rename

invalsi.py → Invalsi.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
 
 
 import csv
@@ -22,8 +21,6 @@ import os
 import datasets
 
 
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
 @misc{esuli2024invalsi,
     title={The Invalsi Benchmark: measuring Language Models Mathematical and Language understanding in Italian}, 
@@ -35,54 +32,34 @@ _CITATION = """\
 }
 """
 
-# TODO: Add description of the dataset here
-# You can copy an official description
 _DESCRIPTION = """\
 This new dataset is designed to measure Language Models mathematical and language understanding in Italian.
 """
 
-# TODO: Add a link to an official homepage for the dataset here
 _HOMEPAGE = ""
 
-# TODO: Add the licence for the dataset here if you can find it
 _LICENSE = "CC BY 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)
+
 _URLS = {
-    "mate": "https://huggingface.co/datasets/ai4text/Invalsi/tree/main/invalsi_mate_data",
+    "mate": "https:/invalsi_mate_data//huggingface.co/datasets/ai4text/Invalsi/tree/main/invalsi_mate_data",
     "ita": "https://huggingface.co/datasets/ai4text/Invalsi/tree/main/invalsi_ita_data",
 }
 
 
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class Invalsi(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    VERSION = datasets.Version("0.1")
+class invalsi(datasets.GeneratorBasedBuilder):
 
-    # 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.
+    VERSION = datasets.Version("0.1.0")
 
-    # 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
-
-    # 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="mate", version=VERSION, description="Mathematical Understanding"),
         datasets.BuilderConfig(name="ita", version=VERSION, description="Italian Understanding"),
     ]
 
-    # DEFAULT_CONFIG_NAME = "mate"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "mate"
 
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "mate":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        if self.config.name == "mate":
             features = datasets.Features(
                 {
                     "domanda": datasets.Value("string"),
@@ -91,7 +68,7 @@ class Invalsi(datasets.GeneratorBasedBuilder):
                     "test_id": datasets.Value("string"),
                 }
             )
-        elif self.config.name == "ita":  # This is an example to show how to have different features for "first_domain" and "second_domain"
+        elif self.config.name == "ita":
             features = datasets.Features(
                 {
                     "testo": datasets.Value("string"),
@@ -101,19 +78,12 @@ class Invalsi(datasets.GeneratorBasedBuilder):
                     "test_id": datasets.Value("string"),
                 }
             )
+
         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,
         )
 
@@ -125,20 +95,13 @@ class Invalsi(datasets.GeneratorBasedBuilder):
         # 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)
+        # data_dir = dl_manager.download_and_extract(urls)
+        data_dir = "."
         if self.config.name == "mate":
-            data_file = "invalsi_mate_clean.csv"
+            data_file = "Invalsi/invalsi_mate_data/invalsi_mate_clean.csv"
         elif self.config.name == "ita":
-            data_file = "invalsi_ita_clean.csv"
+            data_file = "Invalsi/invalsi_ita_data/invalsi_ita_clean.csv"
         return [
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.TRAIN,
-            #     # These kwargs will be passed to _generate_examples
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "train.jsonl"),
-            #         "split": "train",
-            #     },
-            # ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
@@ -147,36 +110,38 @@ class Invalsi(datasets.GeneratorBasedBuilder):
                     "split": "val",
                 },
             ),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.TEST,
-            #     # These kwargs will be passed to _generate_examples
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "test.jsonl"),
-            #         "split": "test"
-            #     },
-            # ),
         ]
 
-    # 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.
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "mate":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "domanda": datasets.Value("string"),
-                        "risposta": datasets.Value("string"),
-                        "immagine": datasets.Value("string"),
-                        "test_id": datasets.Value("string"),
-                    }
-                elif self.config.name == "ita":
-                    yield key, {
-                        "testo": datasets.Value("string"),
-                        "domanda": datasets.Value("string"),
-                        "risposta": datasets.Value("string"),
-                        "immagine": datasets.Value("string"),
-                        "test_id": datasets.Value("string"),
-                    }
+        ds = datasets.load_dataset("csv", data_files=filepath)["train"]
+        for key, row in enumerate(ds):
+            # data = json.loads(row)
+            if self.config.name == "mate":
+                # Yields examples as (key, example) tuples
+                out = {
+                    # "domanda": datasets.Value("string"),
+                    # "risposta": datasets.Value("string"),
+                    # "immagine": datasets.Value("string"),
+                    # "test_id": datasets.Value("string"),
+                    "domanda": row["domanda"],
+                    "risposta": row["risposta"],
+
+                    "test_id": row["test_id"],
+                }
+                if "image_file_names" in row:
+                    out["immagine"] = row["image_file_names"]
+
+                yield key, out
+            elif self.config.name == "ita":
+                yield key, {
+                    # "testo": datasets.Value("string"),
+                    # "domanda": datasets.Value("string"),
+                    # "risposta": datasets.Value("string"),
+                    # "immagine": datasets.Value("string"),
+                    # "test_id": datasets.Value("string"),
+                    "testo": row["testo"],
+                    "domanda": row["domanda"],
+                    "risposta": row["risposta"],
+                    "immagine": row["image_file_names"],
+                    "test_id": row["test_id"],
+                }