Initial commit

boolq.py

@@ -0,0 +1,88 @@
+# Based on https://huggingface.co/datasets/boolq/blob/main/boolq.py
+
+import json
+
+import datasets
+
+
+# TODO(boolq): BibTeX citation
+_CITATION = """\
+@inproceedings{clark2019boolq,
+  title =     {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
+  author =    {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
+  booktitle = {NAACL},
+  year =      {2019},
+}
+"""
+
+# TODO(boolq):
+_DESCRIPTION = """\
+BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
+occurring ---they are generated in unprompted and unconstrained settings.
+Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
+The text-pair classification setup is similar to existing natural language inference tasks.
+"""
+
+_URL = "https://storage.googleapis.com/boolq/"
+_URLS = {
+    "train": _URL + "train.jsonl",
+    "dev": _URL + "dev.jsonl",
+}
+
+
+class Boolq(datasets.GeneratorBasedBuilder):
+    """TODO(boolq): Short description of my dataset."""
+
+    # TODO(boolq): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(boolq): 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(
+                {
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("bool"),
+                    "passage": datasets.Value("string")
+                    # 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://github.com/google-research-datasets/boolean-questions",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(boolq): 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(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"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(boolq): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                question = data["question"]
+                answer = data["answer"]
+                passage = data["passage"]
+                title = data["title"]
+                yield id_, {"question": question, "answer": answer, "passage": passage, "title": title}