Add high temp results

Code/mean_deviation_results.py

@@ -168,18 +168,24 @@ par_reloc_rec = [0.9182879377431906, 0.933852140077821, 0.933852140077821, 0.920
 
 
 #paraphrases with high temp
-high_loc_acc = [0.8777533039647577, 0.8810572687224669, 0.8821585903083701]
-high_loc_f1 = [0.672566371681416, 0.6707317073170731, 0.6805970149253731]
-high_loc_prec = [0.75, 0.7801418439716312, 0.7702702702702703]
-high_loc_rec = [0.6096256684491979, 0.5882352941176471, 0.6096256684491979]
+high_loc_acc = [0.8777533039647577, 0.8810572687224669, 0.8821585903083701, 0.8909691629955947, 0.8931718061674009]
+high_loc_f1 = [0.672566371681416, 0.6707317073170731, 0.6805970149253731, 0.6972477064220183, 0.6996904024767802]
+high_loc_prec = [0.75, 0.7801418439716312, 0.7702702702702703, 0.8142857142857143, 0.8308823529411765]
+high_loc_rec = [0.6096256684491979, 0.5882352941176471, 0.6096256684491979, 0.6096256684491979, 0.6042780748663101]
+
+
+high_comp_acc = [0.7125550660792952, 0.7455947136563876, 0.7422907488986784, 0.5770925110132159, 0.6916299559471366]
+high_comp_f1 = [0.41348314606741576, 0.384, 0.3641304347826087, 0.3356401384083044, 0.388646288209607]
+high_comp_prec = [0.35658914728682173, 0.3829787234042553, 0.3701657458563536, 0.24808184143222506, 0.3284132841328413]
+high_comp_rec = [0.4919786096256685, 0.3850267379679144, 0.3582887700534759, 0.5187165775401069, 0.47593582887700536]
 
 
 # Original array
-array1 = par_loc_acc
+array1 = high_loc_acc
 #array2= roberta_context_split_comp_acc
-array2 = par_loc_f1
-array3 = par_loc_prec
-array4 = par_loc_rec
+array2 = high_loc_f1
+array3 = high_loc_prec
+array4 = high_loc_rec
 #print(array)
   
 r1 = np.mean(array1)