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.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+train.tsv filter=lfs diff=lfs merge=lfs -text

train.tsv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8eb7751dd929934eed45b4317870fcd6eafa0ec1d136544e2155e3337efde670
+size 28529403

vua20_dataset.py

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+import datasets
+import pandas as pd
+import os
+
+class Vua20(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIG_CLASS=datasets.BuilderConfig
+
+    def _info(self):
+
+        if self.config.name == 'default':
+            feature = {
+                'id':datasets.Value('int32'),
+                'sent_id':datasets.Value('string'),
+                'word_index': datasets.Value('int32'),
+                'tokens':datasets.Sequence(datasets.Value('string')),
+                'label':datasets.ClassLabel(num_classes=2, names=['Not Metaphor', 'Is Metaphor']),
+                'pos':datasets.Value('string')
+            }
+        elif self.config.name == 'combined':
+            feature = {
+                'sent_id':datasets.Value('string'),
+                'is_target': datasets.Sequence(datasets.ClassLabel(num_classes=2)),
+                'tokens':datasets.Sequence(datasets.Value('string')),
+                'labels': datasets.Sequence(datasets.ClassLabel(num_classes=2)),
+                'pos_tags':datasets.Sequence(datasets.Value('string'))
+            }
+
+        return datasets.DatasetInfo(
+            description='Vua metaphor detection datasets.',
+            features=datasets.Features(feature),
+            config_name=self.config.name
+        )
+    
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepath': dl_manager.download('train.tsv')}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'filepath': dl_manager.download('test.tsv')}),
+        ]
+    
+    def _generate_examples(self, filepath):
+        df = pd.read_csv(filepath, sep='\t')
+
+        if self.config.name == 'default':
+            for index, row in df.iterrows():
+                yield index, {
+                    'id': index,
+                    'sent_id': row['index'],
+                    'tokens': row['sentence'].split(),
+                    'word_index': row['w_index'],
+                    'label': row['label'],
+                    'pos': row['POS']
+                }
+        
+        elif self.config.name == 'combined':
+            for index, (sent_id, group) in enumerate(df.groupby('index')):
+                tokens = group.iloc[0]['sentence'].split()
+                is_target = [1 if i in group.w_index.values else 0 for i in range(len(tokens))]
+                labels = [1 if is_target[i] and group.label.iloc[sum(is_target[:i+1])-1] else 0 for i in range(len(tokens))]
+                pos_tags = [group.POS.iloc[sum(is_target[:i+1])-1] if is_target[i] else '_' for i in range(len(tokens))]
+
+                yield index, {
+                    'sent_id': sent_id,
+                    'tokens': tokens,
+                    'is_target': is_target,
+                    'labels': labels,
+                    'pos_tags': pos_tags
+                }