diff --git a/src/learning/transformers.py b/src/learning/transformers.py
index f99c23b..e6c5194 100644
--- a/src/learning/transformers.py
+++ b/src/learning/transformers.py
@@ -328,7 +328,7 @@ class RecurrentEmbedder:
         self.posteriorEmbedder = MetaClassifier(
             SVC(kernel='rbf', gamma='auto', probability=True, cache_size=1000, random_state=1), n_jobs=options.n_jobs)
 
-    def fit(self, lX, ly, lV=None, batch_size=64, nepochs=2, val_epochs=1):
+    def fit(self, lX, ly, lV=None, batch_size=64, nepochs=200, val_epochs=1):
         print('### Gated Recurrent Unit View Generator (G)')
         # could be better to init model here at first .fit() call!
         if self.model is None:
@@ -412,14 +412,19 @@ class RecurrentEmbedder:
         batcher_transform = BatchGRU(batch_size, batches_per_epoch=batch_size, languages=self.langs,
                                      lpad=self.multilingual_index.l_pad())
 
-        l_devel_index = self.multilingual_index.l_devel_index()
-        l_devel_target = self.multilingual_index.l_devel_target()
-        # l_devel_target = {k: v[:len(data[lang])] for k, v in l_devel_target.items()}
+        # l_devel_index = self.multilingual_index.l_devel_index()
 
+        l_devel_target = self.multilingual_index.l_devel_target()
+        l_devel_target = {k: v[:len(data[k])] for k, v in l_devel_target.items()}    # todo -> debug
+        for batch, _, target, lang, in batchify(l_index=data,
+                                                l_post=None,
+                                                llabels=l_devel_target,
+                                                batchsize=batch_size,
+                                                lpad=self.multilingual_index.l_pad()):
         # for idx, (batch, post, bert_emb, target, lang) in enumerate(
         #         batcher_transform.batchify(l_devel_index, None, None, l_devel_target)):
-        for idx, (batch, post, bert_emb, target, lang) in enumerate(
-                batcher_transform.batchify(data, None, None, l_devel_target)):
+        # for idx, (batch, post, bert_emb, target, lang) in enumerate(
+        #         batcher_transform.batchify(data, None, None, l_devel_target)):
             if lang not in lX.keys():
                 lX[lang] = self.model.get_embeddings(batch, lang)
                 ly[lang] = target.cpu().detach().numpy()