Upload mbib.py

mbib.py

@@ -0,0 +1,46 @@
+import datasets
+import pandas as pd
+
+
+_MBIB_DESCRIPTION = 'bla bla'
+
+class MBIBConfig(datasets.BuilderConfig):
+    def __init__(self,data_dir,**kwargs):
+        super(MBIBConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.data_dir = data_dir
+
+
+class MBIB(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        MBIBConfig(name="cognitive-bias",data_dir="mbib-aggregated/cognitive-bias.csv"),
+        MBIBConfig(name="fake-news",data_dir="mbib-aggregated/fake-news.csv"),
+        MBIBConfig(name="gender-bias",data_dir="mbib-aggregated/gender-bias.csv"),
+        MBIBConfig(name="hate-speech",data_dir="mbib-aggregated/hate-speech.csv"),
+        MBIBConfig(name="liguistic-bias",data_dir="mbib-aggregated/linguistic-bias.csv"),
+        MBIBConfig(name="political-bias",data_dir="mbib-aggregated/political-bias.csv"),
+        MBIBConfig(name="racial-bias",data_dir="mbib-aggregated/racial-bias.csv"),
+        MBIBConfig(name="text-level-bias",data_dir="mbib-aggregated/text-level-bias.csv")]
+    
+    def _info(self):
+        features=datasets.Features(
+            {"text":datasets.Value("string"),
+            "id":datasets.Value("string"),
+            "label":datasets.Value("string"),
+            "dataset_id":datasets.Value("string")}
+        )
+
+        return datasets.DatasetInfo(
+            description=_MBIB_DESCRIPTION,
+            features=features
+        )
+
+    def _split_generators(self, dl_manager):
+        data_file = dl_manager.download(self.config.data_dir)
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN,gen_kwargs={"data_file": data_file})]
+
+
+    def _generate_examples(self, data_dir):
+       df = pd.read_csv(data_dir)
+        
+       for i, row in df.iterrows():
+                yield i, {"text":row['text'],"label":row['label'],"id":row['id']}