Make mypy happy

flair/models/relation_classifier_model.py

@@ -435,8 +435,8 @@ def _encode_sentence(
         # since there may be multiple occurrences of the same entity mentioned in the sentence.
         # Therefore, we use the span's position in the sentence.
         encoded_sentence_tokens: list[str] = []
-        head_idx = None
-        tail_idx = None
+        head_idx = -10000
+        tail_idx = 10000
         for token in original_sentence:
             if token is head.span[0]:
                 head_idx = len(encoded_sentence_tokens)
@@ -456,8 +456,6 @@ def _encode_sentence(
         if abs(head_idx - tail_idx) > self._max_allowed_tokens_between_entities:
             return None
 
-        print(head_idx, tail_idx)
-
         # remove excess tokens left and right of entity pair to make encoded sentence shorter
         encoded_sentence_tokens = self._slice_encoded_sentence_to_max_allowed_length(
             encoded_sentence_tokens, head_idx, tail_idx
@@ -511,13 +509,15 @@ def _encode_sentence_for_inference(
         Returns: Encoded sentences annotated with their gold relation and the corresponding relation in the original sentence
         """
         for head, tail, gold_label in self._entity_pair_permutations(sentence):
-            masked_sentence: EncodedSentence = self._encode_sentence(
+            masked_sentence = self._encode_sentence(
                 head=head,
                 tail=tail,
                 gold_label=gold_label if gold_label is not None else self.zero_tag_value,
             )
             original_relation: Relation = Relation(first=head.span, second=tail.span)
-            yield masked_sentence, original_relation
+
+            if masked_sentence is not None:
+                yield masked_sentence, original_relation
 
     def _encode_sentence_for_training(self, sentence: Sentence) -> Iterator[EncodedSentence]:
         """Create Encoded Sentences and Relation pairs for Training.
@@ -534,13 +534,14 @@ def _encode_sentence_for_training(self, sentence: Sentence) -> Iterator[EncodedS
                 else:
                     continue  # Skip generated data points that do not express an originally annotated relation
 
-            masked_sentence: EncodedSentence = self._encode_sentence(
+            masked_sentence = self._encode_sentence(
                 head=head,
                 tail=tail,
                 gold_label=gold_label,
             )
 
-            yield masked_sentence
+            if masked_sentence is not None:
+                yield masked_sentence
 
     def transform_sentence(self, sentences: Union[Sentence, list[Sentence]]) -> list[EncodedSentence]:
         """Transforms sentences into encoded sentences specific to the `RelationClassifier`.
@@ -562,7 +563,6 @@ def transform_sentence(self, sentences: Union[Sentence, list[Sentence]]) -> list
             encoded_sentence
             for sentence in sentences
             for encoded_sentence in self._encode_sentence_for_training(sentence)
-            if encoded_sentence is not None
         ]
 
     def transform_dataset(self, dataset: Dataset[Sentence]) -> FlairDatapointDataset[EncodedSentence]:
@@ -691,10 +691,6 @@ def predict(
                 )
             )
 
-            sentences_with_relation_reference = [
-                item for item in sentences_with_relation_reference if item[0] is not None
-            ]
-
             encoded_sentences = [x[0] for x in sentences_with_relation_reference]
             loss = super().predict(
                 encoded_sentences,