print tabulated results

nextus_regressor_class.py

@@ -18,13 +18,20 @@ class NextUsRegressor(nn.Module):
         return
     
     def forward(self, txts):
-        # expects a list of strings
         if type(txts) == str:
             txts = [txts]
         embedded = self.embedder.encode(np.array(txts))
+        # embedded_tensor = self.embedder(np.array(txts))
+        
         embedded_tensor = torch.tensor(embedded, dtype=torch.float32)
         regressed = self.regressor(embedded_tensor)
-        vals = regressed.flatten().tolist()
-        return str(vals)
-        #return "\n".join([str(v) for v in vals])
         
+        # return regressed.tolist()
+        # TODO: actually handle list of strings
+        vals = regressed.flatten().tolist()
+        # must return the whole thing, not just the 0-th element
+        strs = list()
+        for t, v in list(zip(txts, vals)):
+            strs.append(str(round(v, 4)) + "\t" + t[:20])
+        return "\n".join(strs)
+        # return torch.tensor(val).unsqueeze(1)