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**************__xlnet_**************
------------union-----------------
Accuracy in F1: 0.5216238119796656, 	 Precision: 0.3643474680938658, 	 Recall: 0.9178117708063261
------------intersection-----------------
Accuracy in F1: 0.48111455108359136, 	 Precision: 0.597003457548982, 	 Recall: 0.4029038112522686
**************__bert_**************
------------union-----------------
Accuracy in F1: 0.6178445229681979, 	 Precision: 0.4685783197105722, 	 Recall: 0.9066632097485092
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Accuracy in F1: 0.7483903547531877, 	 Precision: 0.7293307086614174, 	 Recall: 0.7684729064039408
**************__roberta_**************
------------union-----------------
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------------intersection-----------------
Accuracy in F1: 0.7367123287671234, 	 Precision: 0.7810049375544583, 	 Recall: 0.6971739694062743
**************__albert_**************
------------union-----------------
Accuracy in F1: 0.491410723581468, 	 Precision: 0.32803405438276434, 	 Recall: 0.9789992221934146
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Accuracy in F1: 0.7631515151515151, 	 Precision: 0.7165945822900068, 	 Recall: 0.8161783769769251
**************__online_delivary_**************
**************__xlnet_**************
------------union-----------------
Accuracy in F1: 0.3723613156602847, 	 Precision: 0.23139109212934716, 	 Recall: 0.9528894472361809
------------intersection-----------------
Accuracy in F1: 0.42281879194630867, 	 Precision: 0.6363636363636364, 	 Recall: 0.3165829145728643
**************__bert_**************
------------union-----------------
Accuracy in F1: 0.6632833186231244, 	 Precision: 0.5112244897959184, 	 Recall: 0.9440954773869347
------------intersection-----------------
Accuracy in F1: 0.7306936253861275, 	 Precision: 0.6607414931437278, 	 Recall: 0.8172110552763819
**************__roberta_**************
------------union-----------------
Accuracy in F1: 0.6464076405469938, 	 Precision: 0.49386401326699836, 	 Recall: 0.9353015075376885
------------intersection-----------------
Accuracy in F1: 0.7759036144578312, 	 Precision: 0.7453703703703703, 	 Recall: 0.8090452261306532
**************__albert_**************
------------union-----------------
Accuracy in F1: 0.6586414445399829, 	 Precision: 0.5006535947712418, 	 Recall: 0.9623115577889447
------------intersection-----------------
Accuracy in F1: 0.7462937062937062, 	 Precision: 0.6727181038830056, 	 Recall: 0.8379396984924623
**************__student_perf_**************
**************__xlnet_**************
------------union-----------------
Accuracy in F1: 0.46692607003891046, 	 Precision: 0.3226594964556343, 	 Recall: 0.8445297504798465
------------intersection-----------------
Accuracy in F1: 0.20095693779904306, 	 Precision: 0.5943396226415094, 	 Recall: 0.12092130518234165
**************__bert_**************
------------union-----------------
Accuracy in F1: 0.48767967145790564, 	 Precision: 0.3328661527680449, 	 Recall: 0.9117082533589251
------------intersection-----------------
Accuracy in F1: 0.5118170266836086, 	 Precision: 0.4245362563237774, 	 Recall: 0.6442738323736404
**************__roberta_**************
------------union-----------------
Accuracy in F1: 0.5407980941036331, 	 Precision: 0.3920552677029361, 	 Recall: 0.8714011516314779
------------intersection-----------------
Accuracy in F1: 0.5448504983388704, 	 Precision: 0.5160183066361556, 	 Recall: 0.5770953294945618
**************__albert_**************
------------union-----------------
Accuracy in F1: 0.45132743362831856, 	 Precision: 0.29968520461699893, 	 Recall: 0.9136276391554703
------------intersection-----------------
Accuracy in F1: 0.5001256597134959, 	 Precision: 0.41183774834437087, 	 Recall: 0.63659628918746
