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ืื ืืกืฃ, ืื ืืืื ืืจืื ืกืืื ืืฆืืจ ืื ืืื, ืืืืคืงืื ืืกืืืื ืืืื, ื ืืชื ืืืืก ืืืชื ืืืืืกื ืืื ืืช ืืืื ืฆืืจื ืื ืืงืื ืืืฉ | wikipedia | 0 | ืืืืก | null |
ืืืจืชื ืืืืชื ืืืฆืข ืชืืืื ืืืืืก ืืขืื ืืืชืจ, ืืืืืื ืฉืืื ืงืฉื ืืืชืจ ืืืืก ืืขืืจืช ืืื ืืจืืขืืช ืืืืืื | wikipedia | 0 | ืืืืก | null |
ืืืกืืจ ืืืืกื, ืืืืื ืืชืกืคืืจืช ืืืืื ืืกืจื ืืืืก, ืืืื ืืืืกืชืคืจ ืืืื ืืืืขื | wikipedia | 0 | ืืืืก | null |
ืืืื, ืืืกืืจืืช ืืืืจืื ืืืืก ืืช ืืื ืืืกืืจืื ืืืืจืื ืฉืืื ืืืืืืืื ืืฆืืื | wikipedia | 0 | ืืืืก | null |
ืืื ื ืืืืืืช ืขื ืืืืื ืืืื ืชืฉืขื ืืืกืืจืื: ืืืกืชืคืจ ืืืืก ืืืืื ืืืชืจืืฅ ืืกืื ืืช ืืืืฃ ืืฉืื ืืืืจืช ืื ืื ืชืฉืืืฉ ืืืืื ืื ืขืื ื ืขืืืื ืืขืืจ ืืขืฉืืช ืืืืื ืืืืื ืชืืจื ืืืกืืจ ืฉืืื ืืื ืื ืืืื ืขื ืืืื ืฉื ื ืืืืืื: ืืืคืื (ืืืฉืื ืืืืื: ืืืคืืช) ืืช ืื ืืืืืืช ืืืืช ืืขืืืฃ ืืช ืืืืฃ ืชืกืคืืจืช ืืืงืืจ ืืื ืฉืืืื ืืกืืจ ืืชืกืคืืจืช ืืื ืืืืืืจื ืฉืืืืืจ ืืฉื ืจืื ื ืืช ืืืขืืจ ืืืืชืืจ ืื ื ืืืจืื, ืืืืช ืืืืื ื ืื ืืืืืืื: | wikipedia | 0 | ืืืืก | null |
ืืื ืื ืฉื ืืื ืืื ืืื ืืกืืจ ืืื ืืื ืืืกืชืคืจ ืื ืืืืก ืืช ืืืืื ืืื ืืื ื ืืืื, ืื ืืืชืจ ืื ืืืืื ืืืืื ืขื ืืืจืื | wikipedia | 0 | ืืืืก | null |
ืืขืชื ืฉื ืืชืืกืคืืช ืืื ืื ืืืื ืืืืจ ืืขืืงืจื ืฉืืื ืืชืืืช ืืืชื ืืืงืื ืฉืืื ืื ื ืืฆืื ืื, ืื ืืื ืืื ืกืืื ืืืืจ ืฉืืฉ ืืชืืืช ืืช ืืืขืื ืืจืฉืืช ืฉืืื ื ืืฆืื ืื, ืืืื ืืฉื ืฉืืฉืืืื ืืืืจืชื ืืื ืืืืืจ ืืืฉืืื ื ืืฆื ืืืื ืื ืื ืืืืื ืืืจ, ืืืฉืืืื ืืื ื ื ืืืืื ืืืจืืฉ ืืืฉืืืืช ืื ืงืืช ืื ืืืืก ืืช ืืืื ืืืขื ื ืฉืืื ื ืืฆื ืืจืฉืืชื, ืืืืืื ืฉืืืื ืจืง ืืืืจ ืฉืืื ืืชืืืช ืืช ืืืขืื ืืืงืื ืฉืืื ืื ื ืืฆืื ืื, ืื ืื ืืื ืืืืืื ืฉืืื ืงืฉืืจื ืืืงืื ืื ืืื ื ืืฆืื | wikipedia | 0 | ืืืืก | null |
ืืฉ ืืืืก ืืืืืืืฃ ืืืื ืขืืืื ืืืืืงื ืืขืืื ืืขืืชืื ืงืจืืืืช, ืืืืืฅ ืืื ืืื | wikipedia | 0 | ืืืืก | null |
ืืืืกื, ืจืืืฆื ืืกืืื ืืืงืืจ ืืื ืฉืืืื ืืกืืจ ืืืืก ืืืืื ืืืจืืืฅ ืืืคื ืืืกืื ืืื ืืืคืกืืง ืฉืืืื ืืืืืื ืฉืืฉ ืืงืืื ืืื ืืืืืช ืืืืืฉืช ืืืืืื ืืื ืืืืืืฃ ืืืืืื ืืืฉืื ืืืืืืฆืื | wikipedia | 0 | ืืืืก | null |
ืืืืกื: ืืฉืื ืฉืจืื ืืืจืืช ืืฉืืจืืช ืืืืื ืืื ื ืขืจืืืืช ืืงืืืืช ืื ืืช ืืืืืืช ืืขืืืชืืช ืืกืืืขืืช ืืขืืจื ืืืืืืช ืื ืืฆืืืช ืืขื ื ืืืงืื ืฉืืื ืืช ืืืืื ืืืืก ืืื ืืช ืืืืืื | wikipedia | 0 | ืืืืก | null |
ืืืื ืืืื ืืชืืคืืื ืื ืืขืจื ืืืื ืืืื (ืืกืืกื) ืืขืฉืืจืื ืืืืืื ืืืืืืช ื ืชืจื ืคืืืชื, ืืืฉืืฉืื ืืืืืจื ืืืืื ืื ืืงืื (ื ืืชื ืืืืก ืืืืื ืืืืื ืืืื ืืื ืฆืืจื ืืกืืื) | wikipedia | 0 | ืืืืก | null |
ืืืืืจืื ืืืืื ืืฉืืชืคืื ืืืฆืจ ืจืฉืืื ืืื ืืข ืื ืืื ืฉืืืืฉ ืืืฆืจ ืืฆืจืืื ืฉืื ืื, ืืื ืืื ื ืืืืืื ืืื ืืข ืื ืืฉืื ืืช ืืฉืืชืคืืช ืืืฆืจ ืืืืก ืื, ืื ืืื ืื ืืืืื ืฉื ืื ืืช ืืฉืจืื ืืืืก ืืจืฉืืช ืืจืืื | wikipedia | 0 | ืืืืก | null |
ื ืืืกืืงื ืืจืื ืืืืฃ ืืื ืขื ืื ืืช ืืืืืืชื ืืืืจื ืืืืก ืืช ืืืืกืช ืืืจืืื | wikipedia | 0 | ืืืืก | null |
ื ืืกืจ ืขื ืืืฆืืจืข ืืฉืืื ืืฉืืื ืื ืืื, ืืื ื ืืกืจ ืขืืื ืืืืก ืืืืกืชืคืจ ืืคืืื ืืืืื ืฉืืช ืืืื ืืื | wikipedia | 0 | ืืืืก | null |
ืชืคืงืืื ืืืืจ ืขื ืืงืืช ืืงืืืฆืืช ืืจืืฉืื ืืช ืืื ืื ืืื ืจืช ืกืืจืื ืชืืืื ืืืืจืื ืืงืื ื ืฉืื ืืืืจืืช ืฉืืืช-ืืขืื ืืจืง ืฉืืืชืช ืื ืฉืื ืฉืืคืกืืงื ืืืืก ืืืืฉื ืืื ืืืจืืื ืืืชื ืืงืืื ืืืืจืืช | wikipedia | 0 | ืืืืก | null |
(ืืืฉื: ืืื ืื ืืชืื ืฉืืกืืจ ืืืืก ืืงื ืืื ืืื ืืืืื ืช ืืืืกื? ืืื ืื ืืชืื ืฉืืกืืจ ืืื ืื ืขื ืคืืืคืื ื ืืืจ ืืืืืืช ืืฉืขื ืฉืืฉ ืืกืืื ืืืง ืืืืืจื ื ืคืฅ?) | wikipedia | 0 | ืืืืก | null |
ืืืื ืืืจืช ืืกืคืจืช ืื ืืื ืืืจืชื ืืืืืกื ืฉืื ืืืืก ืจืง ืืช ืืืืงืื ืืืืืืืืื ืืืืชืจ ืืฉืืืชื ืืื ืืืกืื ืืกืฃ ืขื ืกืืื | wikipedia | 0 | ืืืืก | null |
ืืืื ื ืืขืฆืืื ืืกืจืืช ืืืชืืชื ืขื ืืื ืืืืื ื ืืืืืจืช ืื ืืื ืืจืฆืื ื ืืืฉื, ืืืืก ืืืฉืืฉ ืืชืืฉืื ืืืืฉืฃ ืืืงื ืืืืืืขืจ | wikipedia | 0 | ืืืืก | null |
ืืื ื ืืฉ ืฆืืจื ืืืืก ืืช ืืืืืช ืืื ืคืขื, ืื ืืืืืกื ืืจืืืืช ื ืืชื ืืืกืื ืืืืฆืืืช | wikipedia | 0 | ืืืืก | null |
ืืืืชื ืืืืฉ ืืืืื ืคืืืืช ืขืืืื ืืืืืืช ืฉื 20 ืขื 40 ื ืฉืื ืขื ืื ืช ืืืืื, ืืืืก ืืืชืงื ืืช ืืืืืื | wikipedia | 0 | ืืืืก | null |
ืืืืขืืจ ืืคืฉืจืืช ืืืืก ืืืืื ืืื ืืืื ืืืืืื ืืืื ืืืืช ืืจืืื ืืกืชืืืื ืืืืืื ืืืืืืืื | wikipedia | 0 | ืืืืก | null |
ืืืืื ืืืคืืื ืกืืจืื ืืืืืง ืืช ืืืืื ืฉืขืื ืืจืฉืืชื ืขื ืืจืขืืื ืืืื ืื ืืฉืชืืฉื ืืืื ืืืขืืื ืืื ืืืืก ืืช ืืืืื ืืขืื ืื ืืกืขืื ืืืืขืื ืืฆืื | wikipedia | 0 | ืืืืก | null |
ืืืืื ืืกืคืจ ืฉืืื ืืจื ืืืืืืื ืืกืคืจ ืืืชืืื ืฉืืื ืืื ืืืงืฉ (ืืฉื ืืืื) ืืืืก ืืช ืืืืกืชื, ืื ืืืืืื ืืื ื ืืงืืืืช ืืช ืืืงืฉื | wikipedia | 0 | ืืืืก | null |
ื'ืืืื ืืชืงืฉื ืื ืงืืช, ืืืืก ืืืืืื ืืืื ืืืืืื ืฉืืืืช ืืืฉื ืกืืื ืืืื ืื ืืืื ื ืืืืืจ ืืืฉืื | wikipedia | 0 | ืืืืก | null |
ืื ืฉื ืงืืจืื ืืืืช ืฉืืืจ ืืืื ืก ืืืื ืืฆืืจื ืืจืืงืชื ืขื ืืืืื, ืืืืจื ื ืืจืง ืขื ืืืืื ื ืืชื ืขื ืืื ืืืฉืื ืฉืจืืื ืืงืื ืืืืื (ืืื ืืืืงื ืืื ืืืื ื) - ืืฆืืื ืืืืก ืืช ืืืื ืืชืืืื ืืขืืจื, ืื ืฉืืื ืืฉืืจ ืืืชืื ืืืงืืฉ ืืื ืืืฆื ืืื ื | wikipedia | 0 | ืืืืก | null |
ืื ืืื ืคืืก ืืชืืืจ ืืขืืื ืืืืืกื ืฉื ืืืืจ ืืช ืื ืืื ืืืืจ ืฉืืืืขื ืืืืก ืืืงืื | wikipedia | 0 | ืืืืก | null |
ืืื ืืืื ืืืืืงืฆืขืื, ืฉืื ืืืื ืืืฉืื ืขื ืื ืฉืืืขื ืืืืืื ืื ืงื ืืงืืืช ืืฉืื ืื ืืืช, ืืื ืจืืืืื ืืืืื ืืืชื ืขื ืืคืคืืช, ืื ืื ืืืื ืืืืก ืืช ืืคืคืืชืืื ืืฉื ืืฉืืืชื - ืื ืฉืืจื ืืืจืืื ืืืืืจืืช ืื ืงื ืืงืืืช ืืื ืื ืืืื | wikipedia | 0 | ืืืืก | null |
ืืืืืืื ืืื ืืืืื ืืืืจื ืืงืืืฆื ืืื ืืืื ืกืืขืจ, ืืขื ืืืช ืืชืื ืืจืฅ ืืืืืจืื ืืชืื: ืืื ืื ื ืจืืฆืืช ืจืง ืืขืืื ืืืืช ืืืืืืช ืืคืืืืืช, ืืจืง ืืืืก ืืืชืงื.. | wikipedia | 0 | ืืืืก | null |
ืืืกืจืื ืืช ืฉื ืืชืืืืฉืืช ืืจื-ืคืขืืืืช ืื ืืฆืืจื ืืืืก ืืืชื, ืืื ืืฆืืจื ืืฉืืืจ ืืืชื ืืชืืง ืืืขืืื ืืืื ืืืื ืืืจืื ืืืืื ืืื ืืืืืคืืช ืืืืฅ ืืืืช | wikipedia | 0 | ืืืืก | null |
ืืื ืืืืขืช ืืื, ืฉืืืืื ืืฉ ืจืง ืืฆืืืช ืืืืืฆื ืืืืฉืจ ืฆืจืื ืืืืก ืืืชื ืืื ืื ืฉืื ืืขืจืืช ืืืืื ืืืจืช ืืืืืคื, ืื ืชืืืืชื ืืื ืืื ืืฉืชืืืช, ืืื ืื ืฉืืื ืืืชื ืืื ืืืชื ืืฆืืืืช ืืืืื, ืคืกืืงื ืืืงืืจืชืืช, ืืืืืืื ืืืืื ืืคืืืขืช ืื | wikipedia | 0 | ืืืืก | null |
ืืื ืกืืคืจื ืฉืืื ืืืื ืืก ืืช ืืชื ืืื ืืื ืขืืื ืืืชื ืื ืืืืก ืืช ืืืื ืื ืืืืืื ืฉืื, ืืฉืื ืฉืื ืืืืชื ืืืืื ืืขืืื ืืชืฉืืื ืื ืืจืฉ ืืืืืจืื | wikipedia | 0 | ืืืืก | null |
ืืื ืขืืืื ืืืฉื ืืืื ืืืจืื ืื ืฉืื - ืืื ืืฆืจืืื, ืืืืก, ืื ืงืืช ืืืืจืื ืืช ืื ืืฉืืจืืชืื - ืืื ืชื ืืื ืืื ืืืืืื ืืื | wikipedia | 0 | ืืืืก | null |
ืืืจืื ืืขื ื ืฉืืืจืืช ืฉืืชืืืืจ ืฉื ืฉืืืืฉ ืืกืืื ืืกืงืก ืืื ืจืืืืืกืื, ืืกืืจ ืืืืก ืืช ืืืืืกื ืืืืืืืืช ืืขืื ื ืื, ืชืืืืจ ืฉืืื ื ืืืขืื ืืืืจื ืืงืืืื ืฉืืืงืฉืื ืืื ื ืขื ืืืืืืช ืืืืจื ืืืืืื ืืขืช ืฉืืืืขืื ื ืืืง ืขื ืืืืืืช ืืืจื ืืงืืืื | wikipedia | 1 | ืืืืก | null |
ืืืืื ืฉืื ืื ืืืืืื, ืืืงืจื ืืื, ืื ืืืื ืืืืื ืืืฆืืช ืืขืืื ืืคื ืฉืืื ืชืืคืกืช ืืช ืืืืช ืฉืื โ ืืกืคืจืืช ืืื ืืืจืชื ืืืืก ืขืืืจ ืืืืืื ืืช ืืืฆืืืืช ืืืื ืคืืกืืช ืื ืืชื ืืช ืืขืืืื ืงื, ืืื ืคืืกืืช ืืืกืืืขืืช ืืืื ืช ืืืฆืืืืช ืืื ืืฉืืช ืฉืืกืืืื | wikipedia | 1 | ืืืืก | null |
null | twitter | 0 | ืืืืก | 1536747267582984192 |
null | twitter | 0 | ืืืืก | 1491420596890406912 |
null | twitter | 0 | ืืืืก | 1576953159003832322 |
null | twitter | 0 | ืืืืก | 1537331311634948096 |
null | twitter | 0 | ืืืืก | 1561059548164771841 |
null | twitter | 0 | ืืืืก | 1538851864497868800 |
null | twitter | 0 | ืืืืก | 1514992693683933190 |
null | twitter | 0 | ืืืืก | 1534927932086489090 |
null | twitter | 0 | ืืืืก | 1538227916189929473 |
null | twitter | 0 | ืืืืก | 1493713224189063173 |
null | twitter | 0 | ืืืืก | 1576093649439363073 |
null | twitter | 0 | ืืืืก | 1525351270483214336 |
null | twitter | 0 | ืืืืก | 1508828477079924752 |
null | twitter | 0 | ืืืืก | 1504482199256506373 |
null | twitter | 0 | ืืืืก | 1538074600730394624 |
null | twitter | 0 | ืืืืก | 1590825129877327873 |
null | twitter | 0 | ืืืืก | 1508798788999294981 |
null | twitter | 0 | ืืืืก | 1508429838595637254 |
null | twitter | 0 | ืืืืก | 1526458769751912448 |
null | twitter | 0 | ืืืืก | 1521238579107840002 |
null | twitter | 0 | ืืืืก | 1510565168517988359 |
null | twitter | 0 | ืืืืก | 1491379457781018626 |
null | twitter | 0 | ืืืืก | 1549768061682679810 |
null | twitter | 0 | ืืืืก | 1519951300154298368 |
null | twitter | 0 | ืืืืก | 1507242233988358144 |
null | twitter | 0 | ืืืืก | 1543844669343383553 |
null | twitter | 0 | ืืืืก | 1547803699891777536 |
null | twitter | 0 | ืืืืก | 1506547423446978560 |
null | twitter | 0 | ืืืืก | 1532449061604933661 |
null | twitter | 0 | ืืืืก | 1503485990941437954 |
null | twitter | 0 | ืืืืก | 1573245313455988738 |
null | twitter | 0 | ืืืืก | 1588449313533067265 |
null | twitter | 0 | ืืืืก | 1552656265758666754 |
null | twitter | 0 | ืืืืก | 1538402481662832640 |
null | twitter | 0 | ืืืืก | 1492777228530294784 |
null | twitter | 0 | ืืืืก | 1587072721074196481 |
null | twitter | 0 | ืืืืก | 1585505643741454336 |
null | twitter | 0 | ืืืืก | 1514249093538795527 |
null | twitter | 0 | ืืืืก | 1555461604237627398 |
null | twitter | 0 | ืืืืก | 1546035121370923008 |
null | twitter | 0 | ืืืืก | 1530141884236156931 |
null | twitter | 0 | ืืืืก | 1560621078631747587 |
null | twitter | 0 | ืืืืก | 1590433395594518528 |
null | twitter | 0 | ืืืืก | 1565630601482362880 |
null | twitter | 0 | ืืืืก | 1510372461682044936 |
null | twitter | 0 | ืืืืก | 1582135208924454912 |
null | twitter | 0 | ืืืืก | 1503594886209097728 |
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null | twitter | 0 | ืืืืก | 1564498666483814400 |
null | twitter | 0 | ืืืืก | 1554435041954091008 |
null | twitter | 0 | ืืืืก | 1494598763939348480 |
null | twitter | 0 | ืืืืก | 1579163419005313025 |
null | twitter | 0 | ืืืืก | 1581597741276286978 |
null | twitter | 0 | ืืืืก | 1566156184729653251 |
null | twitter | 0 | ืืืืก | 1498005184861163520 |
null | twitter | 0 | ืืืืก | 1550922086134210560 |
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null | twitter | 0 | ืืืืก | 1561327427250438144 |
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null | twitter | 0 | ืืืืก | 1582984744463060994 |
null | twitter | 0 | ืืืืก | 1527197102463500289 |
null | twitter | 0 | ืืืืก | 1497510533037178886 |
null | twitter | 0 | ืืืืก | 1527161009244348417 |
null | twitter | 0 | ืืืืก | 1521769049285349377 |
null | twitter | 0 | ืืืืก | 1510351342526738439 |
null | twitter | 0 | ืืืืก | 1576953629898330112 |
Dataset Card for "HebrewMetaphors"
Dataset Summary
A common dataset for text classification task is IMDb. Large Movie Review Dataset. This is a dataset for binary sentiment classification. The first step in our project was to create a Hebrew dataset with an IMDB-like structure but different in that, in addition to the sentences we have, there will also be verb names, and a classification of whether the verb name is literal or metaphorical in the given sentence. Using an API, sentences that contained the verbs we selected were pulled from Twitter and Wikipedia and Prodigy was used to classify them. The accuracy of the classification was confirmed by having two different persons classify each line twice.
Supported Tasks and Leaderboards
Classification
Languages
Hebrew
Dataset Structure
The IMDb dataset is a json file with objects containing the text of the review, and a number 0 or 1 which is a negative or positive review
For example:
{
"label": 1, //(pos)
"text": "A wonderful little production ... "
}, {
"label": 0, //(neg)
"text": ""It actually pains me to say it, but this movie was horrible on every level ..."
}
After retrieving data from Wikipedia and Twitter, classifying and processing, we received the following data:
{'text': 'ืืชืืื ืืืืฅ ืืฉืืื ืืช ืืืืืื ืืืืืกื ืืืจื ืืืื ืืืื ืื ืืื ืืฉ ืืื ืืืืก ืื ืื ืืจืื ืืืืื ืืืจื ืืืคืฉ',
'source': 'twitter',
'label': 0,
'verb': 'ืืืืก',
'tweet_id': '1546803262065606658'},
{'text': ' ืืื ื ืืื ื ืืืื ืื ืืืขืจื ืืืชืืื ืืืจืืข ืืจืก ืืืืืืช ืขืืฅ',
'source': 'wikipedia',
'label': 1, //(Metaphor)
'verb': 'ืืืจืืข',
'tweet_id': None},
{'text': 'ืื ืืฉืชืืฉื ืืืขืจืื ืืื ืืืคื ืืช ืืช ืืืืืก ืืฉืื ืืชืขืืคื ืฉืื ืืืืจื ืืื ืืฉืืื ืืืชื',
'source': 'wikipedia',
'label': 0, //(Literal)
'verb': 'ืืฉืืื',
'tweet_id': None}
As you can see, we have the following fields:
- 'text': A sentence in Hebrew that was taken from Wikipedia or Twitter that included the verb name.
- 'source': There are two possible sources for this text: 'twitter' or 'wikipedia'.
- 'label': Classification of whether the verb name is literal ('label': 0) or metaphorical ('label': 1) in the given sentence.
- 'verb': A verb name in Hebrew on which the text is labeled.
- 'tweet_id': If the text was taken from Twitter, the tweet's id, or None if it was retrieved from Wikipedia.
In the next section we will explain how we created the dataset.
Data Statistics
Verb | After Agreement Between Taggers | Disagreements because of different answers | Disagreements because of different ignores | Total Literals After Agreement | Total Metaphors After Agreement | Split For Train Set | Split For Validation Set | Split For Test Set |
---|---|---|---|---|---|---|---|---|
lakhalom_twitter | 131 | 42 | 42 | 26 | 105 | 83 | 21 | 27 |
lakhalom_wiki | 93 | 53 | 69 | 34 | 59 | 58 | 16 | 19 |
lakhatokh_twitter | 220 | 41 | 40 | 100 | 120 | 140 | 36 | 44 |
lakhatokh_wiki | 288 | 54 | 6 | 286 | 2 | 182 | 47 | 59 |
lauf_twitter | 166 | 8 | 29 | 14 | 152 | 104 | 28 | 34 |
lauf_wiki | 172 | 11 | 67 | 161 | 11 | 108 | 28 | 36 |
lefareq_twitter | 70 | 109 | 71 | 21 | 49 | 43 | 12 | 15 |
lefareq_wiki | 96 | 48 | 213 | 89 | 7 | 60 | 16 | 20 |
lehadliq_twitter | 270 | 14 | 19 | 194 | 76 | 172 | 43 | 55 |
lehadliq_wiki | 375 | 13 | 13 | 370 | 5 | 239 | 61 | 75 |
lekhabes_twitter | 203 | 2 | 26 | 155 | 48 | 129 | 33 | 41 |
lekhabes_wiki | 55 | 1 | 0 | 51 | 4 | 34 | 9 | 12 |
lekhofef_twitter | 289 | 7 | 11 | 18 | 271 | 183 | 47 | 59 |
lekhofef_wiki | 107 | 3 | 6 | 73 | 34 | 67 | 18 | 22 |
lerasek_twitter | 141 | 6 | 4 | 18 | 123 | 89 | 23 | 29 |
lerasek_wiki | 154 | 3 | 12 | 110 | 44 | 98 | 25 | 31 |
levashel_twitter | 280 | 3 | 19 | 264 | 16 | 177 | 46 | 57 |
levashel_wiki | 228 | 3 | 19 | 221 | 7 | 144 | 37 | 47 |
limkhoq_twitter | 222 | 16 | 22 | 123 | 99 | 141 | 36 | 45 |
limkhoq_wiki | 212 | 11 | 29 | 109 | 103 | 134 | 35 | 43 |
liqpots_twitter | 178 | 18 | 25 | 77 | 101 | 112 | 29 | 37 |
liqpots_wiki | 168 | 6 | 28 | 163 | 5 | 107 | 27 | 34 |
liqroa_twitter | 261 | 26 | 16 | 36 | 225 | 166 | 42 | 53 |
liqroa_wiki | 226 | 18 | 19 | 147 | 79 | 143 | 37 | 46 |
liqtsor_twitter | 256 | 18 | 28 | 43 | 213 | 163 | 41 | 52 |
liqtsor_wiki | 167 | 11 | 2 | 99 | 68 | 106 | 27 | 34 |
lirqod_twitter | 187 | 14 | 31 | 138 | 49 | 119 | 30 | 38 |
lirqod_wiki | 214 | 18 | 34 | 211 | 3 | 135 | 35 | 44 |
lishbor_twitter | 185 | 8 | 30 | 38 | 147 | 117 | 30 | 38 |
lishbor_wiki | 244 | 6 | 50 | 69 | 175 | 156 | 39 | 49 |
lishdod_twitter | 163 | 21 | 16 | 91 | 72 | 102 | 27 | 34 |
lishdod_wiki | 209 | 2 | 4 | 209 | 0 | 133 | 34 | 42 |
lishtot_twitter | 275 | 6 | 20 | 219 | 56 | 175 | 44 | 56 |
lishtot_wiki | 192 | 7 | 2 | 192 | 0 | 122 | 31 | 39 |
litkhon_twitter | 280 | 13 | 20 | 44 | 236 | 178 | 45 | 57 |
litkhon_wiki | 87 | 0 | 0 | 85 | 2 | 54 | 15 | 18 |
litpor_twitter | 193 | 4 | 13 | 26 | 167 | 122 | 31 | 40 |
litpor_wiki | 178 | 2 | 14 | 153 | 25 | 113 | 29 | 36 |
lizroa_twitter | 246 | 3 | 51 | 11 | 235 | 156 | 40 | 50 |
lizroa_wiki | 127 | 2 | 171 | 67 | 60 | 80 | 21 | 26 |
total | 7808 | 651 | 1291 | 4555 | 3253 | 4944 | 1271 | 1593 |
Contributions
Created by Doron Ben-chorin, Matan Ben-chorin, Tomer Tzipori, Guided by Dr. Oren Mishali. This is our project as part of computer engineering studies in the Faculty of Electrical Engineering combined with Computer Science at Technion, Israel Institute of Technology. For more cooperation, please contact email:
Doron Ben-chorin: [email protected]
Matan Ben-chorin: [email protected]
Tomer Tzipori: [email protected]
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