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**************__xlnet_**************
------------union-----------------
Accuracy in F1: 0.6156911581569117, 	 Precision: 0.4528578407425501, 	 Recall: 0.9613689395903552
------------intersection-----------------
Accuracy in F1: 0.34399075500770415, 	 Precision: 0.6689138576779026, 	 Recall: 0.2315270935960591
**************__bert_**************
------------union-----------------
Accuracy in F1: 0.6571508017425155, 	 Precision: 0.5113964223889209, 	 Recall: 0.9191081151153746
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Accuracy in F1: 0.7380772142316427, 	 Precision: 0.7188498402555911, 	 Recall: 0.7583614207933627
**************__roberta_**************
------------union-----------------
Accuracy in F1: 0.7319132227348583, 	 Precision: 0.5889415481832544, 	 Recall: 0.9665543168265491
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Accuracy in F1: 0.8291089626339169, 	 Precision: 0.8356597313668686, 	 Recall: 0.8226600985221675
**************__albert_**************
------------union-----------------
Accuracy in F1: 0.743519781718963, 	 Precision: 0.5956284153005464, 	 Recall: 0.9891107078039928
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Accuracy in F1: 0.7824637866940135, 	 Precision: 0.7738336713995944, 	 Recall: 0.7912885662431942
**************__online_delivary_**************
**************__xlnet_**************
------------union-----------------
Accuracy in F1: 0.4559099437148218, 	 Precision: 0.3034970857618651, 	 Recall: 0.9158291457286433
------------intersection-----------------
Accuracy in F1: 0.33888344760039174, 	 Precision: 0.7757847533632287, 	 Recall: 0.21679197994987467
**************__bert_**************
------------union-----------------
Accuracy in F1: 0.5704467353951891, 	 Precision: 0.4097641250685683, 	 Recall: 0.9384422110552764
------------intersection-----------------
Accuracy in F1: 0.7275794208602755, 	 Precision: 0.6585241730279898, 	 Recall: 0.8128140703517588
**************__roberta_**************
------------union-----------------
Accuracy in F1: 0.7069571328179902, 	 Precision: 0.5636906985431454, 	 Recall: 0.9478643216080402
------------intersection-----------------
Accuracy in F1: 0.7868453105968333, 	 Precision: 0.7635933806146572, 	 Recall: 0.8115577889447236
**************__albert_**************
------------union-----------------
Accuracy in F1: 0.640907181856363, 	 Precision: 0.48138801261829656, 	 Recall: 0.9585427135678392
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Accuracy in F1: 0.745949074074074, 	 Precision: 0.6915236051502146, 	 Recall: 0.8096733668341709
**************__student_perf_**************
**************__xlnet_**************
------------union-----------------
Accuracy in F1: 0.4563758389261745, 	 Precision: 0.3300028546959749, 	 Recall: 0.7396033269353807
------------intersection-----------------
Accuracy in F1: 0.3388203017832647, 	 Precision: 0.36511456023651145, 	 Recall: 0.3160588611644274
**************__bert_**************
------------union-----------------
Accuracy in F1: 0.45145018915510726, 	 Precision: 0.29951892909433175, 	 Recall: 0.9161868202175304
------------intersection-----------------
Accuracy in F1: 0.5172098690222358, 	 Precision: 0.4936046511627907, 	 Recall: 0.5431861804222649
**************__roberta_**************
------------union-----------------
Accuracy in F1: 0.5452771272443404, 	 Precision: 0.39230553215388936, 	 Recall: 0.8937939859245042
------------intersection-----------------
Accuracy in F1: 0.49032258064516127, 	 Precision: 0.5574572127139364, 	 Recall: 0.43761996161228406
**************__albert_**************
------------union-----------------
Accuracy in F1: 0.4460212873796249, 	 Precision: 0.30303030303030304, 	 Recall: 0.8445297504798465
------------intersection-----------------
Accuracy in F1: 0.49148099606815204, 	 Precision: 0.503693754197448, 	 Recall: 0.4798464491362764
