loading script created

123_test.py

@@ -45,9 +45,9 @@ _LICENSE = "Apache 2.0"
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 
 _URLS = {
-    "data_0": "https://huggingface.co/datasets/JeremyAlain/123_test/raw/main/data/0/file_0.jsonl",
-    "data_1": "https://huggingface.co/datasets/JeremyAlain/123_test/raw/main/data/1/file_0.jsonl",
-    "data_2": "https://huggingface.co/datasets/JeremyAlain/123_test/raw/main/data/2/file_0.jsonl",
+    "data_0": ["https://huggingface.co/datasets/JeremyAlain/123_test/raw/main/data/0/file_{}.jsonl".format(i) for i in range(20)],
+    "data_1": ["https://huggingface.co/datasets/JeremyAlain/123_test/raw/main/data/1/file_{}.jsonl".format(i) for i in range(20)],
+    "data_2": ["https://huggingface.co/datasets/JeremyAlain/123_test/raw/main/data/2/file_{}.jsonl".format(i) for i in range(20)],
 
 }
 logger = datasets.logging.get_logger(__name__)
@@ -113,34 +113,34 @@ class FewshotPretraining(datasets.GeneratorBasedBuilder):
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
         urls = _URLS[self.config.name]
 
-        data_dir = dl_manager.download_and_extract(urls)
+        extracted_paths = dl_manager.download_and_extract(urls)
         return datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "folder_path": data_dir,
+                    "file_paths": extracted_paths,
                     "split": "train",
                 },
             )
 
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, folder_path, split):
+    def _generate_examples(self, file_path, split):
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        for filepath in os.listdir(folder_path):
-            with open(filepath, encoding="utf-8") as f:
-                data = pd.read_json(filepath, orient="records", lines=True)
-                for i in range(data.shape[0]):
-                    row = data.iloc[i]
-                    # Yields examples as (key, example) tuples
-                    key = row["task"] + "_i"
-                    yield key, {
-                        "task": data["task"],
-                        "input": data["input"],
-                        "output": data["output"],
-                        "options": data["options"],
-                        "pageTitle": data["pageTitle"],
-                        "outputColName": data["outputColName"],
-                        "url": data["url"],
-                        "wdcFile": data["wdcFile"],
-                    }
+        print("generating {}".format(file_path))
+        with open(file_path, encoding="utf-8") as f:
+            data = pd.read_json(file_path, orient="records", lines=True)
+            for i in range(data.shape[0]):
+                row = data.iloc[i]
+                # Yields examples as (key, example) tuples
+                key = row["task"] + "_i"
+                yield key, {
+                    "task": data["task"],
+                    "input": data["input"],
+                    "output": data["output"],
+                    "options": data["options"],
+                    "pageTitle": data["pageTitle"],
+                    "outputColName": data["outputColName"],
+                    "url": data["url"],
+                    "wdcFile": data["wdcFile"],
+                }