Release 1.0.0b2.

combo/predict.py

@@ -29,8 +29,9 @@ class COMBO(predictor.Predictor):
         super().__init__(model, dataset_reader)
         self.batch_size = batch_size
         self.vocab = model.vocab
-        self._dataset_reader.generate_labels = False
-        self._dataset_reader.lazy = True
+        self.dataset_reader = self._dataset_reader
+        self.dataset_reader.generate_labels = False
+        self.dataset_reader.lazy = True
         self._tokenizer = tokenizer
         self.without_sentence_embedding = False
         self.line_to_conllu = line_to_conllu
@@ -112,7 +113,7 @@ class COMBO(predictor.Predictor):
             tokens = sentence
         else:
             raise ValueError("Input must be either string or list of strings.")
-        return self._dataset_reader.text_to_instance(tokens2conllu(tokens))
+        return self.dataset_reader.text_to_instance(tokens2conllu(tokens))
 
     @overrides
     def load_line(self, line: str) -> common.JsonDict:
@@ -125,7 +126,7 @@ class COMBO(predictor.Predictor):
         if self.without_sentence_embedding:
             outputs.sentence_embedding = []
         if self.line_to_conllu:
-            return sentence2conllu(outputs, keep_semrel=self._dataset_reader.use_sem).serialize()
+            return sentence2conllu(outputs, keep_semrel=self.dataset_reader.use_sem).serialize()
         else:
             return outputs.to_json()
 
@@ -134,7 +135,7 @@ class COMBO(predictor.Predictor):
         return {"sentence": sentence}
 
     def _to_input_instance(self, sentence: data.Sentence) -> allen_data.Instance:
-        return self._dataset_reader.text_to_instance(sentence2conllu(sentence))
+        return self.dataset_reader.text_to_instance(sentence2conllu(sentence))
 
     def _predictions_as_tree(self, predictions: Dict[str, Any], instance: allen_data.Instance):
         tree = instance.fields["metadata"]["input"]