more split stuff

rumoureval_2019.py

@@ -45,95 +45,57 @@ _HOMEPAGE = ""
 # TODO: Add the licence for the dataset here if you can find it
 _LICENSE = ""
 
-# 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)
-_URLS = {
-    "train": "rumoureval2019_train.csv",
-    "validation": "NA",
-}
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
 class RumourEval2019(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+    VERSION = datasets.Version("0.9.0")
 
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="train", version=VERSION, description="Training"),
-        datasets.BuilderConfig(name="validation", version=VERSION, description="Validation")
+        datasets.BuilderConfig(name="train", version=VERSION, description="Training data for RumourEval 2019"),
+        datasets.BuilderConfig(name="validation", version=VERSION, description="Validation data for RumourEval 2019"),
+        datasets.BuilderConfig(name="test", version=VERSION, description="Testing data for RumourEval 2019")
     ]
-
-    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
-        if self.config.name == "train":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "source_text": datasets.Value("string"),
-                    "reply_text": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(
-                        names=[
-                            "support",
-                            "query",
-                            "deny",
-                            "comment"
-                        ]
-                    )
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "source_text": datasets.Value("string"),
+                "reply_text": datasets.Value("string"),
+                "label": datasets.features.ClassLabel(
+                    names=[
+                        "support",
+                        "query",
+                        "deny",
+                        "comment"
+                    ]
+                )
+            }
+        )
 
         return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
+            features=features,
             homepage=_HOMEPAGE,
-            # License for the dataset if available
             license=_LICENSE,
-            # Citation for the dataset
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # 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]
         train_text = dl_manager.download_and_extract("rumoureval2019_train.csv")
+        validation_text = dl_manager.download_and_extract("rumoureval2019_val.csv")
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_text, "split": "train"})
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_text, "split": "train"}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_text, "split": "validation"})
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
         # 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.
-        if split == "train":
+        if split == "train" or split == "validation":
             with open(filepath, encoding="utf-8") as f:
-                reader = csv.DictReader(f, delimiter=",", quotechar='"')
+                reader = csv.DictReader(f, delimiter=",")
                 guid = 0
                 for instance in reader:
                     instance["source_text"] = instance.pop("source_text")