Upload liv4ever.py

liv4ever.py

@@ -66,10 +66,11 @@ _LICENSE = "CC BY-NC-SA 4.0"
 # TODO: Add link to the official dataset URLs here
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "https://huggingface.co/datasets/tartuNLP/liv4ever/raw/main/"
 _URLS = {
-    "train": "https://huggingface.co/datasets/tartuNLP/liv4ever/raw/main/train.json",
-    "dev": "https://huggingface.co/datasets/tartuNLP/liv4ever/raw/main/dev.json",
-    "test": "https://huggingface.co/datasets/tartuNLP/liv4ever/raw/main/test.json",
+    "train": _URL + "train.json",
+    "dev": _URL + "dev.json",
+    "test": _URL + "test.json",
 }
 
 
@@ -90,12 +91,12 @@ class liv4ever(datasets.GeneratorBasedBuilder):
     # You will be able to load one or the other configurations in the following list with
     # data = datasets.load_dataset('my_dataset', 'train')
     # data = datasets.load_dataset('my_dataset', 'dev')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="train", version=VERSION, description="This part of my dataset covers a first domain"),
-        datasets.BuilderConfig(name="dev", version=VERSION, description="This part of my dataset covers a second domain"),
-    ]
+    # BUILDER_CONFIGS = [
+        # datasets.BuilderConfig(name="train", version=VERSION, description="This part of my dataset covers a first domain"),
+        # datasets.BuilderConfig(name="dev", version=VERSION, description="This part of my dataset covers a second domain"),
+    # ]
 
-    DEFAULT_CONFIG_NAME = "train"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    # DEFAULT_CONFIG_NAME = "train"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
         # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
@@ -130,33 +131,41 @@ class liv4ever(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # 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)
+        # urls = _URLS[self.config.name]
+        # data_dir = dl_manager.download_and_extract(urls)
+        # return [
+            # datasets.SplitGenerator(
+                # name=datasets.Split.TRAIN,
+                # # These kwargs will be passed to _generate_examples
+                # gen_kwargs={
+                    # "filepath": os.path.join(data_dir),
+                    # "split": "train",
+                # },
+            # ),
+            # datasets.SplitGenerator(
+                # name=datasets.Split.TEST,
+                # # These kwargs will be passed to _generate_examples
+                # gen_kwargs={
+                    # "filepath": os.path.join(data_dir),
+                    # "split": "test"
+                # },
+            # ),
+            # datasets.SplitGenerator(
+                # name=datasets.Split.VALIDATION,
+                # # These kwargs will be passed to _generate_examples
+                # gen_kwargs={
+                    # "filepath": os.path.join(data_dir),
+                    # "split": "dev",
+                # },
+            # ),
+        # ]
+        urls_to_download = self._URLS
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir),
-                    "split": "test"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir),
-                    "split": "dev",
-                },
-            ),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
@@ -164,11 +173,12 @@ class liv4ever(datasets.GeneratorBasedBuilder):
         # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
+            jsondata = json.load(f)
+        
+            for sentence in jsondata:
                 # Yields examples as (key, example) tuples
                 yield key, {
-                    "source": data["source"],
-                    "en": data["en"],
-                    "liv": data["liv"],
+                    "source": sentence["source"],
+                    "en": sentence["en"],
+                    "liv": sentence["liv"],
                 }