Update E3C.py

E3C.py

@@ -41,26 +41,45 @@ class E3C(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name=f"{lang}", version="1.0.0", description=f"The {lang} subset of the E3C corpus") for lang in _LANGUAGES
     ]
+    
+    DEFAULT_CONFIG_NAME = "clinical"
 
     def _info(self):
+        
+        if self.config.name == "default":
+            self.config.name = self.DEFAULT_CONFIG_NAME
 
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "ner_clinical_tags": datasets.Sequence(
-                    datasets.features.ClassLabel(
-                        names=["O","B-CLINENTITY","I-CLINENTITY"],
+        if self.config.name == "clinical":
+            
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_clinical_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=["O","B-CLINENTITY","I-CLINENTITY"],
+                        ),
                     ),
-                ),
-                "ner_temporal_tags": datasets.Sequence(
-                    datasets.features.ClassLabel(
-                        names=["O", "B-EVENT", "B-ACTOR", "B-BODYPART", "B-TIMEX3", "B-RML", "I-EVENT", "I-ACTOR", "I-BODYPART", "I-TIMEX3", "I-RML"],
+                }
+            )
+        
+        elif self.config.name == "temporal":
+            
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_temporal_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=["O", "B-EVENT", "B-ACTOR", "B-BODYPART", "B-TIMEX3", "B-RML", "I-EVENT", "I-ACTOR", "I-BODYPART", "I-TIMEX3", "I-RML"],
+                        ),
                     ),
-                ),
-            }
-        )
+                }
+            )
+
+
 
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -73,29 +92,57 @@ class E3C(datasets.GeneratorBasedBuilder):
 
         data_dir = dl_manager.download_and_extract(_URL)
 
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name, "layer1"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name, "layer1"),
-                    "split": "validation",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_validation", self.config.name, "layer2"),
-                    "split": "test",
-                },
-            ),
-        ]
+        if self.config.name == "clinical":
+            
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name, "layer2"),
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name, "layer2"),
+                        "split": "validation",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name, "layer1"),
+                        "split": "test",
+                    },
+                ),
+            ]
+            
+        elif self.config.name == "temporal":
+            
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name, "layer1"),
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name, "layer1"),
+                        "split": "validation",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name, "layer1"),
+                        "split": "test",
+                    },
+                ),
+            ]
 
     @staticmethod
     def get_annotations(entities: ResultSet, text: str) -> list:
@@ -204,7 +251,33 @@ class E3C(datasets.GeneratorBasedBuilder):
 
                 key += 1
         
-        if split != "test":
+        if self.config.name == "clinical":
+            
+            if split != "test":
+                
+                ids = [r["id"] for r in all_res]
+        
+                random.seed(4)
+                random.shuffle(ids)
+                random.shuffle(ids)
+                random.shuffle(ids)
+                
+                train, validation = np.split(ids, [int(len(ids)*0.8738)])
+        
+                if split == "train":
+                    allowed_ids = list(train)
+                elif split == "validation":
+                    allowed_ids = list(validation)
+                
+                for r in all_res:
+                    if r["id"] in allowed_ids:
+                        yield r["id"], r
+            else:
+                
+                for r in all_res:
+                    yield r["id"], r
+        
+        elif self.config.name == "temporal":
             
             ids = [r["id"] for r in all_res]
     
@@ -223,7 +296,3 @@ class E3C(datasets.GeneratorBasedBuilder):
             for r in all_res:
                 if r["id"] in allowed_ids:
                     yield r["id"], r
-        else:
-            
-            for r in all_res:
-                yield r["id"], r