Dataset that yield squad_v2 compatible data

frenchQA.py

@@ -0,0 +1,115 @@
+"""FrenchQA: One French QA Dataset to rule them all"""
+
+
+import csv
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+# TODO(squad_v2): BibTeX citation
+_CITATION = """\
+"""
+
+_DESCRIPTION = """\
+One French QA Dataset to rule them all, One French QA Dataset to find them, One French QA Dataset to bring them all, and in the darkness bind them.
+"""
+
+_URLS = {
+    "train": "train.csv",
+    "dev": "valid.csv",
+    "test": "test.csv"
+}
+
+
+class FrenchQAConfig(datasets.BuilderConfig):
+    """BuilderConfig for frenchQA."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for FrenchQA.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FrenchQAConfig, self).__init__(**kwargs)
+
+
+class FrenchQA(datasets.GeneratorBasedBuilder):
+    """TODO(squad_v2): Short description of my dataset."""
+
+    # TODO(squad_v2): Set up version.
+    BUILDER_CONFIGS = [
+        FrenchQAConfig(name="frenchQA", version=datasets.Version("1.0.0"), description="frenchQA"),
+    ]
+
+    def _info(self):
+        # TODO(squad_v2): Specifies the datasets.DatasetInfo object
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.features.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "answer_start": datasets.Value("int32"),
+                        }
+                    ),
+                    # These are the features of your dataset like images, labels ...
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="",
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(squad_v2): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        urls_to_download = _URLS
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            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"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(squad_v2): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            squad = csv.DictReader(f, delimiter = ";")
+            for id_, row in enumerate(squad):
+                answer_start = []
+                text = []
+                
+                if row["answer_start"] != "-1":
+                    answer_start = [row["answer_start"]]
+                    text = [row["answer"]]
+                    
+                yield id_, {
+                    "title": row["dataset"],
+                    "context": row["context"],
+                    "question": row["question"],
+                    "id": id_,
+                    "answers": {
+                        "answer_start": answer_start,
+                        "text": text,
+                    },
+                }