Redo changing of inti

haystack/reader/farm.py

@@ -23,6 +23,49 @@ class FARMReader:
      - fine-tune the model on QA data via train()
     """
 
+    def __init__(
+        self,
+        model_name_or_path,
+        context_window_size=30,
+        batch_size=50,
+        use_gpu=True,
+        no_ans_boost=None,
+        n_candidates_per_paragraph=1):
+        """
+        :param model_name_or_path: directory of a saved model or the name of a public model:
+                                   - 'bert-base-cased'
+                                   - 'deepset/bert-base-cased-squad2'
+                                   - 'deepset/bert-base-cased-squad2'
+                                   - 'distilbert-base-uncased-distilled-squad'
+                                   ....
+                                   See https://huggingface.co/models for full list of available models.
+        :param context_window_size: The size, in characters, of the window around the answer span that is used when displaying the context around the answer.
+        :param batch_size: Number of samples the model receives in one batch for inference
+                           Memory consumption is much lower in inference mode. Recommendation: increase the batch size to a value so only a single batch is used.
+        :param use_gpu: Whether to use GPU (if available)
+        :param no_ans_boost: How much the no_answer logit is boosted/increased.
+                             Possible values: None (default) = disable returning "no answer" predictions
+                                              Negative = lower chance of "no answer" being predicted
+                                              Positive = increase chance of "no answer"
+        :param n_candidates_per_paragraph: How many candidate answers are extracted per text sequence that the model can process at once (depends on `max_seq_len`).
+                                           Note: - This is not the number of "final answers" you will receive
+                                                   (see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
+                                                 - FARM includes no_answer in the sorted list of predictions
+
+
+        """
+
+        if no_ans_boost is None:
+            no_ans_boost = 0
+            self.return_no_answers = False
+        else:
+            self.return_no_answers = True
+        self.n_candidates_per_paragraph = n_candidates_per_paragraph
+        self.inferencer = Inferencer.load(model_name_or_path, batch_size=batch_size, gpu=use_gpu, task_type="question_answering")
+        self.inferencer.model.prediction_heads[0].context_window_size = context_window_size
+        self.inferencer.model.prediction_heads[0].no_ans_boost = no_ans_boost
+        self.inferencer.model.prediction_heads[0].n_best = n_candidates_per_paragraph + 1 # including possible no_answer
+
     def train(self, data_dir, train_filename, dev_filename=None, test_file_name=None,
               use_gpu=True, batch_size=10, n_epochs=2, learning_rate=1e-5,
               max_seq_len=256, warmup_proportion=0.2, dev_split=0.1, evaluate_every=300, save_dir=None):
@@ -104,49 +147,6 @@ class FARMReader:
         self.inferencer.model = trainer.train()
         self.save(save_dir)
 
-    def __init__(
-        self,
-        model_name_or_path,
-        context_window_size=30,
-        batch_size=50,
-        use_gpu=True,
-        no_ans_boost=None,
-        n_candidates_per_paragraph=1):
-        """
-        :param model_name_or_path: directory of a saved model or the name of a public model:
-                                   - 'bert-base-cased'
-                                   - 'deepset/bert-base-cased-squad2'
-                                   - 'deepset/bert-base-cased-squad2'
-                                   - 'distilbert-base-uncased-distilled-squad'
-                                   ....
-                                   See https://huggingface.co/models for full list of available models.
-        :param context_window_size: The size, in characters, of the window around the answer span that is used when displaying the context around the answer.
-        :param batch_size: Number of samples the model receives in one batch for inference
-                           Memory consumption is much lower in inference mode. Recommendation: increase the batch size to a value so only a single batch is used.
-        :param use_gpu: Whether to use GPU (if available)
-        :param no_ans_boost: How much the no_answer logit is boosted/increased.
-                             Possible values: None (default) = disable returning "no answer" predictions
-                                              Negative = lower chance of "no answer" being predicted
-                                              Positive = increase chance of "no answer"
-        :param n_candidates_per_paragraph: How many candidate answers are extracted per text sequence that the model can process at once (depends on `max_seq_len`).
-                                           Note: - This is not the number of "final answers" you will receive
-                                                   (see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
-                                                 - FARM includes no_answer in the sorted list of predictions
-
-
-        """
-
-        if no_ans_boost is None:
-            no_ans_boost = 0
-            self.return_no_answers = False
-        else:
-            self.return_no_answers = True
-        self.n_candidates_per_paragraph = n_candidates_per_paragraph
-        self.inferencer = Inferencer.load(model_name_or_path, batch_size=batch_size, gpu=use_gpu, task_type="question_answering")
-        self.inferencer.model.prediction_heads[0].context_window_size = context_window_size
-        self.inferencer.model.prediction_heads[0].no_ans_boost = no_ans_boost
-        self.inferencer.model.prediction_heads[0].n_best = n_candidates_per_paragraph + 1 # including possible no_answer
-
     def save(self, directory):
         logger.info(f"Saving reader model to {directory}")
         self.inferencer.model.save(directory)