diff --git a/refactor/main.py b/refactor/main.py
index 45487b1..bea0067 100644
--- a/refactor/main.py
+++ b/refactor/main.py
@@ -15,7 +15,7 @@ def main(args):
     _DATASET = '/home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle'
     EMBEDDINGS_PATH = '/home/andreapdr/gfun/embeddings'
     data = MultilingualDataset.load(_DATASET)
-    data.set_view(languages=['it'], categories=[0, 1])
+    # data.set_view(languages=['it'], categories=[0, 1])
     lX, ly = data.training()
     lXte, lyte = data.test()
 
@@ -28,7 +28,7 @@ def main(args):
     # gFun = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=N_JOBS)
     # gFun = MuseGen(muse_dir='/home/andreapdr/funneling_pdr/embeddings', n_jobs=N_JOBS)
     # gFun = WordClassGen(n_jobs=N_JOBS)
-    gFun = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=5, nepochs=100,
+    gFun = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=256, nepochs=50,
                         gpus=args.gpus, n_jobs=N_JOBS)
     # gFun = BertGen(multilingualIndex, gpus=args.gpus, batch_size=128, n_jobs=N_JOBS)
 
diff --git a/refactor/models/pl_gru.py b/refactor/models/pl_gru.py
index 2e3ecf1..1ed8314 100644
--- a/refactor/models/pl_gru.py
+++ b/refactor/models/pl_gru.py
@@ -29,10 +29,11 @@ class RecurrentModel(pl.LightningModule):
         self.drop_embedding_range = drop_embedding_range
         self.drop_embedding_prop = drop_embedding_prop
         self.loss = torch.nn.BCEWithLogitsLoss()
-        self.microf1 = F1(num_classes=output_size, multilabel=True, average='micro')
-        self.macrof1 = F1(num_classes=output_size, multilabel=True, average='macro')
+        # self.microf1 = F1(num_classes=output_size, multilabel=True, average='micro')
+        # self.macrof1 = F1(num_classes=output_size, multilabel=True, average='macro')
         self.accuracy = Accuracy()
-        self.customMetrics = CustomF1(num_classes=output_size, device=self.gpus)
+        self.customMicroF1 = CustomF1(num_classes=output_size, average='micro', device=self.gpus)
+        self.customMacroF1 = CustomF1(num_classes=output_size, average='macro', device=self.gpus)
 
         self.lPretrained_embeddings = nn.ModuleDict()
         self.lLearnable_embeddings = nn.ModuleDict()
@@ -103,10 +104,12 @@ class RecurrentModel(pl.LightningModule):
         # Squashing logits through Sigmoid in order to get confidence score
         predictions = torch.sigmoid(logits) > 0.5
         accuracy = self.accuracy(predictions, ly)
-        custom = self.customMetrics(predictions, ly)
+        microF1 = self.customMicroF1(predictions, ly)
+        macroF1 = self.customMacroF1(predictions, ly)
         self.log('train-loss', loss, on_step=True, on_epoch=True, prog_bar=False, logger=True)
         self.log('train-accuracy', accuracy, on_step=True, on_epoch=True, prog_bar=False, logger=True)
-        self.log('custom', custom, on_step=True, on_epoch=True, prog_bar=True, logger=True)
+        self.log('microF1', microF1, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+        self.log('macroF1', macroF1, on_step=False, on_epoch=True, prog_bar=True, logger=True)
         return {'loss': loss}
 
     def validation_step(self, val_batch, batch_idx):
@@ -212,4 +215,17 @@ class CustomF1(Metric):
                 return (num / den).to(self.device)
             return torch.FloatTensor([1.]).to(self.device)
         if self.average == 'macro':
-            raise NotImplementedError
+            class_specific = []
+            for i in range(self.num_classes):
+                class_tp = self.true_positive[i]
+                # class_tn = self.true_negative[i]
+                class_fp = self.false_positive[i]
+                class_fn = self.false_negative[i]
+                num = 2.0 * class_tp
+                den = 2.0 * class_tp + class_fp + class_fn
+                if den > 0:
+                    class_specific.append(num / den)
+                else:
+                    class_specific.append(1.)
+            average = torch.sum(torch.Tensor(class_specific))/self.num_classes
+            return average.to(self.device)