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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 4 new columns ({'clicknewsID', 'rewrite_titles', 'userid', 'posnewID'}) and 9 missing columns ({'ClicknewsID', 'start', 'dwelltime', 'neg', 'pos', 'dwelltime_pos', 'UserID', 'exposure_time', 'end'}). This happened while the csv dataset builder was generating data using hf://datasets/THEATLAS/PENS/personalized_test.tsv (at revision 181ff06919ea35f4c55514b882ab9eb02913a195) 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 1869, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 580, 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 2292, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast userid: string clicknewsID: string posnewID: string rewrite_titles: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 739 to {'UserID': Value(dtype='string', id=None), 'ClicknewsID': Value(dtype='string', id=None), 'dwelltime': Value(dtype='string', id=None), 'exposure_time': Value(dtype='string', id=None), 'pos': Value(dtype='string', id=None), 'neg': Value(dtype='string', id=None), 'start': Value(dtype='string', id=None), 'end': Value(dtype='string', id=None), 'dwelltime_pos': Value(dtype='string', 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 1387, 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 980, in stream_convert_to_parquet builder._prepare_split( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1740, 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 1871, 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 4 new columns ({'clicknewsID', 'rewrite_titles', 'userid', 'posnewID'}) and 9 missing columns ({'ClicknewsID', 'start', 'dwelltime', 'neg', 'pos', 'dwelltime_pos', 'UserID', 'exposure_time', 'end'}). This happened while the csv dataset builder was generating data using hf://datasets/THEATLAS/PENS/personalized_test.tsv (at revision 181ff06919ea35f4c55514b882ab9eb02913a195) 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)
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UserID
string | ClicknewsID
string | dwelltime
string | exposure_time
string | pos
string | neg
string | start
string | end
string | dwelltime_pos
string |
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6/23/2019 10:28:02 AM | 6/23/2019 10:54:50 AM | 5 34 90 |
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U299988 | N52481 N61763 N107795 N46954 N43826 | 328 196 104 73 61 | 6/14/2019 10:08:36 AM#TAB#6/16/2019 5:21:10 AM#TAB#6/16/2019 5:27:07 AM#TAB#6/18/2019 7:10:55 PM#TAB#6/21/2019 9:57:15 AM | N69599 N58500 | N39964 N14872 N108858 N118951 N71699 N89429 N66336 N17442 N18659 N59713 N44139 N84470 N25990 N74501 N95521 N74267 N49919 N48408 N95906 N26218 N15467 N48906 N92626 N111593 N56261 N110386 N121758 N115478 N56547 N75117 N112696 N46487 N18523 N36857 N99138 N64645 N85909 N105957 N66660 N72416 N39191 N99785 | 6/22/2019 3:44:13 PM | 6/22/2019 4:13:21 PM | 100 56 |
U429177 | N55111 N12470 N13696 N100679 N94044 N12695 N119155 N92115 N55167 N101671 N114725 N15761 N19024 N48858 | 50 42 100 41 90 60 139 71 39 17 16 128 76 65 | 6/17/2019 5:42:49 AM#TAB#6/18/2019 5:16:22 AM#TAB#6/18/2019 5:45:36 AM#TAB#6/18/2019 5:52:59 AM#TAB#6/18/2019 8:43:59 AM#TAB#6/18/2019 8:51:29 AM#TAB#6/18/2019 9:44:03 AM#TAB#6/18/2019 12:07:27 PM#TAB#6/18/2019 12:10:52 PM#TAB#6/18/2019 1:06:17 PM#TAB#6/19/2019 5:29:25 AM#TAB#6/19/2019 5:33:21 AM#TAB#6/19/2019 8:49:56 AM#TAB#6/19/2019 8:52:50 AM | N81145 | N113051 N111360 N120443 N87546 N119985 N84809 N93019 N64404 N81907 N106486 N42576 N64846 N107618 N28149 N56180 N43334 N54014 N110015 N95038 N121451 N43826 N108781 N24847 N89907 N78798 N74429 N10898 N72126 N23948 N68456 N89085 N114715 N109436 N55938 N20724 N119047 N89196 N80819 N89208 N22865 N86743 | 6/21/2019 4:38:21 AM | 6/21/2019 4:50:32 AM | 104 |
U217448 | N43558 N77086 N122550 | 725 46 699 | 6/14/2019 8:15:43 AM#TAB#6/14/2019 12:36:10 PM#TAB#6/17/2019 3:48:23 PM | N56674 N86226 | N81948 N84343 N111808 N104733 N80645 N20031 N119531 N31437 N97643 N87665 N92602 N111452 N92972 N27570 N56386 N99572 N69139 N31115 N57315 N46954 N104263 N47528 N60443 N39614 N112492 N104096 N121292 N46244 N123024 N77361 N93379 N49794 N84273 N46252 N117076 N31573 N54516 N95007 N79600 N21782 N11156 N94956 N113677 N52578 N85749 N58206 N34483 | 6/19/2019 10:09:02 AM | 6/19/2019 10:34:24 AM | 134 84 |
U118907 | N75768 N58081 N25368 N113705 N98288 N11143 N78088 N51184 N71657 N38517 N40778 | 16 19 315 47 2 12 109 5 16 61 222 | 6/14/2019 9:36:42 AM#TAB#6/14/2019 9:36:46 AM#TAB#6/14/2019 9:37:12 AM#TAB#6/17/2019 8:42:06 AM#TAB#6/17/2019 8:42:33 AM#TAB#6/17/2019 8:44:21 AM#TAB#6/18/2019 10:10:07 AM#TAB#6/18/2019 10:14:26 AM#TAB#6/18/2019 10:14:51 AM#TAB#6/18/2019 10:15:47 AM#TAB#6/18/2019 10:24:19 AM | N63252 N99572 N47528 N75058 | N116937 N81948 N84343 N111808 N104733 N22227 N80645 N23921 N20031 N31437 N97643 N87665 N50599 N55139 N18457 N111452 N92972 N56386 N49383 N18912 N69139 N46954 N39614 N104096 N77212 N46244 N77361 N93379 N49794 N109636 N46252 N36405 N86196 N74743 N37719 N48341 N25862 N48999 N44759 N117076 N31573 N110954 N54516 N21413 N79600 N81223 N11248 N43382 N11156 N65059 N94956 N53438 N46765 N52578 N58206 N105557 N34483 | 6/19/2019 11:41:36 AM | 6/19/2019 12:11:41 PM | 15 13 41 89 |
U317643 | N27499 N37813 N47697 N81048 N123402 N94579 N76864 N24914 N88991 N79300 N122835 N98578 N103854 N84319 | 75 30 21 142 18 31 13 7 51 179 151 243 8 469 | 6/14/2019 5:20:46 AM#TAB#6/14/2019 8:15:51 AM#TAB#6/14/2019 9:00:48 AM#TAB#6/14/2019 9:05:29 AM#TAB#6/14/2019 9:09:57 AM#TAB#6/14/2019 9:14:24 AM#TAB#6/14/2019 9:14:49 AM#TAB#6/14/2019 9:14:51 AM#TAB#6/14/2019 10:42:23 AM#TAB#6/14/2019 11:22:05 AM#TAB#6/14/2019 11:35:13 AM#TAB#6/14/2019 12:07:36 PM#TAB#6/14/2019 12:11:39 PM#TAB#6/14/2019 1:29:09 PM | N103844 | N85806 N39990 N93732 N115495 N51924 N59685 N14961 N88470 N81726 N91267 N119833 N115534 N112255 N110099 N72064 | 6/17/2019 11:15:40 AM | 6/17/2019 11:27:39 AM | 28 |
U288300 | N44141 N73870 N22545 | 121 8 2 | 7/1/2019 4:07:51 PM#TAB#7/2/2019 3:50:10 PM#TAB#7/3/2019 3:11:36 AM | N96373 | N69635 N65982 N92295 N40211 N110935 N83257 N98752 N93042 N57523 N74516 N69952 N28533 N11071 N97496 | 7/4/2019 8:34:28 PM | 7/4/2019 8:38:53 PM | 68 |
U2582 | N67649 N58081 N95202 | 29 200 17 | 6/14/2019 1:55:11 AM#TAB#6/14/2019 10:36:29 AM#TAB#6/14/2019 12:25:56 PM | N92168 | N11893 N88026 N39897 N119816 N67166 N75461 N123553 N111562 N59993 | 6/16/2019 4:25:38 AM | 6/16/2019 4:52:23 AM | 39 |
U92676 | N123225 N11255 N58081 N50771 N121334 N98923 N79300 N20589 N109222 N57729 N59308 | 106 105 30 1 111 48 46 60 38 301 3 | 6/14/2019 6:00:53 AM#TAB#6/14/2019 6:02:50 AM#TAB#6/14/2019 6:43:14 AM#TAB#6/14/2019 6:43:20 AM#TAB#6/14/2019 7:35:26 AM#TAB#6/14/2019 7:37:18 AM#TAB#6/14/2019 8:39:54 AM#TAB#6/17/2019 2:31:13 PM#TAB#6/17/2019 2:32:55 PM#TAB#6/17/2019 6:40:48 PM#TAB#6/17/2019 6:40:52 PM | N111808 | N81948 N84343 N92972 N18912 N31115 N39614 N104096 N123024 N77361 N93379 N109636 N48999 N44759 N117076 N110954 N79600 N21782 N56975 N86226 | 6/19/2019 11:07:17 AM | 6/19/2019 11:10:16 AM | 94 |
U193564 | N27807 N82259 N118791 N27093 N24612 N102644 N23912 | 9 22 41 68 72 31 73 | 6/13/2019 5:05:33 PM#TAB#6/13/2019 5:37:34 PM#TAB#6/14/2019 6:34:16 PM#TAB#6/14/2019 6:34:33 PM#TAB#6/14/2019 6:34:46 PM#TAB#6/14/2019 6:44:48 PM#TAB#6/14/2019 6:46:49 PM | N94579 N90064 | N96530 N50461 N101248 N91695 N90094 N116546 N11341 N92024 N54273 N56678 N110105 N35418 N95456 N86089 N11804 N37501 N38939 N95772 N20178 N101671 N104651 N19160 N31114 N18094 N15761 N114725 N34830 N121466 N18071 | 6/18/2019 6:51:51 PM | 6/18/2019 7:00:33 PM | 11 20 |
U10003 | N113715 N82259 N30838 N67437 N61223 N47034 N10817 N37308 N49515 N103951 N47934 N117433 N56485 N13691 N96955 N43877 N15119 | 9 31 11 167 31 387 72 3 606 7 55 6 90 51 333 518 50 | 6/13/2019 6:24:58 PM#TAB#6/13/2019 6:25:08 PM#TAB#6/13/2019 6:25:24 PM#TAB#6/20/2019 7:37:13 PM#TAB#6/20/2019 7:40:57 PM#TAB#6/23/2019 6:30:49 PM#TAB#6/23/2019 6:37:34 PM#TAB#6/23/2019 6:38:46 PM#TAB#6/23/2019 6:51:27 PM#TAB#6/23/2019 6:51:40 PM#TAB#6/24/2019 1:08:26 PM#TAB#6/24/2019 1:08:39 PM#TAB#6/24/2019 1:09:22 PM#TAB#6/24/2019 1:41:59 PM#TAB#6/27/2019 6:53:23 AM#TAB#6/27/2019 6:59:06 AM#TAB#6/29/2019 7:45:44 PM | N122527 N75168 | N88045 N120122 N108379 N34615 N66402 N88819 N96884 N29363 N84898 N38386 N43378 N12913 N121240 N123483 N101667 N22514 N30600 N57764 N52708 N43104 N33408 N36079 N119568 N106373 N117791 N68345 N86945 N41719 N122614 N112168 N61657 N36067 N113102 N52847 | 7/1/2019 1:33:08 PM | 7/1/2019 1:52:40 PM | 29 63 |
U101398 | N13696 N19101 N82453 N41392 N98516 N122803 | 98 908 16 23 169 367 | 6/19/2019 4:52:43 AM#TAB#6/21/2019 2:31:33 PM#TAB#6/21/2019 2:47:57 PM#TAB#6/21/2019 2:48:59 PM#TAB#6/21/2019 2:51:22 PM#TAB#6/22/2019 8:10:09 AM | N112743 | N27508 N77334 N47748 N72751 N53464 N11234 N114263 N14396 N71612 N64005 N105817 N59858 N71728 | 6/28/2019 9:34:40 AM | 6/28/2019 9:35:21 AM | 30 |
U46343 | N83606 N43145 N39195 N69326 N84162 N60541 N39995 N119816 N14376 N85514 N16840 N23870 N114578 N40251 N104138 N39094 N15124 N122334 N29427 N54647 N38780 N41447 N32132 N42583 N66523 N30104 N31487 N69542 N86613 N108981 N41392 N87253 N98516 N82910 N65832 N92786 N54265 N40558 N71699 N39191 N96346 N93807 N51035 N48963 N15023 | 180 178 48 199 618 57 654 47 145 24 9 210 87 41 322 71 827 1059 125 107 83 143 48 116 139 924 68 176 139 418 152 219 280 677 45 26 1789 7 11 15 9 255 209 77 566 | 6/14/2019 5:55:42 AM#TAB#6/14/2019 6:56:23 AM#TAB#6/14/2019 8:10:55 AM#TAB#6/14/2019 8:11:54 AM#TAB#6/14/2019 1:14:38 PM#TAB#6/14/2019 1:45:27 PM#TAB#6/14/2019 1:47:01 PM#TAB#6/15/2019 10:06:27 AM#TAB#6/15/2019 10:09:56 AM#TAB#6/16/2019 4:51:36 AM#TAB#6/16/2019 4:51:42 AM#TAB#6/16/2019 5:39:55 AM#TAB#6/16/2019 5:51:37 AM#TAB#6/16/2019 5:54:05 AM#TAB#6/16/2019 8:51:11 AM#TAB#6/16/2019 5:33:07 PM#TAB#6/17/2019 6:49:16 AM#TAB#6/17/2019 7:10:43 AM#TAB#6/17/2019 3:39:15 PM#TAB#6/18/2019 6:44:03 AM#TAB#6/18/2019 6:46:00 AM#TAB#6/18/2019 6:47:45 AM#TAB#6/18/2019 6:50:56 AM#TAB#6/18/2019 12:33:25 PM#TAB#6/18/2019 12:35:29 PM#TAB#6/18/2019 12:38:16 PM#TAB#6/19/2019 4:35:57 PM#TAB#6/20/2019 7:25:14 AM#TAB#6/20/2019 7:28:24 AM#TAB#6/20/2019 7:34:20 AM#TAB#6/21/2019 1:35:14 PM#TAB#6/21/2019 1:40:46 PM#TAB#6/21/2019 2:21:53 PM#TAB#6/21/2019 2:27:26 PM#TAB#6/22/2019 9:33:04 AM#TAB#6/22/2019 9:37:22 AM#TAB#6/22/2019 9:37:30 AM#TAB#6/22/2019 10:26:22 AM#TAB#6/22/2019 11:20:09 AM#TAB#6/22/2019 11:24:21 AM#TAB#6/23/2019 8:25:26 AM#TAB#6/23/2019 9:33:52 AM#TAB#6/24/2019 8:39:36 AM#TAB#6/24/2019 8:47:56 AM#TAB#6/24/2019 8:50:06 AM | N102300 | N110460 N70065 N41979 N98034 N38633 N42107 N101558 N64949 N104066 N67373 N65931 N45846 N62288 N25568 N53826 N108673 N27719 N105874 N49846 N93930 N100594 N28736 N85801 N67174 N52277 N31593 N43877 N21194 N109447 N24092 N97569 N39027 N96275 N15119 | 6/26/2019 6:45:31 PM | 6/26/2019 6:46:31 PM | 37 |
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U171990 | N103854 N42583 | 25 1582 | 6/14/2019 11:16:12 AM#TAB#6/18/2019 11:41:15 AM | N61223 | N12313 N86613 N83288 N23679 N105995 N61629 N79824 N120637 N73098 N23959 | 6/20/2019 8:27:14 AM | 6/20/2019 8:37:53 AM | 631 |
U417753 | N23681 N120637 N74501 N70646 | 241 127 120 25 | 6/18/2019 12:38:32 PM#TAB#6/20/2019 7:32:57 AM#TAB#6/22/2019 9:42:00 AM#TAB#6/26/2019 7:21:49 AM | N107832 | N88045 N122527 N88106 N25844 N34615 N45033 N84898 N123483 N59744 N54914 N76332 N89311 N53098 N78737 N16684 N53937 N32327 N53394 N97484 N83297 N71542 N113102 N19885 N75168 | 7/1/2019 8:37:13 PM | 7/1/2019 8:39:52 PM | 75 |
U444356 | N99430 N52322 N117735 N116873 N27570 N77212 N32315 N42592 N67918 N14844 N19468 N41081 N39957 N29475 N59840 N13421 N101818 N113021 N70298 N99177 N38766 N65253 N111878 N69780 N15506 N116180 N110328 N25251 | 416 223 38 611 611 86 852 102 191 176 55 190 164 502 89 86 217 70 119 103 135 58 12 79 201 612 150 279 | 6/19/2019 5:57:47 AM#TAB#6/19/2019 6:07:27 AM#TAB#6/19/2019 6:12:54 AM#TAB#6/19/2019 6:26:50 AM#TAB#6/19/2019 10:54:04 AM#TAB#6/19/2019 3:05:49 PM#TAB#6/19/2019 3:26:37 PM#TAB#6/19/2019 3:46:46 PM#TAB#6/20/2019 6:12:49 AM#TAB#6/20/2019 6:17:10 AM#TAB#6/20/2019 1:22:36 PM#TAB#6/21/2019 6:51:45 AM#TAB#6/23/2019 5:31:10 PM#TAB#6/23/2019 5:34:21 PM#TAB#6/23/2019 5:42:59 PM#TAB#6/24/2019 6:17:08 AM#TAB#6/24/2019 6:22:03 AM#TAB#6/24/2019 8:57:52 AM#TAB#6/24/2019 11:07:18 AM#TAB#6/24/2019 11:09:49 AM#TAB#6/24/2019 12:27:38 PM#TAB#6/24/2019 12:32:17 PM#TAB#6/24/2019 12:37:07 PM#TAB#6/24/2019 12:38:12 PM#TAB#6/24/2019 12:57:04 PM#TAB#6/24/2019 1:55:49 PM#TAB#6/25/2019 6:15:51 AM#TAB#6/25/2019 6:19:13 AM | N104870 N23323 | N65742 N119172 N17103 N122006 N93272 N98034 N83653 N42107 N68929 N109245 N28807 N96241 N44353 N31018 N66423 N21631 N121939 N109804 N61687 N70646 N19616 N115515 N29003 N41193 N115199 N116036 N106108 N27222 N20362 N114775 N70871 N107851 N117490 N43053 N96013 N119602 N121773 N95365 N111523 N112275 N121550 N33793 N72944 | 6/26/2019 8:46:22 AM | 6/26/2019 9:01:38 AM | 59 34 |
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U61431 | N109049 N47697 N15107 N11705 N85449 N53716 N120373 N14869 N39555 N75910 N50552 N99270 N51924 N121414 N65327 N90242 N65118 N95121 N60159 N72064 N110099 N15268 N107680 | 28 15 46 7 143 54 55 246 58 1440 2 327 82 40 7 76 80 929 230 1281 817 128 418 | 6/14/2019 6:46:20 AM#TAB#6/14/2019 6:47:06 AM#TAB#6/14/2019 6:48:47 AM#TAB#6/14/2019 6:53:58 AM#TAB#6/14/2019 7:37:31 AM#TAB#6/14/2019 8:32:02 AM#TAB#6/14/2019 2:46:57 PM#TAB#6/14/2019 2:47:39 PM#TAB#6/17/2019 6:53:17 AM#TAB#6/17/2019 7:06:30 AM#TAB#6/17/2019 8:15:01 AM#TAB#6/17/2019 8:15:11 AM#TAB#6/17/2019 8:21:05 AM#TAB#6/17/2019 9:03:05 AM#TAB#6/17/2019 9:08:02 AM#TAB#6/17/2019 9:10:57 AM#TAB#6/17/2019 9:18:37 AM#TAB#6/17/2019 12:06:18 PM#TAB#6/17/2019 12:11:27 PM#TAB#6/17/2019 12:12:31 PM#TAB#6/17/2019 1:14:47 PM#TAB#6/17/2019 1:24:45 PM#TAB#6/17/2019 1:27:48 PM | N108433 | N121719 N90094 N11341 N51008 N79662 N86089 N122770 N31391 N11804 N112765 N103211 N42583 N101671 N104279 N59188 N105649 N88483 N62585 N96294 N100773 N61448 N15761 N69333 N10028 N12695 N18071 N104663 | 6/18/2019 2:23:15 PM | 6/18/2019 2:30:36 PM | 20 |
U174179 | N38780 N30664 N86089 N90425 N59544 N92972 N95092 N121044 N16976 N121414 N82910 N26275 N80822 N105712 N49403 N87370 N66396 N122296 N38987 N63734 N81849 N10686 N41112 N66631 N49558 N116169 N68462 N24617 N53793 N51752 N77320 N58235 N52394 | 32 240 76 148 81 98 219 163 162 123 164 228 176 114 62 369 91 14 77 87 137 5 64 19 287 238 55 10 16 140 806 190 0 | 6/18/2019 6:58:32 AM#TAB#6/18/2019 6:59:10 AM#TAB#6/18/2019 12:54:11 PM#TAB#6/18/2019 12:55:28 PM#TAB#6/19/2019 7:03:54 AM#TAB#6/19/2019 10:18:59 AM#TAB#6/20/2019 7:24:33 AM#TAB#6/21/2019 9:22:36 AM#TAB#6/21/2019 9:25:19 AM#TAB#6/21/2019 9:28:02 AM#TAB#6/21/2019 9:36:42 AM#TAB#6/21/2019 9:39:27 AM#TAB#6/21/2019 9:43:15 AM#TAB#6/21/2019 9:46:46 AM#TAB#6/21/2019 9:48:40 AM#TAB#6/21/2019 9:49:43 AM#TAB#6/24/2019 8:09:52 AM#TAB#6/24/2019 8:11:23 AM#TAB#6/25/2019 7:31:24 AM#TAB#6/25/2019 11:57:31 AM#TAB#6/25/2019 11:58:58 AM#TAB#6/25/2019 12:01:16 PM#TAB#6/25/2019 12:01:22 PM#TAB#6/25/2019 12:02:26 PM#TAB#6/25/2019 12:04:30 PM#TAB#6/26/2019 7:11:45 AM#TAB#6/26/2019 7:15:50 AM#TAB#6/26/2019 7:22:01 AM#TAB#6/27/2019 6:00:42 AM#TAB#6/27/2019 6:01:27 AM#TAB#6/27/2019 6:04:03 AM#TAB#6/27/2019 7:23:46 AM#TAB#6/27/2019 9:21:23 AM | N79115 | N37853 N68344 N99978 N119481 N86042 N13223 N114030 N54268 N107638 N36828 N89649 N67404 N46272 N49046 N93339 N71947 N98455 N101672 N80304 N49284 | 7/2/2019 7:43:35 AM | 7/2/2019 8:24:12 AM | 546 |
U397787 | N102938 N44139 N110993 N84898 N50892 N42970 N120709 N29737 N65329 N105956 | 49 30 67 4 525 12 80 145 853 50 | 6/18/2019 7:17:40 PM#TAB#6/22/2019 8:53:16 AM#TAB#6/22/2019 8:54:15 AM#TAB#6/22/2019 8:58:21 AM#TAB#6/22/2019 8:58:24 AM#TAB#6/22/2019 9:08:08 AM#TAB#6/22/2019 9:08:11 AM#TAB#6/23/2019 12:13:30 PM#TAB#6/23/2019 2:07:54 PM#TAB#6/23/2019 3:54:44 PM | N59937 N11182 | N24820 N83589 N72885 N58210 N103594 N67842 N48360 N65174 N76977 N18417 N63867 N67971 N76163 N55633 N53116 N24424 N96733 N41216 N21021 N28601 | 6/30/2019 2:18:19 PM | 6/30/2019 2:22:03 PM | 62 41 |
U257982 | N50928 N70471 N93998 N35054 | 602 44 31 18 | 6/14/2019 8:06:38 AM#TAB#6/18/2019 7:19:24 AM#TAB#6/25/2019 8:08:56 AM#TAB#6/25/2019 8:09:01 AM | N53784 | N119254 N18926 N55263 N13688 N105845 N116744 N96955 N102082 N104861 N47094 N122639 N47127 N45571 N122242 N35242 N60588 N54692 N22109 N33322 N84362 | 6/27/2019 11:12:40 AM | 6/27/2019 11:44:38 AM | 122 |
U210765 | N95202 N121334 N58113 N84162 N90558 N87652 N17024 N85220 N20045 N112008 N90242 N41168 N65974 N14961 N90215 N62632 N68652 N78202 N94044 N120253 N65118 N92115 N48113 N112765 N103099 N42583 N21782 N104096 N62402 N14466 N48341 N87223 N101051 N111241 N51515 | 80 92 612 256 116 48 83 53 96 103 29 72 50 19 322 75 45 74 50 51 33 70 66 231 5 130 80 59 94 69 71 34 59 129 177 | 6/14/2019 12:09:45 PM#TAB#6/14/2019 12:11:13 PM#TAB#6/14/2019 12:12:46 PM#TAB#6/14/2019 1:33:06 PM#TAB#6/14/2019 1:52:54 PM#TAB#6/14/2019 1:55:29 PM#TAB#6/14/2019 1:57:59 PM#TAB#6/14/2019 1:59:31 PM#TAB#6/14/2019 2:00:25 PM#TAB#6/14/2019 2:00:44 PM#TAB#6/17/2019 7:12:32 AM#TAB#6/17/2019 7:13:22 AM#TAB#6/17/2019 7:14:48 AM#TAB#6/17/2019 11:40:23 AM#TAB#6/17/2019 11:40:50 AM#TAB#6/17/2019 11:46:55 AM#TAB#6/17/2019 11:48:57 AM#TAB#6/17/2019 12:29:13 PM#TAB#6/18/2019 12:34:10 PM#TAB#6/18/2019 12:35:30 PM#TAB#6/18/2019 12:36:36 PM#TAB#6/18/2019 12:52:30 PM#TAB#6/18/2019 1:11:07 PM#TAB#6/18/2019 1:12:00 PM#TAB#6/18/2019 1:17:33 PM#TAB#6/18/2019 1:51:08 PM#TAB#6/19/2019 12:35:05 PM#TAB#6/19/2019 12:36:32 PM#TAB#6/19/2019 12:37:51 PM#TAB#6/19/2019 12:39:37 PM#TAB#6/19/2019 12:43:14 PM#TAB#6/19/2019 12:44:39 PM#TAB#6/19/2019 12:45:27 PM#TAB#6/19/2019 12:49:58 PM#TAB#6/19/2019 1:47:11 PM | N80819 | N81145 N108781 N78798 N74429 N68456 N89085 N114715 N109436 N18319 N56128 N88313 N101623 N20113 N28953 | 6/21/2019 6:47:20 AM | 6/21/2019 6:48:33 AM | 44 |
U344051 | N43178 N55055 N74186 | 6 89 22 | 6/15/2019 5:06:19 PM#TAB#6/15/2019 5:07:15 PM#TAB#6/15/2019 5:18:37 PM | N67437 | N74784 N66052 N65076 N68856 N17039 N111024 N108946 N73098 N29771 N67259 N97167 N34856 N117525 N27401 | 6/20/2019 3:36:48 PM | 6/20/2019 3:47:43 PM | 102 |
U124839 | N105994 N80996 N87523 N17355 N84319 N41288 N121334 N58811 N91273 N62749 N95202 N100509 N43119 N36905 N94432 N26659 | 124 57 64 54 76 16 86 20 13 438 119 102 36 56 9 983 | 6/14/2019 4:50:36 AM#TAB#6/14/2019 4:53:45 AM#TAB#6/14/2019 8:53:15 AM#TAB#6/14/2019 8:56:21 AM#TAB#6/14/2019 8:58:01 AM#TAB#6/14/2019 9:00:50 AM#TAB#6/14/2019 9:01:46 AM#TAB#6/14/2019 9:03:30 AM#TAB#6/14/2019 9:12:58 AM#TAB#6/14/2019 9:21:24 AM#TAB#6/14/2019 11:56:36 AM#TAB#6/14/2019 11:58:54 AM#TAB#6/14/2019 12:01:18 PM#TAB#6/14/2019 12:02:21 PM#TAB#6/14/2019 12:05:09 PM#TAB#6/14/2019 12:05:26 PM | N85531 | N30687 N51909 N94579 N21590 N57729 N39572 N79595 N41447 N13696 N90376 N93306 N94053 N107590 N12607 N44283 N12470 N25898 N110213 N119877 N102515 N11462 N106552 N37568 N51100 N87223 N20802 N79960 N13725 N23681 N68307 N98363 N32910 N66045 N95501 N17723 N75100 N35080 N92065 N13555 N28113 N104663 | 6/18/2019 5:03:17 AM | 6/18/2019 5:19:19 AM | 35 |
U103175 | N38122 N116802 N85471 N94241 N108781 N17686 | 59 147 91 243 265 1143 | 6/18/2019 12:38:02 PM#TAB#6/19/2019 2:42:25 PM#TAB#6/19/2019 2:44:52 PM#TAB#6/20/2019 1:39:15 PM#TAB#6/21/2019 10:48:49 AM#TAB#6/24/2019 1:11:29 PM | N87543 | N121554 N49680 N40229 N47332 N50061 N49693 N80140 N111084 N98027 N24324 N67814 N84918 N28753 N116670 N76586 N63734 N107048 N101112 N36169 N67207 N92581 N62716 N44266 N60508 N33474 N54725 N122431 N105381 | 6/25/2019 1:22:57 PM | 6/25/2019 1:30:31 PM | 180 |
U299014 | N37463 N76869 N47697 N103854 N121699 N32113 N93382 N14376 N78688 N121200 N63789 N123552 N54647 N75009 N89847 N104663 N53158 N97051 N39572 N18385 N59169 N75583 N90147 N79787 N89796 N115110 N41399 N43455 N70298 N50599 N11431 N42359 N111857 N42107 | 28 15 45 115 177 41 28 45 44 80 4 42 153 137 5 43 145 4 31 267 204 35 80 118 75 124 122 331 170 3 45 82 109 37 | 6/13/2019 6:09:22 PM#TAB#6/13/2019 6:09:28 PM#TAB#6/14/2019 6:37:43 AM#TAB#6/14/2019 1:21:16 PM#TAB#6/15/2019 6:55:23 AM#TAB#6/15/2019 7:04:52 AM#TAB#6/15/2019 7:08:14 AM#TAB#6/15/2019 7:14:52 AM#TAB#6/15/2019 7:37:43 AM#TAB#6/16/2019 7:06:03 AM#TAB#6/16/2019 7:13:29 AM#TAB#6/16/2019 7:13:33 AM#TAB#6/18/2019 5:52:56 AM#TAB#6/18/2019 6:07:32 AM#TAB#6/18/2019 1:35:09 PM#TAB#6/18/2019 1:35:15 PM#TAB#6/18/2019 1:36:27 PM#TAB#6/18/2019 1:44:03 PM#TAB#6/18/2019 1:44:08 PM#TAB#6/20/2019 6:08:59 AM#TAB#6/21/2019 12:21:42 PM#TAB#6/21/2019 1:36:43 PM#TAB#6/22/2019 6:16:06 PM#TAB#6/23/2019 4:57:30 AM#TAB#6/23/2019 8:56:16 AM#TAB#6/24/2019 6:15:02 AM#TAB#6/24/2019 11:15:57 AM#TAB#6/24/2019 11:18:19 AM#TAB#6/24/2019 11:24:02 AM#TAB#6/24/2019 11:34:15 AM#TAB#6/24/2019 11:34:18 AM#TAB#6/24/2019 4:09:32 PM#TAB#6/25/2019 6:50:12 AM#TAB#6/26/2019 7:10:16 AM | N105772 N100589 | N120447 N24244 N105855 N79041 N86318 N75652 N14882 N77334 N24335 N55895 N62314 N115498 N14678 N81496 N120909 N107573 N51773 N55076 N43695 N63481 N50689 N121294 N94628 N50435 N87898 N96567 N56474 N54940 | 6/28/2019 1:17:05 PM | 6/28/2019 1:22:07 PM | 95 106 |
U350237 | N18363 N43119 N122382 N79391 N86113 N63636 N44108 N118320 N112032 N81726 N85531 N94044 N100679 N105649 N120253 N95251 N78424 N82032 N99572 N27093 N89400 N42592 N48858 N101051 N61629 N69542 N68999 N83241 N75943 N104003 N110194 N23948 N103617 N26402 N110319 | 103 30 11 932 29 23 10 27 655 26 9 67 1517 24 131 107 617 27 151 12 260 21 2378 22 46 240 357 249 17 135 186 698 31 1091 561 | 6/14/2019 6:13:32 AM#TAB#6/14/2019 9:38:16 AM#TAB#6/17/2019 6:15:53 AM#TAB#6/17/2019 6:28:44 AM#TAB#6/17/2019 6:44:47 AM#TAB#6/17/2019 6:45:53 AM#TAB#6/17/2019 9:40:52 AM#TAB#6/17/2019 10:11:30 AM#TAB#6/17/2019 11:39:21 AM#TAB#6/17/2019 11:52:24 AM#TAB#6/18/2019 5:16:12 AM#TAB#6/18/2019 7:00:15 AM#TAB#6/18/2019 7:34:07 AM#TAB#6/18/2019 12:30:07 PM#TAB#6/18/2019 1:09:49 PM#TAB#6/19/2019 7:20:04 AM#TAB#6/19/2019 7:43:05 AM#TAB#6/19/2019 10:51:12 AM#TAB#6/19/2019 11:10:52 AM#TAB#6/19/2019 11:51:45 AM#TAB#6/19/2019 12:15:35 PM#TAB#6/19/2019 12:20:08 PM#TAB#6/19/2019 12:26:58 PM#TAB#6/19/2019 3:01:57 PM#TAB#6/20/2019 5:08:35 AM#TAB#6/20/2019 7:18:12 AM#TAB#6/20/2019 7:22:27 AM#TAB#6/20/2019 8:22:39 AM#TAB#6/20/2019 2:23:14 PM#TAB#6/21/2019 5:53:55 AM#TAB#6/21/2019 8:53:25 AM#TAB#6/21/2019 8:57:17 AM#TAB#6/24/2019 5:28:53 AM#TAB#6/24/2019 5:29:25 AM#TAB#6/24/2019 5:47:58 AM | N93998 | N63458 N68125 N49680 N116989 N47590 N81302 N95546 N71031 N69937 N74276 N25251 N24100 N78447 N52760 N122091 N111857 N80140 N111084 N79855 N95738 N84101 N119720 N74036 N86656 N68188 N38774 N26047 N48809 N93164 N72237 N107372 N24324 N102626 N112738 N87272 N36468 N38862 N116770 N52378 N51376 N107357 N69103 N91931 N107048 N72238 N83507 | 6/25/2019 7:01:20 AM | 6/25/2019 8:48:34 AM | 831 |
U214322 | N89255 N95202 | 1328 436 | 6/14/2019 6:51:52 AM#TAB#6/14/2019 10:24:20 AM | N119833 | N61154 N15124 N114938 N115495 N65974 N51924 N78202 N59685 N14961 N81783 N42055 N109041 N17790 N103844 N50223 N118951 N25370 N88470 N65118 N75333 N81726 N90215 N118320 N87417 N47696 N115534 N98288 | 6/17/2019 11:34:29 AM | 6/17/2019 11:41:29 AM | 261 |
U290629 | N72931 N78965 N90242 N79842 N42055 N75910 N63119 N26275 N88841 N103887 N13203 N104304 N67688 N36730 N68298 N79391 N62746 N93019 N57235 N90825 N25370 N70426 N116839 N115311 N44987 N29409 N89663 N105905 N13879 N36897 N34916 N14452 N65974 N37252 N81726 N84050 N102498 N104892 N62632 N10083 N115495 N31693 N65623 N54627 N38318 N102284 N85806 N109222 N90215 N38517 N88490 N11637 N100569 N108135 N36166 N13696 N119877 N56372 N30664 N44283 N40222 N26279 N94400 N18402 N31687 N80907 N17723 N62593 N70471 N46755 N53943 N48582 N56855 N90425 N95941 N63185 N92024 N23681 N49054 N35069 N90431 N101671 N88483 N123665 N51008 N88434 N59544 N60476 N56109 N19160 N95829 N88929 N104096 N109636 N55139 N57109 N95120 N95772 N42583 N10856 N52234 N120253 N90094 N43420 N115129 N92972 N67163 N56386 N98456 N106098 N95007 N79600 N101051 N80645 N48341 N18457 N44759 N74743 N105611 N20031 N31778 N47528 N83046 N77212 N97851 N22108 N39306 N47779 N100653 N91454 N29105 N56664 N31487 N73098 N84255 N14480 N41179 N53158 N96234 N61223 N50023 N75943 N44812 N19230 N65076 N81145 N37742 N72126 N114715 N84809 N105712 N80819 N18319 N108781 N28149 N33346 N82663 N46170 N62799 N16262 N25043 N86648 N11595 N70031 N86048 N22083 N30996 N61495 N123294 N105279 N79757 N77463 N72108 N61808 N71569 N51557 N17361 N72057 N51832 N71207 N41397 N13892 N88971 N77291 N90169 N88888 N30442 N71915 N70388 N20440 N24548 N121202 N20329 N56158 N16357 N72375 N75503 N111506 N17979 N11209 N88272 N66396 N90517 N53291 N43384 N93719 N68265 N70298 N38133 N114664 N46197 N17686 N30054 N100613 N43455 N63041 N76208 N97870 N64326 N41621 N42449 N58061 N115883 N57305 N112727 N48002 N76598 N55766 N21685 N100777 N78899 N59938 N108973 N14080 N28235 N78447 N69103 N49558 N88231 N35210 N75354 N18133 N58447 N111084 N52760 N119787 N76217 N26774 N76253 N111918 N41158 N38420 N44063 N11139 N116731 N107890 N66946 N107048 N22924 N76586 N70300 N116670 N38720 N86664 N87272 N47735 N63734 N90005 N50061 N69094 N117416 N101112 N93635 N55003 N104446 N115929 N12827 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Dataset Card for PENS: PErsonalized News headlineS
PENS is an English dataset for Personalized News Headline Generation Research. It contains two parts for training and test individually. The training set was collected from anonymized user impressions logs of Microsoft News website, and the test set is manually-created by hundreds of native speakers to enable a fair testbed for evaluating models in an offline mode.
PENS contains about 113k English news articles whose topics are distributed into 15 categories and 500k impression logs generated by over 445k users for training. In detail, every news article contains rich textual content including title, body, category and corresponding entities. Each impression log contains the click events, non-clicked events and historical news click behaviors of this user before this impression. To provide an offline testbed, we invited 103 English native speakers to manually create a test set by two stages. In detail, there are over 100k personalized news headlines generated.
Notice that each user was de-linked from the production system when securely hashed into an anonymized ID to protect user privacy. For more detailed information about the PENS dataset, you can refer to the following paper: PENS: A Dataset and Generic Framework for Personalized News Headline Generation.
Dataset Details
Dataset Description
PENS (PErsonalized News headlineS) is an English dataset tailored for Personalized News Headline Generation research. The dataset is divided into training and test sets to support both model development and evaluation.
Training Set: Collected from anonymized user impression logs of the Microsoft News website, containing approximately 113k English news articles across 15 categories and 500k impression logs from over 445k users. Each news article includes a title, body, category, and associated entities. Impression logs detail click events, non-click events, and historical news click behaviors of users prior to each impression.
Test Set: Manually created by 103 native English speakers to provide a fair and reliable benchmark for offline model evaluation. This set includes over 100k personalized news headlines generated through a two-stage annotation process.
PENS ensures user privacy by de-linking user data from the production system and anonymizing user IDs through secure hashing.
- Curated by: Microsoft Research
- Shared by [optional]: Microsoft Research
- Language(s) (NLP): English
- License: Microsoft Research License Terms
Dataset Sources [optional]
- Repository: PENS Dataset Repository
- Paper: Link to the PENS Paper
- Related Works: Available Related Works on PENS
Dataset Structure
PENS consists of two main components: the training set and the test set.
Training Set:
- News Articles: ~113,762 entries
- Fields:
News ID
: Unique identifier for each news article (e.g., N10000).Category
: One of 15 predefined categories (e.g., sports).Topic
: Specific topic within the category (e.g., soccer).Headline
: The title of the news article.News body
: The full content of the news article.Title entity
: JSON mapping between phrases in the title and their corresponding entities in WikiData.Entity content
: Detailed information about each entity from WikiData.
- Fields:
- Impression Logs: 500,000 entries
- Fields:
UserID
: Anonymous identifier for the user (e.g., U335175).ClicknewsID
: Space-separated list of historically clicked news IDs.Dwelltime
: Space-separated list of browsing durations for each clicked news.Exposure_time
: Space-separated list of exposure times for historical clicked news (separated by#TAB#
).Pos
: Space-separated list of clicked news IDs in the current impression.Neg
: Space-separated list of unclicked news IDs in the current impression.Start
: Start time of the impression (e.g., 7/3/2019 6:43:49 AM).End
: End time of the impression (e.g., 7/3/2019 7:06:06 AM).Dwelltime_pos
: Space-separated list of browsing durations for clicked news in the current impression.
- Fields:
- News Articles: ~113,762 entries
Validation Set:
- Structured similarly to the training set, used for validating model performance during training.
Test Set:
- Personalized Headlines: >100,000 entries
- Fields:
userid
: Unique identifier for each of the 103 test users (e.g., NT1).clicknewsID
: Comma-separated list of historically clicked news IDs from the first stage.posnewID
: Comma-separated list of exhibited news IDs in the second stage.rewrite_titles
: Space-separated list of manually written personalized headlines (separated by#TAB#
).
- Fields:
- Personalized Headlines: >100,000 entries
The dataset is structured to facilitate the mapping between user behaviors and personalized headline generation.
Uses
Direct Use
PENS is intended for research in personalized news headline generation. Suitable use cases include:
- Developing models that generate personalized news headlines based on user interaction data.
- Evaluating the effectiveness of personalization algorithms in news dissemination.
- Analyzing user behavior and preferences in the context of news consumption.
Out-of-Scope Use
PENS is not suitable for:
- Commercial use without explicit permission from Microsoft Research.
- Applications requiring real-time data updates, as the dataset is static.
- Tasks unrelated to text generation or summarization, such as image processing.
Dataset Structure
PENS consists of two main components: the training set and the test set.
Training Set:
- News Articles: ~113k entries
- Fields:
title
,body
,category
,entities
- Fields:
- Impression Logs: ~500k entries
- Fields:
click_events
,non_click_events
,historical_click_behaviors
- Fields:
- News Articles: ~113k entries
Test Set:
- Personalized Headlines: >100k entries
- Fields:
original_article
,personalized_headline
,user_id
- Fields:
- Personalized Headlines: >100k entries
The dataset is structured to facilitate the mapping between user behaviors and personalized headline generation.
Dataset Creation
Curation Rationale
The PENS dataset was created to address the need for comprehensive resources in the domain of personalized news headline generation. By leveraging real user interaction data and manually curated headlines, PENS provides a robust foundation for developing models that can tailor news content to individual preferences.
Source Data
Data Collection and Processing
Training Data: Extracted from anonymized user impression logs of the Microsoft News website. The data includes detailed user interactions with news articles, ensuring a rich source of information for personalization.
Test Data: Generated through a two-stage manual annotation process involving 103 native English speakers. This ensures high-quality, personalized headlines for reliable evaluation.
Who are the source data producers?
Training Data Producers: Users of the Microsoft News website whose interactions were anonymized and aggregated.
Test Data Producers: 103 native English speakers recruited to generate personalized headlines.
Annotations [optional]
Annotation process
The test set annotations were created in two stages:
- Initial Generation: Annotators generated personalized headlines based on provided news articles and user interaction histories.
- Validation: A secondary review ensured consistency and quality of the generated headlines.
Who are the annotators?
The annotators are 103 native English speakers with expertise in journalism and content creation, ensuring that the personalized headlines are both accurate and engaging.
Personal and Sensitive Information
PENS contains anonymized user interaction data. User IDs are securely hashed to prevent re-identification, and no personal, sensitive, or private information is included in the dataset.
Bias, Risks, and Limitations
PENS may contain inherent biases present in user interaction data, such as demographic or preference-based biases. Additionally, the dataset is limited to English-language news and may not generalize to other languages or cultural contexts.
Recommendations
Users should critically assess the dataset for potential biases and ensure that models trained on PENS are evaluated for fairness and representativeness. It's recommended to complement PENS with additional data sources to mitigate identified biases.
Citation
BibTeX:
@inproceedings{pens2024,
title={PENS: PErsonalized News headlineS for Personalized News Headline Generation},
author={Author Names},
booktitle={Proceedings of the Conference on Natural Language Processing},
year={2024},
organization={Microsoft Research}
}
APA: Ao, X., Wang, X., Luo, L., Qiao, Y., He, Q., & Xie, X. (2021, August). PENS: A dataset and generic framework for personalized news headline generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 82-92).
Glossary
- Impression Log: A record of a user's interaction with news articles, including clicks and non-clicks.
- Personalized Headline: A news headline tailored to an individual user's preferences and interaction history.
More Information
Put Your Voice on Stage: Personalized Headline Generation for News Articles
Fact-Preserved Personalized News Headline Generation
Dataset Card Contact
For any inquiries or feedback regarding the PENS dataset, please contact aoxiang@ict.ac.cn.
Download
The PENS dataset is available for free download for research purposes under the Microsoft Research License Terms. Please ensure you have read and agree to the license terms before downloading.
License
The PENS dataset is distributed under the Microsoft Research License Terms. Please review the license agreement before using the dataset.
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