2650,3691,3370,874,3283,2178,2198,3691,173
23,5380,1795,387,1635,4860,15,2485,328,586,4382,3579,3804,2089,2459,2089,1615,3331,1553,910,4542,875,1333,4939,3145,2892,3110,363,3352,335,1066,15,1184,1184,23,5380,1184,1184,2890,14,1728,1621,703,2410,4393,3110,2121,5245,2959,1073,3148,1347,541,1735,14,1246,1239,3822
2111,2973,1821,2198,1351,1812,3352,2121,285,2414,322,2049,3695,4677,4448,1343,693,3110,1591,2040,1097,2146,3719,25474,1562,3031,5234,1329,2121,1321,3028,2275,1048,3246,374,3110,810,2099,4555,4236,1543,864,5234,2121,1321,2275,3391,2978,926,2049,1513,2049,1321,3110,3293,732,1591,2040,1543,864,5234,2121,1347,2959,684,1754,684,303,2308,1749,3110,4481,1562,3031,1543,864,5234,322,1513,3028,1968,480,356,2049,3695,2049,1321,14,3028,2581,2180,730,14,2062,766,1273,335,3110,285,3345,1599,505,1710,2180,730,173
4936,1456,4779,1372,787,172,1173,875,3691,2069,172,1962,2650,3691,2069
4362,3255,5059,4546,5227,5227
8055,32444,7966,23327,5338,5276,5899,19812,21500,5259,6738,15,6211,8525,16,17998,5408,5859,5264,12435,6379,40,8719,12675,5261,6770,5308,33,6155,21313,5264,31387,13474,5431,7759,14,6017,6670,8279,14,7966,5260,5264,17520,5320,14,12073,18616,14,5264,5774,3
21104,5264,5997,22889,15,31931,5285,9,58,6672,19002,5373,9778,22889,5264,14673,5266,16,19829,31865,15,5859,9295,5264,19129,14673,5266,9295,16,17166,7562,14,25564,14,18746,14,32031,8237,5260,14,6189,5707,14,5264,31789,10338,11430,15,19758,15,19797,5847
19160,5264,7003,7449,5337,6588,8776,5474,6316,14,21984,10554,67,7449,15454,7361,15,6588,8776,5474,6316,14,21984,10554,16,7003,6406,3
30793,5267,10282,7823,9861,5261,5503,7123,5528,16051,11296,11825,5310,9861,28853,28011,5528,16,5358,16,5318,18636,9384,5267,10282,11825,5310,5258,9,8001,5748,11296,9,6440,14,7026,5855,19401,5293,5258,16051,11296,7026,10715,7449,5259,8086,16
14078,16664,1381,1741,3771,725,1984,3613,874,3302,573,4349,3110,548,2,172,1361,943,172,1361,4918,839,5026,943,5234,597,2080,448,2815,4989,1351,1826,4560,3784,4588,3110,1361,1430,3028,2581,173,4798,1509,4575,1510,2674,343,2078,3046,5234,376,4166,2178,3485,291,971,1564,5234,1795,2815,2176,952,2169,863,3133,380,1177,4971,573,1741,173
7003,7647,14959,9378,15994,5744,7449,5337,26598,19739,14,6751,7647,14959,5259,7342,21854,6059,14,5570
17010,5378,23280,6305,7321,11112,25132,6778,7530,14643,13958,33,5771,5394,10790,5290,18169,5260,7007,28,26954,24691,7381,14,6154,5257,10418,14,9864,5615,5257,10096,14,17561,5268,14,26677,14,10969,5993,14,16,16,16
4848,3208,2111,2035,406,2121,4848,3208,600,870,2088,292,3110,586,3384,3523,2035,406,377,943,5234,1335,3616,4154,637,2035,406,4895,5013,5234,285,3345,1599,1956,595,2035,406,1346,5234,1656,505,5234,4362,406,4555,5234,56,6316,4742,763,14,1656,505,6189,5518,5234,1527,733,1591,844,3643,1675,4559,4900,1795,692,3768,1502,3140,1562,377,943,5234,2043,2948,5111,5234,4145,2043,1675,5234,4382,2948,449,5227
5293,8163,15,6636,53,18931,8324,5275,11595,30201,5773,14,14765,6662,5422,21602,32632,14,22017,5260,5,28473,6026,24433,6148,14,5276,40,6,24890,14,5396,24,13819,21,16,19,6211,5378,10051,8621,10,20,15,22,11,9419,16,9647,12390,16325,8013,8543,5958,16,16,16
471,3031,5361,16,5358,350,794,1113,880,173,844,1951,2056,410,3108,288,4227,3110,863,480,666,965,1113,880,173,5361,16,5358,1860,1728,374,2111,1333,597,173
2936,935,3246,775,2121,597,3602,2175,1183,3110,1795,387,3246,775,5234,2627,3127,3185,4149,5010,5234,5319,4149,5010,5234,732,2735,5234,1407,4274,5234,2354,655,505,1710,5234,1176,1053,1214,2115,5234,597,1055,3321,4856,5234,586,4382,1069,3539,3523
1844,2175,1219,2955,3110,2321,2894,2677,1794,5234,1417,2215,6291,3024,2145,2677,1794,1183,819,173,2955,2321,2894,2677,1794,5234,4401,2607,4677,4678,1508,5234,2169,1177,1341,2080,2581,732,2788,2831,4525,4072,316,173,6291,3024,2145,2677,1794,1183,819,2121,1055,616,2175,2202,1261,3110,426,4820,2677,1794,292,4483,316,1663,5234,1933,5131,2677,1794,5234,2321,2894,2677,1794,3370,426,4820,2677,1794,1562,2178,1424,2178,5234,3556,462,293,285,2278,3110,2677,1794,453,5097,173
4497,4900,1509,2567,477,4517,2717,1028,2207,2178,4857,600,870,2121,297,2556,361,5111,943,4368,2717,2655,1028,3110,3196,844,294,3028,377,3110,297,303,2198,2226,16,3040,4262,5245,29943,5754,15,27675,6131,5994,28012,6148
3367,285,4362,3550,1341,2080,3602,3345,5234,25,5707,20738,1407,2099,1968,480,3688,1509,4072,1735,172,3550,2592,1183,741,172,4678,4053,2041,3377,172,4072,303,732,1681,172,297,1361,673,2229,3370,4362,3550,4390,4236,5246,597,3602,2923,1361,3139,2009,4285,3688,4242,4678,172,395,2091,3688,1509,172,600,870,294,4072,303,172,4154,3192,4362,971,172,1191,3727,5021,2159,3370,3367,285,4362,3550,3040,4149,3809,3136,173
4470,3756,4607,2509,4470,3284,1846,1341,3602
16670,7449,5376,17,25,7139,7179,5318,10282,30511,12488,7481,7976,6901,23282,9200,6683,5301,7496,7481,25472,25251,5710,5536,14234,5577,6,5515,18636,20677,8223,5289,9,6281,5259,18081,13092,9243,19296,18996,16670,6406,5643,59,15,27179,16670,6406,7105,24314,5383,7,6425,16670,6406,12984,22446,8163,8163,13469,5259,28,5904,43,5793,47,6218,52,5341,58,24775,9194,22017,11887,7430,5260,7544
15997,6631,10607,11453,15,6117,24335,7117,6205,5272,7796,13156,10159,44,11061
1968,480,586,4382,3110,1361,4101,172,1495,4101,3370,874,3414,1815,1433,4101,509,4245,4227,2080,2289,14,586,4382,291,4813,4677,1815,2180,730,14,4130,4101,509,14,1417,291,4853,4129,834,3641,1815,4101,509,173
11837,26295,5846,31576,5371
1984,2919,1984,3455
15,2745,1079,2558,2567,2958,502,2484,1170,2978,4245,1171,2178,4857,600,870,448,361,3616,325,3110,300,3046,5021,3379,4634,2,2,2745,1079,5234,600,870,3768,5349,1544,1795,3343,410,2215,5234,582,881,3196,693,666,1177,2414,2558,2567,2894,285,453,766,2500,2484,172,643,2484,1170,2978,4245,1171,5234,874,3283,4644,3609,729,3875,4101,3609,172,3585,4673,2611,2459,1028,172,2408,2482,4660,4733,844,3028,1028,172,360,2479,766,2482,844,3028,1028,172,2472,2605,2198,172,4517,2717,1028,3370,3442,680,377,943,5234,847,692,2078,3641,3031,1812,839,303,3046,3110,1553,2550,1219,4249,173
49,5523,2461,4398,5234,2121,597,2470,1273,335,3110,297,303,2321,2894,1069,3539,3346,1846,1543,864,14,5059,684,3346,1846,2461,4398,2383,1599,14,586,4382,4401,1205,943,173,1968,480,2068,1071,323,14,5189,1399,14,1042,1055,627,2321,14,4313,2321,14,782,3524,3370,2321,2894,2677,1794,5234,879,2099,4523,2178,1933,5131,14,3911,2976,14,3435,2233,4528,4528,2611,3370,426,4820,2677,1794,173,410,3346,1846,322,2279,1663,5234,4555,4517,3542,3545,2461,4398,3110,1625,1599,5234,4232,1728,1069,3456,1607,697,2766,316,380,847,2677,1794,3110,335,4416,173
16137,28191,5259,12364,11814,12010,6738,7569,16488,14,8793,8948,5831,14,5264,11601,20836,5258,11504,5267,25763,15391,14,11404,25370,27855,6015,14,5264,10225,11601,18328,5447,28507,5267,5552,13614,17200,16,5607,6440,5286,7380,14859,5293,10006,5269,15,22717,11404,5831,16
17166,7058,5259,58,19499,5288,5606,17694,5876,17166,5264,20905,5312,5971,16
291,5051,1509,953,3815,3413,1635,4149,1028,2207,2178,4857,600,870
9837,5310,8001,7299,14,10852,7026,9,6281,14591,13913,5374,10282,5526,16850,5312,5471,6379,14,5259,23492,5493,14,14088,17,19160,5374,40,5322,8205,31913,5298,5466,13864,5267,22646,29,48,5503,16403,5267,18602,7026,17873,10282,7254,9692,9319,5306,12440,7026,5318,20878,6476,5264,10860,6976,5495,7632,5258,7229,9779,5276,18189,16
2882,2141,3105,4129,2180,316,1055,5111,3352,4129,2180,943,2894,14,297,2556,5111,3524,4129,2180,3279,387,4228,693,14,421,303,316,5111,2297,4129,2180,14,3663,303,4101,14,4087,4083,14,1495,460,4129,4101,14,1495,460,2180,3370,5111,3352,4129,2180,1343,693,16,1177,1544,3110,4260,505,3550,3916,14,1335,986,3110,965,881,2180,730,14,1343,693,2831,3539,6000,28674,24433,5824,6131,6026
666,4405,5062,216,1735,1129,227,1735,1129,279,12057,23542,25962,5674,673,2229,212,6292,172,357,1749,213,1637,3343,209,22645,19428,14288,6133,12106,1432,199,5227,180,211,13259,13763,7147,4134,227,11072,8050,3353,5062,270,12286,9344,181,236,3136,1906,6254,32677,32677,2457,2091,2169,2078,6254,8050,20666,5227
5376,17,25,5267,12266,5264,16035,7156,14950,6823
1445,305,4533,2603,3646,3236,3391,4579,3688,419,2178,4857,600,870,2121,285,1361,297,303,398,359,4411,5111,673,1308,4677,3667,342,2832,3442,680,377,943,3196,844,172,3028,377,172,4765,965,5234,1545,1069,2078,290,2224,1905,2894,291,1509,3110,5111,2078,1846,2194,421,303,173,4533,2603,2894,4411,5111,673,1308,4677,3667,342,2832,3391,2207,1503,3550,1069,1055,616,874,1183,640,4678,172,3191,1445,172,3040,725,172,2851,2809,172,766,1495,3370,421,303,1562,3031,1177,1544,5234,1651,692,875,460,421,303,3110,4229,863,173
40,31689,5322,5444,6460,20208,8569,5259,8242,15,13356,22203,10584,6694,5837,9122,8,10040,5275,9346,5704,8161
30180,6205,5267,12266,6476,12440,9180,5267,12266,6823,5259,30180,6205,5264,5258,30180,6205,9460,5996,16,6476,5996,8141,30180,6205,5264,13135,5285,8980,5264,48,15,23,14,48,15,5794,14,9946,5557,8,5974,6579,16,6457,11825,6172,5264,6314,9480,5267,12266,6823,6017,7009,5267,12266,14,5399,8439,5267,12266,16
19296,6421,16293,6672,5359,6115,9303,7280,5997,16,14,18059,5312,16,14,19045,5259,17985,5264,22268,5374,45,7847,5298,20961,14,8493,5281,46,18074,5493,20961,5264,5774,5706,10465,16
841,3993,1361,3279,5170,2682,297,794,1564,3602,291,971,1108,841,3993,1361,3279,5170,2682,297,794,1564,3602,291,971,1108,2423,1599,291,3539,5234,3539,291,4373,2901,5234,3539,292,453,5097,5234,3040,971,3110,417,2281,5234,1347,453,1564,3110,2180,730,5234,291,288,2414,377,943,5234,4232,1553,1183,4998,1351,2179,840,412,1424,1735,4546,4373,5234,417,2281,1347,1741,943,4368,502,4884,965,881,2089,1674
30400,5298,48,7118,5560,24156,30400,5298,48,7118,5560,24156,9806,5503,5437,14,5322,31567,18025,5281,5800,9002,5422,9732,9315,5275,16,9901,31135,14639,30001,48,7118,5560,16,5891,11730,5310,17394,5390,40,24156,5299,26610,5422,21958,13692,5275,8928,7224,5283,13509,16,22609,5298,10267,29212,10336,5475,5260,20375,5544,25062,5390,16,30218,5260,40,7701,7350,5312,5623,6010,16,26371,5415,9357,14600,5488,42,15489,7430,17300,5271,11205,7226,8800,9754,6020,5365,5262,26521,16,16112,15134,32432,64,13986,5415,64,5344,7226,7209,5514,5273
10270,27613,5318,32002,11825,15229,24743,18347,5318,40,22835,26850,5338,19440,5855,27961,16,10852,24743,5337,5258,5358,11292,5261,10282,6771,6379,5318,32002,5306,10282,6610,5267,8169,18484,7736,7026,6031,5258,8088,5400,5310,10282,6771,14637,5466,21503,16177,5493,5310,5258,16934,5261,10282,6050,29062,14,27613,5431,7759,5337,5536,26357,10730,15,21277,5337,8472,5969,5267,17142
2410,2281,2997,5231,1445,305,5232,2958,1143,3284,1846,2178,4857,600,870,297,303,3586,4560,1495,303,2567,643,5234,1495,817,285,3345,1599,2484,3391,1361,2180,730,5234,3196,844,3028,377,1968,4847,1100,699,172,2200,3897,699,3370,1655,2958,2484,1170,2978,766,1321,943,172,3040,766,1321,4862,1100,4101,3609,172,842,2662,4549,3756,4847,1100,699,3370,3756,766,1321,943,5234,600,870,1069,4072,303,616,1890,2178,356,4485,1183,1635,949,5234,582,881,4096,3666,322,316,1055,1495,303,2655,2571,788,430,5231,48,5528,5471,5232,714,2978,359,4797,793,448,5234,316,1055,4768,2801,2484,1170,2978,788,430,5231,42,5554,5818,15475,5232,1532,730,2978,359,793,448,172,316,1055,3432,3544,766,1495,788,430,2978,359,793,448,172,178,2655,2571,300,3046,179,2201,1669,714,2978,359,4797,793,448,172,48,5528,5471,297,1361,1249,913,430,714,323,418,1249,913,793,448,172,48,5528,5471,1495,3296,1249,913,430,714,323,418,1249,913,793,448,172,48,5528,5471,3875,699,1249,913,430,714,323,418,1249,913,793,448,172,48,5528,5471,2655,2571,4101,1171,297,303,1249,913,430,1249,913,793,448,5234,48,5528,5471,1333,597,1495,460,1249,913,430,1249,913,793,448,173,316,1055,766,1495,2082,1495,421,303,788,430,2978,359,793,448,172,316,1055,3756,1495,303,788,430,1045,453,430,913,793,448,172,316,1055,3188,2536,1495,3296,1589,4245,788,430,1045,453,430,913,793,448
1796,600,870,3028,377,4753,875,4678,3108,868,3331,14,4771,4734,3108,868,3331,14,1821,732,3108,868,3331,14,3040,732,3108,868,3331,14,4843,864,1860,2270,14,2281,2264,14,3108,868,3331,666,5021,854,14,2611,4963,3108,868,3331,14,3108,868,3331,4644,416,14,3040,732,3108,868,3331,1097,2207,14,3768,1094,3108,868,3331,14,3108,868,3331,3321,4283,3607,14,1537,1149,4753,793,2224,14,3108,868,3331,1097,2207
6738,22906,13975,5353,15997,6631,14714,21069,8124,5293,10282,27078,22715,15,9254
23413,11523,5919,7331,5921,15832,5260,5974,10854,14,15980,5320,5264,27290,5260,5293,7286,18266,16,6457,11825,40,8226,5261,10465,5337,5468,21053,16304,5974,10854,5267,5279,16357,5390,27290,5260,5924,5297,14233,6274,8,5997,28161,5260,16,6457,11825,7897,5264,21329,6017,14035,3
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PC2-NoiseofWeb

This repo releases data introduced in our paper accepted:

PC2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval
Authors: Yue Duan, Zhangxuan Gu, Zhenzhe Ying, Lei Qi, Changhua Meng and Yinghuan Shi

Data Collection

We develop a new dataset named Noise of Web (NoW) for NCL. It contains 100K image-text pairs consisting of website images and multilingual website meta-descriptions (98,000 pairs for training, 1,000 for validation, and 1,000 for testing). NoW has two main characteristics: without human annotations and the noisy pairs are naturally captured. The source image data of NoW is obtained by taking screenshots when accessing web pages on mobile user interface (MUI) with 720 X 1280 resolution, and we parse the meta-description field in the HTML source code as the captions. In NCR (predecessor of NCL), each image in all datasets were preprocessed using Faster-RCNN detector provided by Bottom-up Attention Model to generate 36 region proposals, and each proposal was encoded as a 2048-dimensional feature. Thus, following NCR, we release our the features instead of raw images for fair comparison. However, we can not just use detection methods like Faster-RCNN to extract image features since it is trained on real-world animals and objects on MS-COCO. To tackle this, we adapt APT as the detection model since it is trained on MUI data. Then, we capture the 768-dimensional features of top 36 objects for one image. Due to the automated and non-human curated data collection process, the noise in NoW is highly authentic and intrinsic. The estimated noise ratio of this dataset is nearly 70%.

Data Structure


|-- h5100k_precomp
|   |-- dev_caps_bpe.txt
|   |-- dev_caps_bert.txt
|   |-- dev_caps_jieba.txt
|   |-- dev_ids.txt
|   |-- dev_ims.npy
|   |-- test_caps_bpe.txt
|   |-- test_caps_bert.txt
|   |-- test_caps_jieba.txt
|   |-- test_ids.txt
|   |-- test_ims.npy
|   |-- train_caps_bpe.txt
|   |-- train_caps_bert.txt
|   |-- train_caps_jieba.txt
|   |-- train_ids.txt
|   |-- train_ims.npy
|-- vocab
|   |-- now100k_precomp_vocab_bert.json
|   |-- now100k_precomp_vocab_bpe.json
|   |-- now100k_precomp_vocab_jieba.json

Please note that since our raw data contains some sensitive business data, we only provide the encoded image features (*_ims.npy) and the token ids of the text tokenized. For tokenizer, we provide Tokenizers with BPE to produce *_caps_bpe.txt, BertTokenizer with bert-base-multilingual-cased pre-trained model to produce *_caps_bert.txt, and Jieba to produce *_caps_jieba.txt. Our vocabulary size of BPETokenizer is 10,000, while BertTokenizer and JiebaTokenizer have a vocabulary size of 32,702 and 56,271 respectively. (recorded in now100k_precomp_vocab_*.txt). *_ids.txt records the data indexs in the original 500k dataset. In the future, we may process and make the original dataset public.

Usage

# data_path: your dataset name and path
# data_split: {train,dev,test}
# tokenizer: {bpe,bert,jieba}
# vocabulary size of {bpe,bert,jieba} is {10000,32702,56271} 

# captions
with open(os.path.join(data_path, "{}_caps_{}.txt".format(data_split, tokenizer))) as f:
    for line in f:
        captions.append(line.strip())
captions_token = []
for index in range(len(captions)):
  caption = captions[index]
  tokens = caption.split(',')
  caption = []
  caption.append(vocab("<start>"))
  caption.extend([int(token) for token in tokens if token])
  caption.append(vocab("<end>"))
  captions_token.append(caption)

# images
images = np.load(os.path.join(data_path, "%s_ims.npy" % data_split))

return captions_token, images

Additionally, you can search for code snippets containing the string now100k_precomp in co_train.py, data.py, evaluation.py, and run.py in PC2's repo and refer to them to process the NoW dataset for use in your own code.

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