Upload emotions_dataset.py

emotions_dataset.py

@@ -4,10 +4,14 @@ import datasets
 import pandas as pd
 from datasets import ClassLabel, Value
 
-_URLS = [
-    "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_1.csv",
-    "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_2.csv",
-    "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_3.csv",
+DATASETS_URLS = [{
+    "name": "go_emotions",
+    "urls": [
+        "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_1.csv",
+        "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_2.csv",
+        "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_3.csv",
+    ],
+    "license": "apache license 2.0"}
 ]
 
 _CLASS_NAMES = [
@@ -57,7 +61,7 @@ class EmotionsDataset(datasets.GeneratorBasedBuilder):
         EmotionsDatasetConfig(
             name="all",
             label_classes=_CLASS_NAMES,
-            features=["text", "label", "dataset"]
+            features=["text", "label", "dataset", "license"]
         )
     ]
 
@@ -68,18 +72,24 @@ class EmotionsDataset(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "id": datasets.Value("string"),
-                    'label': ClassLabel(names=_CLASS_NAMES, id=None),
                     'text': Value(dtype='string', id=None),
-                    'dataset': Value(dtype='string', id=None)
+                    'label': ClassLabel(names=_CLASS_NAMES, id=None),
+                    'dataset': Value(dtype='string', id=None),
+                    'license': Value(dtype='string', id=None)
                 }
             )
         )
 
     def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files})]
+        splits = []
+        for d in DATASETS_URLS:
+            downloaded_files = dl_manager.download_and_extract(d.get("urls"))
+            splits.append(datasets.SplitGenerator(name=d.get("name"), gen_kwargs={"filepaths": downloaded_files,
+                                                                                  "dataset": d.get("name"),
+                                                                                  "license": d.get("license")}))
+        return splits
 
-    def _generate_examples(self, filepaths):
+    def _generate_examples(self, filepaths, dataset, license):
         for i, filepath in enumerate(filepaths):
             df = pd.read_csv(filepath)
             current_classes = list(set(df.columns).intersection(set(_CLASS_NAMES)))
@@ -89,4 +99,6 @@ class EmotionsDataset(datasets.GeneratorBasedBuilder):
                 uid = f"{i}_{row_idx}"
                 yield uid, {"text": row["text"],
                             "id": uid,
+                            "dataset": dataset,
+                            "license": license,
                             "label": row[current_classes][row == 1].index.item()}