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chore: enable singleton-comparison
and cleanup (#3849)
* enable singleton-comparison * fix triadaptive_model bug
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@ -444,7 +444,7 @@ class BaseDocumentStore(BaseComponent):
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f"Split by sentence not supported.\n"
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f"Split by sentence not supported.\n"
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f"Please set 'split_by' to either 'word' or 'passage' in the supplied PreProcessor."
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f"Please set 'split_by' to either 'word' or 'passage' in the supplied PreProcessor."
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)
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)
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assert preprocessor.split_respect_sentence_boundary == False, (
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assert preprocessor.split_respect_sentence_boundary is False, (
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f"split_respect_sentence_boundary not supported yet.\n"
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f"split_respect_sentence_boundary not supported yet.\n"
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f"Please set 'split_respect_sentence_boundary' to False in the supplied PreProcessor."
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f"Please set 'split_respect_sentence_boundary' to False in the supplied PreProcessor."
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)
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)
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@ -452,15 +452,15 @@ class BaseDocumentStore(BaseComponent):
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f"Overlapping documents are currently not supported when adding eval data.\n"
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f"Overlapping documents are currently not supported when adding eval data.\n"
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f"Please set 'split_overlap=0' in the supplied PreProcessor."
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f"Please set 'split_overlap=0' in the supplied PreProcessor."
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)
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)
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assert preprocessor.clean_empty_lines == False, (
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assert preprocessor.clean_empty_lines is False, (
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f"clean_empty_lines currently not supported when adding eval data.\n"
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f"clean_empty_lines currently not supported when adding eval data.\n"
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f"Please set 'clean_empty_lines=False' in the supplied PreProcessor."
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f"Please set 'clean_empty_lines=False' in the supplied PreProcessor."
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)
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)
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assert preprocessor.clean_whitespace == False, (
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assert preprocessor.clean_whitespace is False, (
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f"clean_whitespace is currently not supported when adding eval data.\n"
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f"clean_whitespace is currently not supported when adding eval data.\n"
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f"Please set 'clean_whitespace=False' in the supplied PreProcessor."
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f"Please set 'clean_whitespace=False' in the supplied PreProcessor."
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)
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)
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assert preprocessor.clean_header_footer == False, (
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assert preprocessor.clean_header_footer is False, (
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f"clean_header_footer is currently not supported when adding eval data.\n"
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f"clean_header_footer is currently not supported when adding eval data.\n"
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f"Please set 'clean_header_footer=False' in the supplied PreProcessor."
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f"Please set 'clean_header_footer=False' in the supplied PreProcessor."
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)
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)
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@ -1359,7 +1359,7 @@ class PineconeDocumentStore(BaseDocumentStore):
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# Explode dict of dicts into single flattened dict
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# Explode dict of dicts into single flattened dict
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for key, value in meta.items():
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for key, value in meta.items():
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# Replace any None values with empty strings
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# Replace any None values with empty strings
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if value == None:
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if value is None:
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value = ""
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value = ""
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# format key
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# format key
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new_key = f"{parent_key}.{key}" if parent_key else key
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new_key = f"{parent_key}.{key}" if parent_key else key
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@ -103,7 +103,7 @@ class Inferencer:
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# TODO add support for multiple prediction heads
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# TODO add support for multiple prediction heads
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self.name = name if name != None else f"anonymous-{self.task_type}"
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self.name = name if name is not None else f"anonymous-{self.task_type}"
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self.return_class_probs = return_class_probs
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self.return_class_probs = return_class_probs
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model.connect_heads_with_processor(processor.tasks, require_labels=False)
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model.connect_heads_with_processor(processor.tasks, require_labels=False)
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@ -305,7 +305,7 @@ class TriAdaptiveModel(nn.Module):
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# Forward pass for text passages and tables
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# Forward pass for text passages and tables
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if "passage_input_ids" in kwargs.keys():
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if "passage_input_ids" in kwargs.keys():
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table_mask = torch.flatten(kwargs["is_table"]) == True
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table_mask = torch.flatten(kwargs["is_table"]) == 1
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# Current batch consists of only tables
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# Current batch consists of only tables
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if all(table_mask):
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if all(table_mask):
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@ -1089,7 +1089,7 @@ class FARMReader(BaseReader):
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# create new one
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# create new one
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else:
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else:
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# We don't need to create an answer dict if is_impossible / no_answer
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# We don't need to create an answer dict if is_impossible / no_answer
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if label.no_answer == True:
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if label.no_answer is True:
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aggregated_per_question[aggregation_key] = {
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aggregated_per_question[aggregation_key] = {
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"id": str(hash(str(doc_id) + label.query)),
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"id": str(hash(str(doc_id) + label.query)),
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"question": label.query,
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"question": label.query,
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@ -669,7 +669,7 @@ class MultiLabel:
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if drop_negative_labels:
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if drop_negative_labels:
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labels = [l for l in labels if is_positive_label(l)]
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labels = [l for l in labels if is_positive_label(l)]
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if drop_no_answers:
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if drop_no_answers:
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labels = [l for l in labels if l.no_answer == False]
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labels = [l for l in labels if l.no_answer is False]
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self._labels = labels
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self._labels = labels
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self._query = self._aggregate_labels(key="query", must_be_single_value=True)[0]
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self._query = self._aggregate_labels(key="query", must_be_single_value=True)[0]
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@ -51,7 +51,7 @@ def aggregate_labels(
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# drop no_answers in order to not create empty MultiLabels
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# drop no_answers in order to not create empty MultiLabels
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if drop_no_answers:
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if drop_no_answers:
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labels = [label for label in labels if label.no_answer == False]
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labels = [label for label in labels if label.no_answer is False]
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# add filters for closed domain and dynamic metadata aggregation
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# add filters for closed domain and dynamic metadata aggregation
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for l in labels:
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for l in labels:
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