diff --git a/src/learning/transformers.py b/src/learning/transformers.py
index 0032460..f99c23b 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=200, val_epochs=1):
+    def fit(self, lX, ly, lV=None, batch_size=64, nepochs=2, 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:
@@ -397,6 +397,16 @@ class RecurrentEmbedder:
     def _get_doc_embeddings(self, lX, batch_size=64):
         assert self.is_trained, 'Model is not trained, cannot call transform before fitting the model!'
         print('Generating document embeddings via GRU')
+        data = {}
+        for lang in lX.keys():
+            indexed = index(data=lX[lang],
+                            vocab=self.multilingual_index.l_index[lang].word2index,
+                            known_words=set(self.multilingual_index.l_index[lang].word2index.keys()),
+                            analyzer=self.multilingual_index.l_vectorizer.get_analyzer(lang),
+                            unk_index=self.multilingual_index.l_index[lang].unk_index,
+                            out_of_vocabulary=self.multilingual_index.l_index[lang].out_of_vocabulary)
+            data[lang] = indexed
+
         lX = {}
         ly = {}
         batcher_transform = BatchGRU(batch_size, batches_per_epoch=batch_size, languages=self.langs,
@@ -404,9 +414,12 @@ class RecurrentEmbedder:
 
         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()}
 
+        # 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(l_devel_index, None, None, l_devel_target)):
+                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()