host data on the hub

data/GAD.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f47dbe6fb46c853516cf68d934b7a4cf975d9b68fa0c6ca67ff860b6d9245542
+size 2811223

gad.py

@@ -42,11 +42,6 @@ _HOMEPAGE = "https://github.com/dmis-lab/biobert"  # This data source is used by
 
 _LICENSE = "CC_BY_4p0"
 
-_URLs = {
-    "source": "https://drive.google.com/uc?export=download&id=1-jDKGcXREb2X9xTFnuiJ36PvsqoyHWcw",
-    "bigbio_text": "https://drive.google.com/uc?export=download&id=1-jDKGcXREb2X9xTFnuiJ36PvsqoyHWcw",
-}
-
 _SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
 
 _SOURCE_VERSION = "1.0.0"
@@ -115,31 +110,53 @@ class GAD(datasets.GeneratorBasedBuilder):
         self, dl_manager: datasets.DownloadManager
     ) -> List[datasets.SplitGenerator]:
 
+        data_dir = Path(dl_manager.download_and_extract("data/GAD.zip"))
+
         if "blurb" in self.config.name:
-            return self._blurb_split_generator(dl_manager)
-
-        fold_id = int(self.config.subset_id.split("_fold")[1][0]) + 1
-
-        my_urls = _URLs[self.config.schema]
-        data_dir = Path(dl_manager.download_and_extract(my_urls))
-        data_files = {
-            "train": data_dir / "GAD" / str(fold_id) / "train.tsv",
-            "test": data_dir / "GAD" / str(fold_id) / "test.tsv",
-        }
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": data_files["train"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": data_files["test"]},
-            ),
-        ]
+            data_files = {
+                "train": data_dir / "GAD" / "blurb" / "train.tsv",
+                "validation": data_dir / "GAD" / "blurb" / "dev.tsv",
+                "test": data_dir / "GAD" / "blurb" / "test.tsv",
+            }
+
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": data_files["train"]},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"filepath": data_files["validation"]},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": data_files["test"]},
+                ),
+            ]
+
+        else:
+
+            fold_id = int(self.config.subset_id.split("_fold")[1][0]) + 1
+
+            data_files = {
+                "train": data_dir / "GAD" / str(fold_id) / "train.tsv",
+                "test": data_dir / "GAD" / str(fold_id) / "test.tsv",
+            }
+
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": data_files["train"]},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": data_files["test"]},
+                ),
+            ]
 
     def _generate_examples(self, filepath: Path):
-        if "train.tsv" in str(filepath):
+        # train files in non-blurb splits don't have headers for some reason
+        if "train.tsv" in str(filepath) and "blurb" not in self.config.name:
             df = pd.read_csv(filepath, sep="\t", header=None).reset_index()
         else:
             df = pd.read_csv(filepath, sep="\t")
@@ -164,48 +181,3 @@ class GAD(datasets.GeneratorBasedBuilder):
                 yield id, ex
         else:
             raise ValueError(f"Invalid config: {self.config.name}")
-
-    def _blurb_split_generator(self, dl_manager: datasets.DownloadManager):
-        """Creates train/dev/test for BLURB split"""
-
-        my_urls = _URLs[self.config.schema]
-        data_dir = Path(dl_manager.download_and_extract(my_urls))
-        data_files = {
-            "train": data_dir / "GAD" / str(1) / "train.tsv",
-            "test": data_dir / "GAD" / str(1) / "test.tsv",
-        }
-
-        root_path = data_files["train"].parents[1]
-        # Save the train + validation sets accordingly
-        with open(data_files["train"], "r") as f:
-            train_data = f.readlines()
-
-        data = {}
-        data["train"], data["dev"] = train_data[:4261], train_data[4261:]
-
-        for batch in ["train", "dev"]:
-            fname = batch + "_blurb.tsv"
-            fname = root_path / fname
-
-            with open(fname, "w") as f:
-                f.write("index\tsentence\tlabel\n")
-                for idx, line in enumerate(data[batch]):
-                    f.write(f"{idx}\t{line}")
-
-        train_fpath = root_path / "train_blurb.tsv"
-        dev_fpath = root_path / "dev_blurb.tsv"
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": train_fpath},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": dev_fpath},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": data_files["test"]},
-            ),
-        ]