some changes

CoarseWSD-20.py

@@ -2,7 +2,7 @@
 # @title cwsd20 code
 import json
 import datasets
-
+import os
 
 _CITATION = """\
 @misc{loureiro2021analysis,
@@ -41,47 +41,57 @@ class CWSD20(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [datasets.BuilderConfig(name=word, description=_DESCRIPTION) for word in _WORDS]
 
     def _info(self):
-        # Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "idx": datasets.Value("int32"),
-                    "sentence": datasets.Value("string"),
-                    # datasets.features.ClassLabel(names=self.config.label_classes)
-                    # "label":  datasets.Value("int32")
-                    # "idx": datasets.Value("int32"),
-                    # "word": datasets.Value("string"),
-                    # "start1": datasets.Value("int32"),
-                    # "start2": datasets.Value("int32"),
-                    # "end1": datasets.Value("int32"),
-                    # "end2": datasets.Value("int32"),
-                    # "label": datasets.Value("int32")
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://github.com/google-research-datasets/boolean-questions",
-            citation=_CITATION,
-        )
+      with open(os.path.join("data", self.config.name, "classes_map.txt")) as cmap_f:
+        cmap = json.load(cmap_f)
+      label_classes = [cmap[str(k)] for k in range(len(cmap))]
+      
+      # Specifies the datasets.DatasetInfo object
+      return datasets.DatasetInfo(
+          # This is the description that will appear on the datasets page.
+          description=_DESCRIPTION,
+          # datasets.features.FeatureConnectors
+          features=datasets.Features(
+              {
+                  "idx": datasets.Value("int32"),
+                  "sentence": datasets.Value("string"),
+                  "label": datasets.features.ClassLabel(names=label_classes)
+                  # datasets.features.ClassLabel(names=self.config.label_classes)
+                  # "label":  datasets.Value("int32")
+                  # "idx": datasets.Value("int32"),
+                  # "word": datasets.Value("string"),
+                  # "start1": datasets.Value("int32"),
+                  # "start2": datasets.Value("int32"),
+                  # "end1": datasets.Value("int32"),
+                  # "end2": datasets.Value("int32"),
+                  # "label": datasets.Value("int32")
+              }
+          ),
+          # If there's a common (input, target) tuple from the features,
+          # specify them here. They'll be used if as_supervised=True in
+          # builder.as_dataset.
+          supervised_keys=None,
+          # Homepage of the dataset for documentation
+          homepage="https://github.com/google-research-datasets/boolean-questions",
+          citation=_CITATION,
+      )
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # TODO(WiCTSV): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        # urls_to_download = _URLS
-        dl = path
+        train_ex_path = os.path.join("data", self.config.name,
+                                     "train.data.txt")
+        test_ex_path = os.path.join("data", self.config.name,
+                                     "test.data.txt")
+        train_lb_path = os.path.join("data", self.config.name,
+                                     "train.gold.txt")
+        test_lb_path = os.path.join("data", self.config.name,
+                                     "test.gold.txt")
 
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN, 
-                gen_kwargs={"ex": dl})]
+                gen_kwargs={"ex": train_ex_path, "lb":train_lb_path }),
+                datasets.SplitGenerator(name=datasets.Split.TEST, 
+                gen_kwargs={"ex": test_ex_path, "lb":test_lb_path })]
 
-    def _generate_examples(self, ex):
+    def _generate_examples(self, ex, lb):
       """Yields examples."""
       with open(ex, encoding="utf-8") as exf:
           for id_, exi in enumerate(exf):