Require data manual download

fquad.py

@@ -1,13 +1,21 @@
-"""TODO(fquad): Add a description here."""
+"""FQuAD dataset."""
 
 
 import json
 import os
+from textwrap import dedent
 
 import datasets
 
 
-# TODO(fquad): BibTeX citation
+_HOMEPAGE = "https://fquad.illuin.tech/"
+
+_DESCRIPTION = """\
+FQuAD: French Question Answering Dataset
+We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs.
+Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%.
+"""
+
 _CITATION = """\
 @ARTICLE{2020arXiv200206071
        author = {Martin, d'Hoffschmidt and Maxime, Vidal and
@@ -25,33 +33,28 @@ archivePrefix = {arXiv},
 }
 """
 
-# TODO(fquad):
-_DESCRIPTION = """\
-FQuAD: French Question Answering Dataset
-We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs.
-Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%.
 
-"""
+class Fquad(datasets.GeneratorBasedBuilder):
+    """FQuAD dataset."""
 
-_URL = "https://storage.googleapis.com/illuin/fquad/"
-_URLS = {
-    "train": _URL + "train.json.zip",
-    "valid": _URL + "valid.json.zip",
-}
+    VERSION = datasets.Version("1.0.0")
 
+    @property
+    def manual_download_instructions(self):
+        return dedent("""\
+        To access the data for this dataset, you need to request it at:
+        https://fquad.illuin.tech/#download
 
-class Fquad(datasets.GeneratorBasedBuilder):
-    """TODO(fquad): Short description of my dataset."""
+        Unzip the downloaded file with `unzip download-form-fquad1.0.zip -d <path/to/directory>`, into a destination
+        directory <path/to/directory>, which will contain the two data files train.json and valid.json.
 
-    # TODO(fquad): Set up version.
-    VERSION = datasets.Version("0.1.0")
+        To load the dataset, pass the full path to the destination directory
+        in your call to the loading function: `datasets.load_dataset("fquad", data_dir="<path/to/directory>")`
+        """)
 
     def _info(self):
-        # TODO(fquad): 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(
                 {
                     "context": datasets.Value("string"),
@@ -62,39 +65,28 @@ class Fquad(datasets.GeneratorBasedBuilder):
                     # 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="https://fquad.illuin.tech/",
+            homepage=_HOMEPAGE,
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # TODO(fquad): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        download_urls = _URLS
-        dl_dir = dl_manager.download_and_extract(download_urls)
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(dl_dir["train"], "train.json")},
+                gen_kwargs={"filepath": os.path.join(data_dir, "train.json")},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(dl_dir["valid"], "valid.json")},
+                gen_kwargs={"filepath": os.path.join(data_dir, "valid.json")},
             ),
         ]
 
     def _generate_examples(self, filepath):
-
         """Yields examples."""
-        # TODO(fquad): Yields (key, example) tuples from the dataset
         with open(filepath, encoding="utf-8") as f:
             data = json.load(f)
             for id1, examples in enumerate(data["data"]):