Initial commit

.gitattributes

@@ -52,3 +52,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+train-v2.0.json filter=lfs diff=lfs merge=lfs -text

squad_v2_fi.py

@@ -0,0 +1,94 @@
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+_DESCRIPTION = """\
+combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
+ to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
+ also determine when no answer is supported by the paragraph and abstain from answering.
+"""
+
+
+class SquadV2Config(datasets.BuilderConfig):
+    """BuilderConfig for SQUAD."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SQUADV2.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SquadV2Config, self).__init__(**kwargs)
+
+
+class SquadV2(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        SquadV2Config(name="squad_v2_fi", version=datasets.Version(
+            "1.0.0"), description="Finnish SQuAD v2.0"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            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"),
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://turkunlp.org/",
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
+                                    "filepath": "train-v2.0.json"}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={
+                                    "filepath": "dev-v2.0.json"}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            squad = json.load(f)
+            for example in squad["data"]:
+                title = example.get("title", "")
+                for paragraph in example["paragraphs"]:
+                    context = paragraph["context"]
+                    for qa in paragraph["qas"]:
+                        question = qa["question"]
+                        id_ = qa["id"]
+
+                        answer_starts = [answer["answer_start"]
+                                         for answer in qa["answers"]]
+                        answers = [answer["text"].strip(
+                            ' .,-:') for answer in qa["answers"]]
+
+                        yield id_, {
+                            "title": title,
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                        }

train-v2.0.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ccf811e33d3f794a39b9a7da0a60e8302ec3304d08224478c8d5756f4d073ea8
+size 55320231