Update winogavil.py

winogavil.py

@@ -13,10 +13,9 @@
 # limitations under the License.
 """ WinoGAViL Loading Script """
 
-
 import json
 import os
-import pandas as pd 
+import pandas as pd
 import datasets
 from huggingface_hub import hf_hub_url
 
@@ -40,6 +39,7 @@ _LICENSE = "https://creativecommons.org/licenses/by/4.0/"
 
 _URL = "https://huggingface.co/datasets/nlphuji/winogavil/blob/main"
 
+
 class Winogavil(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.1.0")
 
@@ -57,12 +57,12 @@ class Winogavil(datasets.GeneratorBasedBuilder):
     def _info(self):
         features = datasets.Features(
             {
+                "candidates": [datasets.Value("string")],
+                # "candidates_images": [datasets.Value("string")], 
+                "candidates_images": [datasets.Image()],
                 "cue": datasets.Value("string"),
-                "candidate_images": [datasets.Image()],
-                "association_images": [datasets.Image()],
-                'score_fool_the_ai': datasets.Value("float64"),
                 "associations": [datasets.Value("string")],
-                "candidates": [datasets.Value("string")],
+                'score_fool_the_ai': datasets.Value("float64"),
                 'num_associations': datasets.Value("int64"),
                 'num_candidates': datasets.Value("int64"),
                 'solvers_jaccard_mean': datasets.Value("float64"),
@@ -93,10 +93,10 @@ class Winogavil(datasets.GeneratorBasedBuilder):
         # 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
         data_dir = dl_manager.download_and_extract({
-            "examples_csv": hf_hub_url(repo_id="nlphuji/winogavil", repo_type='dataset', filename="winogavil_dataset.csv"),
-            "images_dir": hf_hub_url(repo_id="nlphuji/winogavil", repo_type='dataset', filename="winogavil_images.zip")
-            })
-            
+            "examples_csv": hf_hub_url("datasets/nlphuji/winogavil", filename="winogavil_dataset.csv"),
+            "images_dir": hf_hub_url("datasets/nlphuji/winogavil", filename="winogavil_images.zip")
+        })
+
         return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
@@ -108,14 +108,13 @@ class Winogavil(datasets.GeneratorBasedBuilder):
         # columns_to_serialize = ['candidates', 'associations']
         # for c in columns_to_serialize:
         #   df[c] = df[c].apply(json.loads)
-    
+
         for r_idx, r in df.iterrows():
             r_dict = r.to_dict()
             r_dict['candidates'] = json.loads(r_dict['candidates'])
-            candidates_images = [os.path.join(images_dir, "winogavil_images", f"{x}.{self.IMAGE_EXTENSION}") for x in r_dict['candidates']]
-            r_dict['candidate_images'] = candidates_images
+            candidates_images = [os.path.join(images_dir, "winogavil_images", f"{x}.{self.IMAGE_EXTENSION}") for x in
+                                 r_dict['candidates']]
+            r_dict['candidates_images'] = candidates_images
             r_dict['associations'] = json.loads(r_dict['associations'])
-            association_images = [os.path.join(images_dir, "winogavil_images", f"{x}.{self.IMAGE_EXTENSION}") for x in r_dict['associations']]
-            r_dict['association_images'] = association_images
             key = r['ID']
-            yield key, r_dict
+            yield key, r_dict