Upload aftdb.py

aftdb.py

@@ -43,14 +43,13 @@ class AFTConfig(datasets.BuilderConfig):
     """Builder Config for AFTdb"""
  
     def __init__(self, nb_files_figure, nb_files_table, **kwargs):
-        """BuilderConfig for AFTdb."""
         super().__init__(version=datasets.__version__, **kwargs)
         self.nb_files_figure = nb_files_figure
         self.nb_files_table = nb_files_table
 
 
 class AFT_Dataset(datasets.GeneratorBasedBuilder):
-    """Arxiv Figure Table (AFT) dataset"""
+    """Arxiv Figure Table database (AFTdb)"""
 
     BUILDER_CONFIGS = [
         AFTConfig(
@@ -59,7 +58,7 @@ class AFT_Dataset(datasets.GeneratorBasedBuilder):
                 "Dataset containing scientific article figures associated "
                 "with their caption, summary, and article title."
             ),
-            data_dir="./{type}",
+            data_dir="./data/arxiv_dataset/{type}", # A modiféer sur Huggingface Hub
             nb_files_figure=_NB_TAR_FIGURE,
             nb_files_table=None
         ),
@@ -71,7 +70,7 @@ class AFT_Dataset(datasets.GeneratorBasedBuilder):
                 "representation of the table, including its caption, summary, "
                 "and article title."
             ),
-            data_dir="./{type}",
+            data_dir="./data/arxiv_dataset/{type}", # A modiféer sur Huggingface Hub
             nb_files_figure=None,
             nb_files_table=_NB_TAR_TABLE
         ),
@@ -83,7 +82,7 @@ class AFT_Dataset(datasets.GeneratorBasedBuilder):
                 "textual representation of the table, including its caption, "
                 "summary, and article title."
             ),
-            data_dir="./{type}",
+            data_dir="./data/arxiv_dataset/{type}", # A modiféer sur Huggingface Hub
             nb_files_figure=_NB_TAR_FIGURE,
             nb_files_table=_NB_TAR_TABLE
         )
@@ -136,32 +135,57 @@ class AFT_Dataset(datasets.GeneratorBasedBuilder):
                 data_dir=self.config.data_dir.format(type='table'),
                 nb_files=self.config.nb_files_table
             )
+        dataset = []
         if dl_manager.is_streaming:
             downloaded_files = dl_manager.download(all_path)
-            downloaded_files['train'] = [
-                dl_manager.iter_archive(ii) for ii in downloaded_files['train']
-            ]
-            downloaded_files['test'] = [
-                dl_manager.iter_archive(ii) for ii in downloaded_files['test']
-            ]
+            dataset.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        'filepaths': [
+                            dl_manager.iter_archive(ii)
+                            for ii in downloaded_files['train']
+                        ],
+                        'is_streaming': dl_manager.is_streaming
+                    }
+                )
+            )
+            dataset.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepaths": [
+                            dl_manager.iter_archive(ii)
+                            for ii in downloaded_files['test']
+                        ],
+                        'is_streaming': dl_manager.is_streaming
+                    }
+                )
+            )
         else:
-            downloaded_files = dl_manager.download_and_extract(all_path)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    'filepaths': downloaded_files['train'],
-                    'is_streaming': dl_manager.is_streaming
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepaths": downloaded_files['test'],
-                    'is_streaming': dl_manager.is_streaming
-                }
+            dataset.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        'filepaths': dl_manager.download_and_extract(
+                            all_path['train']
+                        ),
+                        'is_streaming': dl_manager.is_streaming
+                    }
+                )
+            )
+            dataset.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepaths": dl_manager.download_and_extract(
+                            all_path['test']
+                        ),
+                        'is_streaming': dl_manager.is_streaming
+                    }
+                )
             )
-        ]
+        return dataset
 
     def _generate_examples(self, filepaths, is_streaming):
         if is_streaming: