Update WikiDO.py

WikiDO.py

@@ -11,8 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
 
 
 import csv
@@ -33,30 +31,30 @@ year={2020}
 }
 """
 
-# TODO: Add description of the dataset here
 # You can copy an official description
 _DESCRIPTION = """\
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+To address this gap, we introduce WikiDO (drawn from Wikipedia Ddiversity Observatory), a new cross-modal retrieval benchmark to assess the OOD generalization capabilities of pretrained VLMs. 
+This consists of 380K image-text pairs from Wikipedia with domain labels, along with carefully curated, human-verified in-distribution (ID) and OOD test sets of size 3K each. 
+The image-text pairs are very diverse in topics.
 """
 
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
+# Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://neurips-wikido.github.io/WIKIDO/"
 
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
+# Add the licence for the dataset here if you can find it
+_LICENSE = "CC-by-SA-4.0"
 
 # TODO: Add link to the official dataset URLs here
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
-    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
-    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+    "images":"./images.zip",
 }
 
 
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class NewDataset(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
+# Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class WikiDO(datasets.GeneratorBasedBuilder):
+    """ Short description of my dataset."""
 
     VERSION = datasets.Version("1.1.0")
 
@@ -71,36 +69,26 @@ class NewDataset(datasets.GeneratorBasedBuilder):
     # You will be able to load one or the other configurations in the following list with
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
-        datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
-    ]
 
-    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option2": datasets.Value("string"),
-                    "second_domain_answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+            {
+                "image": datasets.Value("string"),
+                "orig_cap": datasets.Value("string"),
+                "caption": datasets.Value("string"),
+                "topic": datasets.Value("string"),
+                "page_id": datasets.Value("string"),
+                "page_title": datasets.Value("string")
                 }
             )
+
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
+            
             # This defines the different columns of the dataset and their types
             features=features,  # Here we define them above because they are different between the two configurations
             # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
@@ -121,8 +109,8 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(urls)
+        
+        data_dir = dl_manager.download_and_extract(_URLS)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -150,23 +138,73 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             ),
         ]
 
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+
+    def _split_generators(self, dl_manager):
+        # Define the local paths to the dataset files
+        data_dir = os.path.dirname(__file__)
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train_100k.jsonl"),
+                    "split": "train_100k",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train_200k.jsonl"),
+                    "split": "train_200k",
+                },
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TRAIN,
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "train_full.jsonl"),
+            #         "split": "train_full",
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "val_full.jsonl"),
+            #         "split": "val_full",
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "val_verified.jsonl"),
+            #         "split": "val_verified(1k)",
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "id_test_verified.jsonl"),
+            #         "split": "id_test_verified(3k)",
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "ood_test_full.jsonl"),
+            #         "split": "ood_test_full",
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "ood_test_verified.jsonl"),
+            #         "split": "ood_test_verified(3k)",
+            #     },
+            # ),
+        ]
+
     def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        # Open the file and read the data
         with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "first_domain":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option1": data["option1"],
-                        "answer": "" if split == "test" else data["answer"],
-                    }
-                else:
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option2": data["option2"],
-                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
-                    }
+            for id_, line in enumerate(f):
+                data = json.loads(line)
+                yield id_, data