diff --git a/faithfulness_metrics/sst/topk-dev-f1-metrics-description.json b/faithfulness_metrics/sst/topk-dev-f1-metrics-description.json
index 93a17c39..03959daa 100644
--- a/faithfulness_metrics/sst/topk-dev-f1-metrics-description.json
+++ b/faithfulness_metrics/sst/topk-dev-f1-metrics-description.json
@@ -1,38 +1,42 @@
 {
     "f1 macro avg - model labels": {
-        "fixed-fixed-len_var-feat_var-type": 54.679,
+        "fixed-shap": 81.651,
         "fixed-lime": 73.995,
         "fixed-attention": 79.239,
         "fixed-ig": 75.227,
         "fixed-gradients": 86.801,
         "fixed-scaled attention": 62.153,
         "fixed-random": 90.022,
-        "fixed-fixed-len_var-feat": 55.455,
-        "var-var-len_var-feat_var-type": 55.521,
+        "fixed-fixed-len_var-feat": 55.103,
+        "fixed-fixed-len_var-feat_var-type": 53.989,
+        "var-shap": 83.691,
         "var-lime": 79.091,
         "var-attention": 84.235,
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         "var-scaled attention": 63.729,
         "var-random": 91.282,
-        "var-var-len_var-feat": 57.274
+        "var-var-len_var-feat": 56.489,
+        "var-var-len_var-feat_var-type": 54.841
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     "f1 macro avg - actual labels": {
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+        "fixed-shap": 79.931,
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-        "var-var-len_var-feat_var-type": 60.44,
+        "fixed-fixed-len_var-feat": 58.871,
+        "fixed-fixed-len_var-feat_var-type": 58.217,
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         "var-scaled attention": 67.757,
         "var-random": 86.811,
-        "var-var-len_var-feat": 61.965
+        "var-var-len_var-feat": 60.95,
+        "var-var-len_var-feat_var-type": 59.76
     }
 }
\ No newline at end of file
diff --git a/faithfulness_metrics/sst/topk-dev-faithfulness-metrics-description.json b/faithfulness_metrics/sst/topk-dev-faithfulness-metrics-description.json
index 5ed76f0e..d5ef6629 100644
--- a/faithfulness_metrics/sst/topk-dev-faithfulness-metrics-description.json
+++ b/faithfulness_metrics/sst/topk-dev-faithfulness-metrics-description.json
@@ -1,162 +1,182 @@
 {
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-            "std": 0.44906766819925054
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+            "mean": 0.5023566806884354,
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 }
\ No newline at end of file
diff --git a/faithfulness_metrics/sst/topk-dev-faithfulness-metrics.json b/faithfulness_metrics/sst/topk-dev-faithfulness-metrics.json
index da7d3d93..1d3c2a19 100644
--- a/faithfulness_metrics/sst/topk-dev-faithfulness-metrics.json
+++ b/faithfulness_metrics/sst/topk-dev-faithfulness-metrics.json
@@ -1,6 +1,6 @@
 {
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@@ -104,7 +104,20 @@
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@@ -207,15 +220,28 @@
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@@ -314,7 +340,7 @@
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@@ -327,9 +353,22 @@
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@@ -341,7 +380,7 @@
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@@ -354,7 +393,7 @@
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@@ -367,7 +406,7 @@
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@@ -380,7 +419,7 @@
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@@ -394,7 +433,7 @@
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@@ -407,7 +446,20 @@
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@@ -420,12 +472,12 @@
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@@ -524,7 +576,7 @@
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@@ -537,7 +589,7 @@
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@@ -550,9 +602,22 @@
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@@ -565,7 +630,7 @@
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@@ -578,7 +643,7 @@
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@@ -591,7 +656,7 @@
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@@ -603,7 +668,7 @@
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@@ -617,7 +682,20 @@
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@@ -630,12 +708,12 @@
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@@ -734,7 +812,7 @@
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@@ -747,7 +825,7 @@
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@@ -760,7 +838,7 @@
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@@ -773,9 +851,22 @@
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@@ -182384,12 +204952,25 @@
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@@ -182399,7 +204980,7 @@
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@@ -182412,7 +204993,7 @@
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@@ -182450,7 +205031,7 @@
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@@ -182464,7 +205045,7 @@
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@@ -182621,8 +205228,8 @@
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@@ -182647,8 +205254,8 @@
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@@ -182660,8 +205267,8 @@
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@@ -182674,7 +205281,7 @@
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@@ -182686,8 +205293,8 @@
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@@ -182697,15 +205304,28 @@
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@@ -182804,12 +205424,25 @@
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@@ -182831,8 +205464,8 @@
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@@ -182857,8 +205490,8 @@
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@@ -182883,8 +205516,8 @@
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@@ -182907,15 +205540,28 @@
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@@ -183014,12 +205660,25 @@
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@@ -183041,8 +205700,8 @@
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@@ -183055,7 +205714,7 @@
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@@ -183067,8 +205726,8 @@
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@@ -183080,8 +205739,8 @@
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@@ -183093,8 +205752,8 @@
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@@ -183107,7 +205766,20 @@
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diff --git a/faithfulness_metrics/sst/topk-test-f1-metrics-description.json b/faithfulness_metrics/sst/topk-test-f1-metrics-description.json
index b0e8d35f..d5e0bc41 100644
--- a/faithfulness_metrics/sst/topk-test-f1-metrics-description.json
+++ b/faithfulness_metrics/sst/topk-test-f1-metrics-description.json
@@ -1,38 +1,42 @@
 {
     "f1 macro avg - model labels": {
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-        "var-var-len_var-feat_var-type": 57.049,
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         "var-random": 89.456,
-        "var-var-len_var-feat": 57.873
+        "var-var-len_var-feat": 57.225,
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     "f1 macro avg - actual labels": {
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-        "var-var-len_var-feat": 62.123
+        "var-var-len_var-feat": 61.474,
+        "var-var-len_var-feat_var-type": 60.974
     }
 }
\ No newline at end of file
diff --git a/faithfulness_metrics/sst/topk-test-faithfulness-metrics-description.json b/faithfulness_metrics/sst/topk-test-faithfulness-metrics-description.json
index 36b3a9cd..cf888298 100644
--- a/faithfulness_metrics/sst/topk-test-faithfulness-metrics-description.json
+++ b/faithfulness_metrics/sst/topk-test-faithfulness-metrics-description.json
@@ -1,162 +1,182 @@
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 }
\ No newline at end of file
diff --git a/faithfulness_metrics/sst/topk-test-faithfulness-metrics.json b/faithfulness_metrics/sst/topk-test-faithfulness-metrics.json
index b249067b..6ebeb68f 100644
--- a/faithfulness_metrics/sst/topk-test-faithfulness-metrics.json
+++ b/faithfulness_metrics/sst/topk-test-faithfulness-metrics.json
@@ -1,6 +1,6 @@
 {
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@@ -104,7 +104,20 @@
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@@ -207,10 +220,23 @@
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@@ -327,7 +353,7 @@
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@@ -340,9 +366,22 @@
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@@ -368,7 +407,7 @@
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@@ -380,7 +419,7 @@
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@@ -420,7 +472,7 @@
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@@ -630,7 +708,7 @@
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@@ -734,7 +812,7 @@
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@@ -747,8 +825,21 @@
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@@ -840,7 +944,7 @@
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@@ -997,7 +1114,7 @@
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@@ -5250,12 +5900,12 @@
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@@ -5670,7 +6372,7 @@
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@@ -6300,7 +7080,7 @@
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@@ -6642,7 +7461,7 @@
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@@ -8492,11 +9532,11 @@
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@@ -8820,12 +9912,12 @@
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@@ -8924,9 +10016,22 @@
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@@ -8939,7 +10044,7 @@
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@@ -9030,12 +10148,12 @@
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@@ -9134,7 +10252,7 @@
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@@ -9200,7 +10331,7 @@
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@@ -10263,7 +11524,7 @@
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@@ -10290,12 +11564,12 @@
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@@ -12810,12 +14396,12 @@
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@@ -12942,7 +14541,7 @@
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@@ -13017,15 +14616,28 @@
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@@ -21774,8 +24465,8 @@
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@@ -22024,7 +24741,7 @@
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@@ -22037,7 +24754,20 @@
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@@ -22050,12 +24780,12 @@
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@@ -22260,12 +25016,12 @@
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@@ -22364,9 +25120,22 @@
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@@ -22443,7 +25212,7 @@
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@@ -22457,7 +25226,20 @@
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@@ -22470,12 +25252,12 @@
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@@ -22628,7 +25423,7 @@
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@@ -22654,7 +25449,7 @@
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@@ -22667,7 +25462,20 @@
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@@ -22680,12 +25488,12 @@
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@@ -31710,12 +35636,12 @@
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@@ -31842,7 +35781,7 @@
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@@ -40662,7 +45693,7 @@
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@@ -40714,7 +45745,7 @@
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@@ -40740,12 +45784,12 @@
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@@ -40844,7 +45888,7 @@
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@@ -40872,7 +45929,7 @@
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@@ -40911,7 +45968,7 @@
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@@ -40924,7 +45981,7 @@
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@@ -40937,7 +45994,20 @@
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@@ -40950,12 +46020,12 @@
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@@ -54377,7 +61098,20 @@
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@@ -54390,12 +61124,12 @@
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@@ -54600,12 +61360,12 @@
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@@ -55650,12 +62540,12 @@
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@@ -57920,8 +65083,8 @@
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@@ -58079,7 +65268,7 @@
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@@ -61504,7 +69109,7 @@
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@@ -62527,7 +70262,7 @@
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@@ -62580,12 +70328,12 @@
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@@ -62684,12 +70432,25 @@
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@@ -62997,15 +70784,28 @@
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@@ -63104,12 +70904,25 @@
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@@ -79377,15 +89192,28 @@
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@@ -79564,7 +89405,7 @@
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@@ -79577,7 +89418,20 @@
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@@ -79590,12 +89444,12 @@
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@@ -83777,7 +94138,20 @@
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@@ -83948,7 +94335,7 @@
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@@ -84184,7 +94597,7 @@
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@@ -84210,12 +94636,12 @@
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@@ -84417,15 +94856,28 @@
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@@ -84524,12 +94976,25 @@
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@@ -85811,8 +96432,8 @@
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@@ -86061,7 +96708,7 @@
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@@ -86271,7 +96944,7 @@
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@@ -86297,7 +96970,20 @@
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@@ -86310,12 +96996,12 @@
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@@ -86494,7 +97193,7 @@
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@@ -86517,15 +97216,28 @@
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@@ -86624,12 +97336,25 @@
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@@ -86727,15 +97452,28 @@
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@@ -86834,12 +97572,25 @@
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@@ -86849,7 +97600,7 @@
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@@ -86862,7 +97613,7 @@
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@@ -86875,7 +97626,7 @@
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@@ -86888,7 +97639,7 @@
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@@ -95340,12 +107144,12 @@
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@@ -113951,8 +128056,8 @@
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@@ -114344,7 +128488,7 @@
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@@ -114437,7 +128594,20 @@
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@@ -114450,12 +128620,12 @@
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@@ -114554,7 +128724,7 @@
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@@ -114764,7 +128960,7 @@
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@@ -197164,7 +221565,7 @@
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@@ -197335,7 +221762,7 @@
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@@ -198450,12 +223020,12 @@
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@@ -285573,7 +320920,7 @@
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@@ -285600,12 +320960,12 @@
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@@ -351117,15 +394576,28 @@
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@@ -351224,7 +394696,7 @@
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@@ -351327,15 +394812,28 @@
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@@ -352377,15 +395992,28 @@
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@@ -352587,15 +396228,28 @@
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@@ -352694,12 +396348,25 @@
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@@ -352709,7 +396376,7 @@
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@@ -352797,15 +396464,28 @@
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@@ -358050,12 +402380,12 @@
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@@ -371910,12 +417956,12 @@
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                 0.8939763903617859
@@ -382147,8 +429454,8 @@
             "labels": 1
         },
         "var-gradients": {
-            "comprehensiveness": 0.39648523926734924,
-            "sufficiency": 0.9603051543235779,
+            "comprehensiveness": 0.39639225602149963,
+            "sufficiency": 0.9603144526481628,
             "masked prediction probs": [
                 0.613554835319519,
                 0.38644516468048096
@@ -382160,7 +429467,7 @@
             "labels": 1
         },
         "var-scaled attention": {
-            "comprehensiveness": 0.6880525350570679,
+            "comprehensiveness": 0.6878911852836609,
             "sufficiency": 1.0,
             "masked prediction probs": [
                 0.3648408353328705,
@@ -382174,7 +429481,7 @@
         },
         "var-random": {
             "comprehensiveness": 0.0,
-            "sufficiency": 0.8834455013275146,
+            "sufficiency": 0.8834728002548218,
             "masked prediction probs": [
                 0.9783368110656738,
                 0.02166316844522953
@@ -382186,8 +429493,21 @@
             "labels": 1
         },
         "var-var-len_var-feat": {
-            "comprehensiveness": 0.9910957217216492,
-            "sufficiency": 0.9779585599899292,
+            "comprehensiveness": 0.9908633828163147,
+            "sufficiency": 0.9779636859893799,
+            "masked prediction probs": [
+                0.1060236245393753,
+                0.8939763903617859
+            ],
+            "full text prediction probs": [
+                0.9515792727470398,
+                0.048420682549476624
+            ],
+            "labels": 1
+        },
+        "var-var-len_var-feat_var-type": {
+            "comprehensiveness": 0.9906312227249146,
+            "sufficiency": 0.9779688715934753,
             "masked prediction probs": [
                 0.1060236245393753,
                 0.8939763903617859
@@ -382200,12 +429520,12 @@
         }
     },
     "test_1805": {
-        "fixed-fixed-len_var-feat_var-type": {
-            "comprehensiveness": 0.9604332447052002,
-            "sufficiency": 0.9997366666793823,
+        "fixed-shap": {
+            "comprehensiveness": 0.09974683076143265,
+            "sufficiency": 0.9067371487617493,
             "masked prediction probs": [
-                0.13633014261722565,
-                0.8636698722839355
+                0.8955721855163574,
+                0.10442784428596497
             ],
             "full text prediction probs": [
                 0.9835624098777771,
@@ -382304,12 +429624,25 @@
             ],
             "labels": 0
         },
-        "var-var-len_var-feat_var-type": {
-            "comprehensiveness": 0.8962023854255676,
-            "sufficiency": 0.9971527457237244,
+        "fixed-fixed-len_var-feat_var-type": {
+            "comprehensiveness": 0.958694338798523,
+            "sufficiency": 0.9997371435165405,
             "masked prediction probs": [
-                0.19155652821063995,
-                0.8084434270858765
+                0.13633014261722565,
+                0.8636698722839355
+            ],
+            "full text prediction probs": [
+                0.9835624098777771,
+                0.01643764227628708
+            ],
+            "labels": 0
+        },
+        "var-shap": {
+            "comprehensiveness": 0.10901311784982681,
+            "sufficiency": 0.9085985422134399,
+            "masked prediction probs": [
+                0.8872018456459045,
+                0.11279816925525665
             ],
             "full text prediction probs": [
                 0.9835624098777771,
@@ -382319,7 +429652,7 @@
         },
         "var-lime": {
             "comprehensiveness": 0.0,
-            "sufficiency": 0.6996479630470276,
+            "sufficiency": 0.6997158527374268,
             "masked prediction probs": [
                 0.9850903749465942,
                 0.01490964088588953
@@ -382331,8 +429664,8 @@
             "labels": 0
         },
         "var-attention": {
-            "comprehensiveness": 0.050509147346019745,
-            "sufficiency": 0.8507433533668518,
+            "comprehensiveness": 0.05049772560596466,
+            "sufficiency": 0.8507770299911499,
             "masked prediction probs": [
                 0.9389054775238037,
                 0.06109444424510002
@@ -382344,8 +429677,8 @@
             "labels": 0
         },
         "var-ig": {
-            "comprehensiveness": 0.3998306691646576,
-            "sufficiency": 0.9626076221466064,
+            "comprehensiveness": 0.3997402489185333,
+            "sufficiency": 0.9626160860061646,
             "masked prediction probs": [
                 0.6299779415130615,
                 0.3700220584869385
@@ -382357,8 +429690,8 @@
             "labels": 0
         },
         "var-gradients": {
-            "comprehensiveness": 0.5889766812324524,
-            "sufficiency": 0.9556008577346802,
+            "comprehensiveness": 0.588843584060669,
+            "sufficiency": 0.9556108713150024,
             "masked prediction probs": [
                 0.4625915586948395,
                 0.5374084115028381
@@ -382370,8 +429703,8 @@
             "labels": 0
         },
         "var-scaled attention": {
-            "comprehensiveness": 0.8951894640922546,
-            "sufficiency": 0.997156023979187,
+            "comprehensiveness": 0.8949871063232422,
+            "sufficiency": 0.9971567392349243,
             "masked prediction probs": [
                 0.19155652821063995,
                 0.8084434270858765
@@ -382384,7 +429717,7 @@
         },
         "var-random": {
             "comprehensiveness": 0.0,
-            "sufficiency": 0.846777081489563,
+            "sufficiency": 0.8468117117881775,
             "masked prediction probs": [
                 0.9877471923828125,
                 0.01225275918841362
@@ -382396,8 +429729,21 @@
             "labels": 0
         },
         "var-var-len_var-feat": {
-            "comprehensiveness": 0.8947848677635193,
-            "sufficiency": 0.9971573352813721,
+            "comprehensiveness": 0.8945827484130859,
+            "sufficiency": 0.997157871723175,
+            "masked prediction probs": [
+                0.19155652821063995,
+                0.8084434270858765
+            ],
+            "full text prediction probs": [
+                0.9835624098777771,
+                0.01643764227628708
+            ],
+            "labels": 0
+        },
+        "var-var-len_var-feat_var-type": {
+            "comprehensiveness": 0.8943806886672974,
+            "sufficiency": 0.9971585869789124,
             "masked prediction probs": [
                 0.19155652821063995,
                 0.8084434270858765
diff --git a/faithfulness_metrics/sst/topk-test-increasing-feature-scoring.json b/faithfulness_metrics/sst/topk-test-increasing-feature-scoring.json
index 8f71597a..0fc97994 100644
--- a/faithfulness_metrics/sst/topk-test-increasing-feature-scoring.json
+++ b/faithfulness_metrics/sst/topk-test-increasing-feature-scoring.json
@@ -1,125 +1,149 @@
 {
-    "fixed": {
+    "variable": {
         "gradients": {
-            "f1 score - model labels": 86.985,
-            "f1 score - actual labels": 84.068,
+            "f1 score - model labels": 86.763,
+            "f1 score - actual labels": 84.514,
             "sufficiency": {
-                "mean": 0.44393163717227574,
-                "std": 0.4459341003100556
+                "mean": 0.425685491327852,
+                "std": 0.4353088356817606
             },
             "comprehensiveness": {
-                "mean": 0.24682333320138722,
-                "std": 0.3814387155192235
+                "mean": 0.22578238561130531,
+                "std": 0.37458233907657473
             }
         },
         "ig-gradients": {
-            "f1 score - model labels": 75.195,
-            "f1 score - actual labels": 76.421,
+            "f1 score - model labels": 75.668,
+            "f1 score - actual labels": 77.463,
             "sufficiency": {
-                "mean": 0.5196734493857128,
-                "std": 0.4535779920409823
+                "mean": 0.48972631745459283,
+                "std": 0.44742287265854164
             },
             "comprehensiveness": {
-                "mean": 0.3959912680490343,
-                "std": 0.43740705300342847
+                "mean": 0.37186101361909996,
+                "std": 0.43518740065747613
+            }
+        },
+        "lime-ig-attention": {
+            "f1 score - model labels": 64.558,
+            "f1 score - actual labels": 67.484,
+            "sufficiency": {
+                "mean": 0.549531018011628,
+                "std": 0.4355701493518745
+            },
+            "comprehensiveness": {
+                "mean": 0.48276345835999485,
+                "std": 0.4582239962723786
             }
         },
-        "ig-attention-gradients": {
-            "f1 score - model labels": 70.939,
-            "f1 score - actual labels": 72.813,
+        "lime-ig-gradients-attention": {
+            "f1 score - model labels": 63.865,
+            "f1 score - actual labels": 66.902,
             "sufficiency": {
-                "mean": 0.5464308678567867,
-                "std": 0.45230280861269034
+                "mean": 0.5517295368275575,
+                "std": 0.4366490210498552
             },
             "comprehensiveness": {
-                "mean": 0.4394861163707962,
-                "std": 0.4453270330235261
+                "mean": 0.49107218173185696,
+                "std": 0.45708296692984957
             }
         },
-        "scaled attention-attention-gradients-ig": {
-            "f1 score - model labels": 61.096,
-            "f1 score - actual labels": 65.149,
+        "attention-lime-scaled attention-ig-gradients": {
+            "f1 score - model labels": 57.873,
+            "f1 score - actual labels": 62.123,
             "sufficiency": {
-                "mean": 0.6200616891396921,
-                "std": 0.43963408357427725
+                "mean": 0.5843208569497328,
+                "std": 0.43703907103964773
             },
             "comprehensiveness": {
-                "mean": 0.535423665005506,
-                "std": 0.44537890421493337
+                "mean": 0.5471360652019174,
+                "std": 0.4503451232800912
             }
         },
-        "scaled attention-ig-lime-attention-gradients": {
-            "f1 score - model labels": 56.516,
-            "f1 score - actual labels": 60.747,
+        "shap-attention-lime-scaled attention-ig-gradients": {
+            "f1 score - model labels": 57.225,
+            "f1 score - actual labels": 61.474,
             "sufficiency": {
-                "mean": 0.632534822155141,
-                "std": 0.4319432114701702
+                "mean": 0.5857130066521838,
+                "std": 0.43673217957637656
             },
             "comprehensiveness": {
-                "mean": 0.5679991710886132,
-                "std": 0.44618307490280384
+                "mean": 0.5535359131198431,
+                "std": 0.4499029957205866
             }
         }
     },
-    "variable": {
+    "fixed": {
         "gradients": {
-            "f1 score - model labels": 86.763,
-            "f1 score - actual labels": 84.514,
+            "f1 score - model labels": 86.985,
+            "f1 score - actual labels": 84.068,
             "sufficiency": {
-                "mean": 0.4256538513227072,
-                "std": 0.435299567894873
+                "mean": 0.44393163717227574,
+                "std": 0.4459341003100556
             },
             "comprehensiveness": {
-                "mean": 0.2258884437424767,
-                "std": 0.3746481589465204
+                "mean": 0.24682333320138722,
+                "std": 0.3814387155192235
             }
         },
         "ig-gradients": {
-            "f1 score - model labels": 75.668,
-            "f1 score - actual labels": 77.463,
+            "f1 score - model labels": 75.195,
+            "f1 score - actual labels": 76.421,
             "sufficiency": {
-                "mean": 0.4896724335354792,
-                "std": 0.44741967397157073
+                "mean": 0.5196734493857128,
+                "std": 0.4535779920409823
             },
             "comprehensiveness": {
-                "mean": 0.37198795117098377,
-                "std": 0.4352235803013905
+                "mean": 0.3959912680490343,
+                "std": 0.43740705300342847
             }
         },
-        "ig-attention-gradients": {
-            "f1 score - model labels": 72.368,
-            "f1 score - actual labels": 73.827,
+        "lime-ig-attention": {
+            "f1 score - model labels": 62.299,
+            "f1 score - actual labels": 65.198,
             "sufficiency": {
-                "mean": 0.510805485161499,
-                "std": 0.44699786932773355
+                "mean": 0.5970515442405046,
+                "std": 0.43301184510450047
             },
             "comprehensiveness": {
-                "mean": 0.4101780824971072,
-                "std": 0.44300776443836565
+                "mean": 0.5051774819769438,
+                "std": 0.4582025910818161
             }
         },
-        "scaled attention-attention-gradients-ig": {
-            "f1 score - model labels": 62.232,
-            "f1 score - actual labels": 65.868,
+        "lime-ig-gradients-attention": {
+            "f1 score - model labels": 61.849,
+            "f1 score - actual labels": 64.86,
             "sufficiency": {
-                "mean": 0.5738640496858132,
-                "std": 0.44297634523705454
+                "mean": 0.5959752538719126,
+                "std": 0.4338802458738596
             },
             "comprehensiveness": {
-                "mean": 0.510945547127589,
-                "std": 0.4488739677172711
+                "mean": 0.5103476222886805,
+                "std": 0.45765550629713225
             }
         },
-        "scaled attention-ig-lime-attention-gradients": {
-            "f1 score - model labels": 57.873,
-            "f1 score - actual labels": 62.123,
+        "attention-lime-scaled attention-ig-gradients": {
+            "f1 score - model labels": 56.244,
+            "f1 score - actual labels": 60.361,
+            "sufficiency": {
+                "mean": 0.632604242762385,
+                "std": 0.4317609100127963
+            },
+            "comprehensiveness": {
+                "mean": 0.56798543694464,
+                "std": 0.4460996185773944
+            }
+        },
+        "shap-attention-lime-scaled attention-ig-gradients": {
+            "f1 score - model labels": 56.407,
+            "f1 score - actual labels": 60.305,
             "sufficiency": {
-                "mean": 0.5842277082880035,
-                "std": 0.437054562826494
+                "mean": 0.6294621802405657,
+                "std": 0.4316925061114822
             },
             "comprehensiveness": {
-                "mean": 0.5472200292402236,
-                "std": 0.45034825721983157
+                "mean": 0.5687479827713429,
+                "std": 0.4463274342960043
             }
         }
     }
diff --git a/src/evaluation/__pycache__/evaluation_pipeline.cpython-36.pyc b/src/evaluation/__pycache__/evaluation_pipeline.cpython-36.pyc
index 79e3ceb4..f2ceae58 100644
Binary files a/src/evaluation/__pycache__/evaluation_pipeline.cpython-36.pyc and b/src/evaluation/__pycache__/evaluation_pipeline.cpython-36.pyc differ
diff --git a/src/evaluation/evaluation_pipeline.py b/src/evaluation/evaluation_pipeline.py
index 5691e79e..b880fbf2 100644
--- a/src/evaluation/evaluation_pipeline.py
+++ b/src/evaluation/evaluation_pipeline.py
@@ -18,7 +18,7 @@
 from src.models.bert import bert
 from src.variable_rationales.var_length_feat import get_rationale_metadata_
 from src.variable_rationales.var_type import select_between_types_
-from src.evaluation.experiments.rationale_extractor import rationale_creator_, extract_importance_, extract_lime_scores_
+from src.evaluation.experiments.rationale_extractor import rationale_creator_, extract_importance_, extract_lime_scores_, extract_shap_values_
 from src.evaluation.experiments.erasure_tests import conduct_tests_
 from src.evaluation.experiments.increasing_feature_scoring import compute_faithfulness_
 
@@ -69,6 +69,16 @@ def register_importance_(self, data, data_split_name, no_of_labels, max_seq_len,
                 tokenizer = tokenizer
             )
 
+            extract_shap_values_(
+                model = model, 
+                data = data,
+                data_split_name = data_split_name,
+                model_random_seed = self.model_random_seed,
+                no_of_labels = no_of_labels,
+                max_seq_len = max_seq_len,
+                tokenizer = tokenizer
+            )
+
         else:
 
 
@@ -95,8 +105,25 @@ def register_importance_(self, data, data_split_name, no_of_labels, max_seq_len,
                     model_random_seed = self.model_random_seed
                 )
 
-                
+                extract_lime_scores_(
+                    model = model, 
+                    data = data,
+                    data_split_name = data_split_name,
+                    model_random_seed = self.model_random_seed,
+                    no_of_labels = no_of_labels,
+                    max_seq_len = max_seq_len,
+                    tokenizer = tokenizer
+                )
 
+                extract_shap_values_(
+                    model = model, 
+                    data = data,
+                    data_split_name = data_split_name,
+                    model_random_seed = self.model_random_seed,
+                    no_of_labels = no_of_labels,
+                    max_seq_len = max_seq_len,
+                    tokenizer = tokenizer
+                )
 
         return
 
diff --git a/src/evaluation/experiments/__pycache__/erasure_tests.cpython-36.pyc b/src/evaluation/experiments/__pycache__/erasure_tests.cpython-36.pyc
index 5293e8a9..b31f9be7 100644
Binary files a/src/evaluation/experiments/__pycache__/erasure_tests.cpython-36.pyc and b/src/evaluation/experiments/__pycache__/erasure_tests.cpython-36.pyc differ
diff --git a/src/evaluation/experiments/__pycache__/increasing_feature_scoring.cpython-36.pyc b/src/evaluation/experiments/__pycache__/increasing_feature_scoring.cpython-36.pyc
index 171c9280..00a7b292 100644
Binary files a/src/evaluation/experiments/__pycache__/increasing_feature_scoring.cpython-36.pyc and b/src/evaluation/experiments/__pycache__/increasing_feature_scoring.cpython-36.pyc differ
diff --git a/src/evaluation/experiments/__pycache__/lime_predictor.cpython-36.pyc b/src/evaluation/experiments/__pycache__/lime_predictor.cpython-36.pyc
index ef15f1fe..f907720e 100644
Binary files a/src/evaluation/experiments/__pycache__/lime_predictor.cpython-36.pyc and b/src/evaluation/experiments/__pycache__/lime_predictor.cpython-36.pyc differ
diff --git a/src/evaluation/experiments/__pycache__/rationale_extractor.cpython-36.pyc b/src/evaluation/experiments/__pycache__/rationale_extractor.cpython-36.pyc
index d9f0d611..0c6f8754 100644
Binary files a/src/evaluation/experiments/__pycache__/rationale_extractor.cpython-36.pyc and b/src/evaluation/experiments/__pycache__/rationale_extractor.cpython-36.pyc differ
diff --git a/src/evaluation/experiments/__pycache__/shap_predictor.cpython-36.pyc b/src/evaluation/experiments/__pycache__/shap_predictor.cpython-36.pyc
new file mode 100644
index 00000000..84a64ef4
Binary files /dev/null and b/src/evaluation/experiments/__pycache__/shap_predictor.cpython-36.pyc differ
diff --git a/src/evaluation/experiments/erasure_tests.py b/src/evaluation/experiments/erasure_tests.py
index 362f2b0c..2c7accaf 100644
--- a/src/evaluation/experiments/erasure_tests.py
+++ b/src/evaluation/experiments/erasure_tests.py
@@ -141,7 +141,7 @@ def conduct_tests_(model, data, split, model_random_seed):
 
                     var_alias = "fixed"
 
-                for feat_attribution_name in ["lime", "attention", "ig", "gradients", "scaled attention", "random", "--var-feat", "--var-all"]:
+                for feat_attribution_name in ["shap","lime", "attention", "ig", "gradients", "scaled attention", "random", "--var-feat", "--var-all"]:
 
                         if feat_attribution_name == "--var-all":
     
diff --git a/src/evaluation/experiments/increasing_feature_scoring.py b/src/evaluation/experiments/increasing_feature_scoring.py
index 410dc8b5..69363499 100644
--- a/src/evaluation/experiments/increasing_feature_scoring.py
+++ b/src/evaluation/experiments/increasing_feature_scoring.py
@@ -28,9 +28,10 @@ def compute_faithfulness_(rationale_metadata, prediction_data, split_name):
     metric_combos = [
         {"gradients"} , 
         {"gradients", "ig"}, 
-        {"attention", "ig", "gradients"},
-        {"scaled attention", "ig", "attention", "gradients"},
-        {"scaled attention", "ig", "attention", "gradients", "lime"}
+        {"gradients", "ig", "shap"},
+        {"gradients", "ig", "shap", "attention"},
+        {"gradients", "ig", "shap", "attention", "scaled attention"},
+        {"gradients", "ig", "shap", "attention", "scaled attention", "lime"}
     ]        
     
     faith_increasing = {}
diff --git a/src/evaluation/experiments/lime_predictor.py b/src/evaluation/experiments/lime_predictor.py
index 81472161..c917cb12 100644
--- a/src/evaluation/experiments/lime_predictor.py
+++ b/src/evaluation/experiments/lime_predictor.py
@@ -17,28 +17,6 @@ def __init__(self, model, tokenizer, seq_length):
         self.tokenizer = tokenizer
         self.max_seq_length = seq_length
 
-    def predict_label(self, text_a, text_b):
-
-        self.model.to(self.device)
-
-        inputs = self.convert_text_to_features(text_a, text_b)
-
-        with torch.no_grad():
-
-            outputs = self.model(**inputs)
-
-        logits = outputs[0]
-        logits = torch.softmax(logits, dim=1)
-
-        logits_label = torch.argmax(logits, dim=1)
-        label = logits_label.detach().cpu().numpy()
-
-        logits_confidence = logits[0][logits_label]
-        label_confidence_ = logits_confidence.detach().cpu().numpy()
-
-        return label, label_confidence_
-
-
     def convert_text_to_features(self, text):
         
         ## if empty text
diff --git a/src/evaluation/experiments/rationale_extractor.py b/src/evaluation/experiments/rationale_extractor.py
index 26fe7ecc..f096aff5 100644
--- a/src/evaluation/experiments/rationale_extractor.py
+++ b/src/evaluation/experiments/rationale_extractor.py
@@ -54,7 +54,7 @@ def extract_importance_(model, data, data_split_name, model_random_seed):
 
     scorenames = fname + data_split_name + f"_importance_scores_{model_random_seed}.npy"
 
-    ## check if importance scores exist first to avoid unecessary calculations
+    # check if importance scores exist first to avoid unecessary calculations
     if os.path.exists(scorenames):
 
         print(f"importance scores already saved in -> {scorenames}")
@@ -165,7 +165,7 @@ def extract_lime_scores_(model, data, data_split_name, model_random_seed, no_of_
     ## retrieve importance scores
     importance_scores = np.load(fname, allow_pickle = True).item()
 
-    prediction = predictor(
+    lime_predictor = predictor(
         model = model, 
         tokenizer = tokenizer, 
         seq_length = max_seq_len
@@ -210,7 +210,7 @@ def extract_lime_scores_(model, data, data_split_name, model_random_seed, no_of_
 
         exp = explainer.explain_instance(
             train_ls[annot_id]["split example"], 
-            prediction.predictor, 
+            lime_predictor.predictor, 
             num_samples = 500, 
             num_features = len(set(train_ls[annot_id]["split example"])) 
         )
@@ -238,6 +238,79 @@ def extract_lime_scores_(model, data, data_split_name, model_random_seed, no_of_
     return
 
 
+from src.evaluation.experiments.shap_predictor import ShapleyModelWrapper
+from captum.attr import DeepLiftShap
+
+def extract_shap_values_(model, data, data_split_name, model_random_seed, no_of_labels, max_seq_len, tokenizer):
+    
+    
+    fname = os.path.join(
+        os.getcwd(),
+        args["data_dir"],
+        "importance_scores",
+        ""
+    )
+
+    fname += f"{data_split_name}_importance_scores_{model_random_seed}.npy"
+
+    ## retrieve importance scores
+    importance_scores = np.load(fname, allow_pickle = True).item()
+
+    explainer = DeepLiftShap(ShapleyModelWrapper(model))
+
+    pbar = trange(len(data) * data.batch_size, desc=f"extracting shap scores for -> {data_split_name}", leave=True)
+
+    ## we are interested in token level features
+    for batch in data:
+
+        model.eval()
+        model.zero_grad()
+
+        batch = {
+            "annotation_id" : batch["annotation_id"],
+            "input_ids" : batch["input_ids"].squeeze(1).to(device),
+            "lengths" : batch["lengths"].to(device),
+            "labels" : batch["label"].to(device),
+            "token_type_ids" : batch["token_type_ids"].squeeze(1).to(device),
+            "attention_mask" : batch["attention_mask"].squeeze(1).to(device),
+            "query_mask" : batch["query_mask"].squeeze(1).to(device),
+            "special_tokens" : batch["special tokens"],
+            "retain_gradient" : False ## we do not need it
+        }
+            
+        assert batch["input_ids"].size(0) == len(batch["labels"]), "Error: batch size for item 1 not in correct position"
+        
+        original_prediction, _ =  model(**batch)
+
+        embeddings = model.wrapper.model.embeddings.word_embeddings.weight[batch["input_ids"].long()]
+        baseline = torch.zeros_like(embeddings, requires_grad = True).to(device)
+
+        attribution = explainer.attribute(embeddings.requires_grad_(True), baseline, target = original_prediction.argmax(-1))
+
+        attribution = attribution.sum(-1)
+
+        attribution = torch.masked_fill(
+            attribution, 
+            (batch["query_mask"] == 0).bool(), 
+            float("-inf")
+        )
+
+        for _i_ in range(attribution.size(0)):
+
+            annotation_id = batch["annotation_id"][_i_]
+
+            importance_scores[annotation_id]["shap"] = attribution[_i_].detach().cpu().numpy()
+
+
+        pbar.update(data.batch_size)
+
+     ## save them
+    np.save(fname, importance_scores)
+
+    print(f"appended shap scores in -> {fname}")
+
+    return
+
 def rationale_creator_(data, data_split_name, variable, tokenizer):
 
     ## get the thresholder fun
diff --git a/src/evaluation/experiments/shap_predictor.py b/src/evaluation/experiments/shap_predictor.py
new file mode 100644
index 00000000..1d9d6408
--- /dev/null
+++ b/src/evaluation/experiments/shap_predictor.py
@@ -0,0 +1,37 @@
+import torch
+from torch import nn
+import json
+import config.cfg
+from config.cfg import AttrDict
+
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+with open(config.cfg.config_directory + 'instance_config.json', 'r') as f:
+    args = AttrDict(json.load(f))
+
+class ShapleyModelWrapper(nn.Module):
+
+    def __init__(self, model):
+
+        super(ShapleyModelWrapper, self).__init__()
+
+        self.model = model
+
+    def forward(self, embeddings):        
+        
+        head_mask = [None] * self.model.wrapper.model.config.num_hidden_layers
+
+        encoder_outputs = self.model.wrapper.model.encoder(
+            embeddings,
+            head_mask=head_mask,
+            output_attentions=self.model.wrapper.model.config.output_attentions,
+            output_hidden_states=self.model.wrapper.model.config.output_attentions,
+            return_dict=self.model.wrapper.model.config.return_dict
+        )
+
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.model.wrapper.model.pooler(sequence_output) if self.model.wrapper.model.pooler is not None else None
+
+        logits = self.model.output_layer(pooled_output)
+
+        return logits
\ No newline at end of file
diff --git a/src/models/__pycache__/bert.cpython-36.pyc b/src/models/__pycache__/bert.cpython-36.pyc
index 0cf255a5..4af36d7a 100644
Binary files a/src/models/__pycache__/bert.cpython-36.pyc and b/src/models/__pycache__/bert.cpython-36.pyc differ
diff --git a/src/models/__pycache__/bert_components.cpython-36.pyc b/src/models/__pycache__/bert_components.cpython-36.pyc
index 032329dd..505f804b 100644
Binary files a/src/models/__pycache__/bert_components.cpython-36.pyc and b/src/models/__pycache__/bert_components.cpython-36.pyc differ
diff --git a/src/models/bert.py b/src/models/bert.py
index a805c9e6..086efdb3 100644
--- a/src/models/bert.py
+++ b/src/models/bert.py
@@ -4,7 +4,6 @@
 from transformers import AutoModel, AutoConfig,  AutoModelForQuestionAnswering
 import json 
 from src.models.bert_components import BertModelWrapper
-
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 
 import config.cfg
@@ -32,7 +31,7 @@ def __init__(self, output_dim = 2, dropout=0.1):
             attention weights : the attenion weights corresponding to the tokens from the last layer
         """
 
-        self.output_dim = output_dim        
+        self.output_dim = output_dim
         self.dropout = dropout
 
         self.bert_config = AutoConfig.from_pretrained(args["model"], output_attentions = True)   
@@ -50,8 +49,9 @@ def __init__(self, output_dim = 2, dropout=0.1):
         torch.nn.init.xavier_uniform_(self.output_layer.weight)
         self.output_layer.bias.data.fill_(0.0)
 
+    
     def forward(self, **inputs):
-
+    
         if "ig" not in inputs: inputs["ig"] = int(1)
 
         self.output, pooled_output, attention_weights = self.wrapper(
@@ -119,125 +119,3 @@ def integrated_grads(self, original_grad, original_pred, steps = 10, **inputs):
 
         return ig
 
-
-# class qa_bert(nn.Module):
-#     def __init__(self, masked_list = [0,101,102], output_dim = None, dropout=0.1):
-        
-#         super(qa_bert, self).__init__()
-
-#         """
-#         QA bert
-#         Args:
-#             output_dim : number of labels to classify
-#             mask_list : a list of tokens that we want to pad out (e.g. SEP, CLS tokens)
-#                         when normalising the attentions. WARNING mask list is not used 
-#                         on the input
-#         Input:
-#             input : the one hot encoded sequence representation, size [b_size, seq_len]
-#             lengths : the lengths of the batched sequence, size [b_size, 1]
-#             retain_gradient : retain the gradients for the embedding layer to obtain saliency scores
-#         Output:
-#             output : The concatenated encoded representations, size [b_size, seq_len, hidden_dim]
-#             last_hidden : The encoded representation of the last step, size [b_size, hidden_dim]
-#         """
-      
-#         self.bert_hidden_dim = 768
-#         self.masked_list = masked_list
-#         self.dropout = dropout
-
-#         bert_model =  AutoModelForQuestionAnswering.from_pretrained(args["model"], 
-#                                                             return_dict = True,
-#                                                             output_attentions = True)      
-
-
-#         self.bert_model = BertModelWrapperQA(bert_model)  
-
-#     def forward(self, start_positions = None, end_positions = None, **inputs):
-
-#         outputs = self.bert_model(
-#                         input_ids = inputs["input_ids"], 
-#                         token_type_ids = inputs["token_type_ids"],
-#                         attention_mask = inputs["attention_mask"],
-#                         salient_scores = inputs["salient_scores"]
-#                         )
-
-
-
-#         if inputs["retain_gradient"]:
-            
-#             self.weights_or.retain_grad()
-#             self.bert_model.model.embeddings.word_embeddings.weight.retain_grad()
-
-
-#         self.weights = outputs.attentions[-1][:, :, 0, :].mean(1)
-#         # to retain gradients
-#         self.weights_or = outputs.attentions[-1]
-
-#         if self.bert_model.model.config.use_return_dict:del outputs["attentions"]
-#         else: del outputs[-1]
-        
-#         if start_positions is not None and end_positions is not None:
-            
-#             if self.bert_model.model.config.use_return_dict: start_logits, end_logits = outputs.start_logits, outputs.end_logits
-#             else: start_logits, end_logits = outputs[0], outputs[1]
-
-#             # mask from mask_list used to remove SOS, EOS and PAD tokens
-#             normalised_mask = torch.zeros_like(start_logits).bool()
-        
-#             for item in self.masked_list:
-            
-#                 normalised_mask += (inputs["input_ids"] == item).bool()
-       
-#             # mask unwanted tokens
-#             start_logits = torch.masked_fill(start_logits, normalised_mask.to(device), float("-inf"))
-#             end_logits = torch.masked_fill(end_logits, normalised_mask.to(device), float("-inf"))
-
-#             start_logits = torch.softmax(start_logits, dim = -1)
-#             end_logits = torch.softmax(end_logits, dim = -1)
-
-#             total_loss = None
-
-#             # If we are on multi-GPU, split add a dimension
-#             if len(start_positions.size()) > 1:
-#                 start_positions = start_positions.squeeze(-1)
-#             if len(end_positions.size()) > 1:
-#                 end_positions = end_positions.squeeze(-1)
-#             # sometimes the start/end positions are outside our model inputs, we ignore these terms
-#             ignored_index = start_logits.size(1)
-#             start_positions.clamp_(0, ignored_index)
-#             end_positions.clamp_(0, ignored_index)
-
-#             loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
-#             start_loss = loss_fct(start_logits, start_positions)
-#             end_loss = loss_fct(end_logits, end_positions)
-#             total_loss = (start_loss + end_loss) / 2
-
-#             if self.bert_model.model.config.use_return_dict: outputs["loss"], outputs["start_logits"], outputs["end_logits"] = total_loss, start_logits, end_logits
-#             else: outputs = [total_loss, start_logits, end_logits]
-        
-#         return outputs, self.weights
-
-#     def normalise_scores(self, scores, sequence):
-        
-#         """
-#         returns word-piece normalised scores
-#         receives as input the scores {attention-weights, gradients} from bert and the sequence
-#         the sequence is used to create a mask with default tokens masked (101,102,0)
-#         which correspond to (CLS, SEP, PAD)
-#         """
-
-#         # mask from mask_list used to remove SOS, EOS and PAD tokens
-#         self.normalised_mask = torch.zeros_like(scores).bool()
-    
-#         for item in self.masked_list:
-        
-#             self.normalised_mask += (sequence == item).bool()
-
-#         # mask unwanted tokens
-#         scores = torch.masked_fill(scores, self.normalised_mask.to(device), 0)
-   
-#         # return normalised word-piece scores       
-#         return scores / scores.sum(-1, keepdim = True)
-
-
-
diff --git a/src/models/bert_components.py b/src/models/bert_components.py
index c832f846..d03d6c7a 100644
--- a/src/models/bert_components.py
+++ b/src/models/bert_components.py
@@ -96,70 +96,4 @@ def forward(self, inpt_seq, attention_mask, token_type_ids, ig = int(1)):
 
         return sequence_output, pooled_output, attentions
 
-# class BertModelWrapperQA(nn.Module):
-    
-#     def __init__(self, model):
-    
-#         super(BertModelWrapperQA, self).__init__()
-
-#         """
-#         BERT model wrapper for QA
-#         """
-
-#         self.model = model
-
-#         if args["mag_layer"]:
-
-#             self.mag = mag(hidden_dim = self.model.config.hidden_size)
-
-#             assert args["mag_layer"] < self.model.config.num_hidden_layers
-
-#         else: self.mag = None
-
-        
-#     def forward(self, input_ids, attention_mask, token_type_ids, salient_scores = None):        
-        
-#         embeddings, _ = bert_embeddings(self.model.bert, input_ids, 
-#                                             position_ids = None, 
-#                                             token_type_ids = token_type_ids
-#                                         )
-  
-#         extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-
-#         extended_attention_mask = extended_attention_mask.to(dtype=next(self.model.parameters()).dtype) # fp16 compatibility
-#         extended_attention_mask = (1 - extended_attention_mask) * -10000.0
-
-#         head_mask = [None] * self.model.config.num_hidden_layers
-
-#         encoder_outputs = bert_encoder(self.model.bert,
-#                                 embeddings,
-#                                 attention_mask=extended_attention_mask,
-#                                 head_mask=head_mask,
-#                                 output_attentions=self.model.config.output_attentions,
-#                                 output_hidden_states=self.model.config.output_attentions,
-#                                 return_dict=self.model.config.return_dict,
-#                                 mag = self.mag,
-#                                 salient_scores = salient_scores)
-
-#         sequence_output = encoder_outputs[0]
-
-#         attentions = encoder_outputs[2]
-
-#         logits = self.model.qa_outputs(sequence_output)
-#         start_logits, end_logits = logits.split(1, dim=-1)
-#         start_logits = start_logits.squeeze(-1)
-#         end_logits = end_logits.squeeze(-1)
-
-#         if self.model.config.use_return_dict:
-            
-#             results =  aDict({
-#                             "start_logits":start_logits, 
-#                             "end_logits": end_logits,
-#                             "attentions" : attentions
-#                         })
-
-#         else:
 
-#             results = [start_logits, end_logits, attentions]
-            
-#         return results
\ No newline at end of file
diff --git a/src/variable_rationales/__pycache__/var_length_feat.cpython-36.pyc b/src/variable_rationales/__pycache__/var_length_feat.cpython-36.pyc
index fe536ba0..b09b1d6a 100644
Binary files a/src/variable_rationales/__pycache__/var_length_feat.cpython-36.pyc and b/src/variable_rationales/__pycache__/var_length_feat.cpython-36.pyc differ
diff --git a/src/variable_rationales/var_length_feat.py b/src/variable_rationales/var_length_feat.py
index c21f8358..0bc0da8f 100644
--- a/src/variable_rationales/var_length_feat.py
+++ b/src/variable_rationales/var_length_feat.py
@@ -112,12 +112,6 @@ def rationale_length_computer_(
 
             stepwise_preds.append(yhat.argmax(-1).detach().cpu().numpy())
 
-            # ### normalized divergence
-            # full_div = divergence_fun(
-            #     torch.softmax(y_original - zero_logits, dim = -1), 
-            #     torch.softmax(yhat - zero_logits, dim = -1)
-            # ) 
-
             full_div = divergence_fun(
                 torch.softmax(y_original, dim = -1), 
                 torch.softmax(yhat, dim = -1)
@@ -197,12 +191,12 @@ def get_rationale_metadata_(model, data_split_name, data, model_random_seed):
         data_split_name + "-rationale_metadata.npy"
     )
 
-    if os.path.isfile(fname):
+    # if os.path.isfile(fname):
 
-        print(f"rationale metadata file exists at {fname}") 
-        print("remove if you would like to rerun")
+    #     print(f"rationale metadata file exists at {fname}") 
+    #     print("remove if you would like to rerun")
 
-        return 
+    #     return 
 
     desc = f'creating rationale data for -> {data_split_name}'
 
@@ -278,7 +272,7 @@ def get_rationale_metadata_(model, data_split_name, data, model_random_seed):
 
         
         ## percentage of flips
-        for feat_name in {"lime", "random", "attention",  "gradients",   "ig", "scaled attention"}:
+        for feat_name in {"lime", "random", "attention",  "gradients",   "ig", "scaled attention", "shap"}:
             
             feat_score = batch_from_dict(
                 batch_data = batch,
@@ -308,7 +302,7 @@ def get_rationale_metadata_(model, data_split_name, data, model_random_seed):
             init_fixed_div = float("-inf")
             init_var_div = float("-inf")
 
-            for feat_name in {"attention", "scaled attention", "gradients", "ig", "lime"}:
+            for feat_name in {"attention", "scaled attention", "gradients", "ig", "lime", "shap"}:
                 
                 fixed_div = rationale_results[annotation_id][feat_name]["fixed-length divergence"]
                 var_div = rationale_results[annotation_id][feat_name]["variable-length divergence"]