Always explain based on the positive class for IG

With integrated Gradients, always use the positive class
as the target, so that the visualization always shows
green for words that contribute to a positive prediction
and red for words that contribute to a negative prediction

lib/integrated_gradients.py

@@ -35,8 +35,8 @@ class IntegratedGradientsExplainer:
     def run_attribution_model(self, input_ids, attention_mask, index=None, layer=None, steps=20):
         try:
             output = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
-            if index is None:
-                index = output.argmax(axis=-1).item()
+#            if index is None:
+#                index = output.argmax(axis=-1).item()
 
             ablator = LayerIntegratedGradients(self.custom_forward, layer)
             input_tensor = input_ids
@@ -45,7 +45,7 @@ class IntegratedGradientsExplainer:
                     inputs=input_ids,
                     baselines=self.ref_token_id,
                     additional_forward_args=(attention_mask),
-                    target=index,
+                    target=1,
                     n_steps=steps,
             )
             return self.summarize_attributions(attributions).unsqueeze_(0), output, index