Add option to load text pairs with CSVClassificationCorpus

flair/datasets/document_classification.py

@@ -11,7 +11,7 @@
     Corpus,
     Token,
     FlairDataset,
-    Tokenizer
+    Tokenizer, DataPair
 )
 from flair.tokenization import SegtokTokenizer, SpaceTokenizer
 from flair.datasets.base import find_train_dev_test_files
@@ -454,9 +454,12 @@ def __init__(
 
         # most data sets have the token text in the first column, if not, pass 'text' as column
         self.text_columns: List[int] = []
+        self.pair_columns: List[int] = []
         for column in column_name_map:
             if column_name_map[column] == "text":
                 self.text_columns.append(column)
+            if column_name_map[column] == "pair":
+                self.pair_columns.append(column)
 
         with open(self.path_to_file, encoding=encoding) as csv_file:
 
@@ -488,33 +491,61 @@ def __init__(
 
                 if self.in_memory:
 
-                    text = " ".join(
-                        [row[text_column] for text_column in self.text_columns]
-                    )
-
-                    if self.max_chars_per_doc > 0:
-                        text = text[: self.max_chars_per_doc]
-
-                    sentence = Sentence(text, use_tokenizer=self.tokenizer)
-
-                    for column in self.column_name_map:
-                        column_value = row[column]
-                        if (
-                                self.column_name_map[column].startswith("label")
-                                and column_value
-                        ):
-                            if column_value != self.no_class_label:
-                                sentence.add_label(label_type, column_value)
+                    sentence = self._make_labeled_data_point(row)
 
-                    if 0 < self.max_tokens_per_doc < len(sentence):
-                        sentence.tokens = sentence.tokens[: self.max_tokens_per_doc]
                     self.sentences.append(sentence)
 
                 else:
                     self.raw_data.append(row)
 
                 self.total_sentence_count += 1
 
+    def _make_labeled_data_point(self, row):
+
+        # make sentence from text (and filter for length)
+        text = " ".join(
+            [row[text_column] for text_column in self.text_columns]
+        )
+
+        if self.max_chars_per_doc > 0:
+            text = text[: self.max_chars_per_doc]
+
+        sentence = Sentence(text, use_tokenizer=self.tokenizer)
+
+        if 0 < self.max_tokens_per_doc < len(sentence):
+            sentence.tokens = sentence.tokens[: self.max_tokens_per_doc]
+
+        # if a pair column is defined, make a sentence pair object
+        if len(self.pair_columns) > 0:
+
+            text = " ".join(
+                [row[pair_column] for pair_column in self.pair_columns]
+            )
+
+            if self.max_chars_per_doc > 0:
+                text = text[: self.max_chars_per_doc]
+
+            pair = Sentence(text, use_tokenizer=self.tokenizer)
+
+            if 0 < self.max_tokens_per_doc < len(sentence):
+                pair.tokens = pair.tokens[: self.max_tokens_per_doc]
+
+            data_point = DataPair(first=sentence, second=pair)
+
+        else:
+            data_point = sentence
+
+        for column in self.column_name_map:
+            column_value = row[column]
+            if (
+                    self.column_name_map[column].startswith("label")
+                    and column_value
+            ):
+                if column_value != self.no_class_label:
+                    data_point.add_label(self.label_type, column_value)
+
+        return data_point
+
     def is_in_memory(self) -> bool:
         return self.in_memory
 
@@ -527,20 +558,7 @@ def __getitem__(self, index: int = 0) -> Sentence:
         else:
             row = self.raw_data[index]
 
-            text = " ".join([row[text_column] for text_column in self.text_columns])
-
-            if self.max_chars_per_doc > 0:
-                text = text[: self.max_chars_per_doc]
-
-            sentence = Sentence(text, use_tokenizer=self.tokenizer)
-            for column in self.column_name_map:
-                column_value = row[column]
-                if self.column_name_map[column].startswith("label") and column_value:
-                    if column_value != self.no_class_label:
-                        sentence.add_label(self.label_type, column_value)
-
-            if 0 < self.max_tokens_per_doc < len(sentence):
-                sentence.tokens = sentence.tokens[: self.max_tokens_per_doc]
+            sentence = self._make_labeled_data_point(row)
 
             return sentence
 

flair/datasets/sequence_labeling.py

@@ -3526,7 +3526,6 @@ def __init__(
             self,
             base_path: Union[str, Path] = None,
             in_memory: bool = True,
-            document_as_sequence: bool = False,
             **corpusargs,
     ):
         """
@@ -3571,7 +3570,7 @@ def __init__(
 
                     for row in posts: # Go through all the post titles
 
-                        txtout.writelines("-DOCSTART-\n") # Start each post with a -DOCSTART- token
+                        txtout.writelines("-DOCSTART-\n\n") # Start each post with a -DOCSTART- token
 
                         # Keep track of how many and which entity mentions does a given post title have
                         link_annots = [] # [start pos, end pos, wiki page title] of an entity mention
@@ -3643,7 +3642,7 @@ def __init__(
             train_file=corpus_file_name,
             column_delimiter="\t",
             in_memory=in_memory,
-            document_separator_token=None if not document_as_sequence else "-DOCSTART-",
+            document_separator_token="-DOCSTART-",
             **corpusargs,
         )