diff --git a/combo/data/api.py b/combo/data/api.py
index f25d035d5f428314055185449799e2ca5ff8987c..8a9208f1039cd4831cb7368a0f4b7c5292032e61 100644
--- a/combo/data/api.py
+++ b/combo/data/api.py
@@ -1,5 +1,6 @@
 import collections
 import dataclasses
+import html
 import json
 import os
 import string
@@ -55,12 +56,12 @@ class Sentence:
         # numpy.savez(hash + '_trimmed.npz', self.relation_distribution[1:, 1:])
 
         return json.dumps({
-            "tokens": [(t.token, t.lemma, t.upostag, t.xpostag, t. feats, t.head, t.deprel) for t in self.tokens],
+            "tokens": [(str(html.unescape(t.token)), str(html.unescape(t.lemma)), t.upostag, t.xpostag, t. feats, t.head, t.deprel) for t in self.tokens],
             # "sentence_embedding": self.sentence_embedding,
             "head": [t.head for t in self.tokens],
             "relation_distribution_hash": hash,
             "path_file": str(os.path.join(save_relation_distribution_path, hash + '.npz'))
-        }, cls=NumpyArrayEncoder)
+        }, cls=NumpyArrayEncoder, ensure_ascii=False)
 
     def __len__(self):
         return len(self.tokens)
diff --git a/combo/predict.py b/combo/predict.py
index 90915a89bc9ab5101bef1bac4f249f061098dba1..356cafae1851a757168e8ff94b7182afdd203192 100644
--- a/combo/predict.py
+++ b/combo/predict.py
@@ -58,6 +58,7 @@ class COMBO(predictor.Predictor):
             sys.exit(1)
 
     def predict(self, sentence: Union[str, List[str], List[List[str]], List[data.Sentence]]):
+        sentence = sentence.replace(".", " .")
         if isinstance(sentence, str):
             if isinstance(self._tokenizer,lambo.LamboTokenizer):
                 segmented = self._tokenizer.segment(sentence)
@@ -247,7 +248,7 @@ class COMBO(predictor.Predictor):
     
     @classmethod
     def with_lambo_tokenizer(cls, model: models.Model,
-                             dataset_reader: allen_data.DatasetReader, lambo_model_name : str = 'en'):
+                             dataset_reader: allen_data.DatasetReader, lambo_model_name : str = 'de'):
         return cls(model, dataset_reader, lambo.LamboTokenizer(lambo_model_name))
 
     @classmethod