Add dataset loading script

feverous.py

@@ -0,0 +1,126 @@
+"""FEVEROUS dataset."""
+
+import json
+import textwrap
+
+import datasets
+
+
+class FeverousConfig(datasets.BuilderConfig):
+    """BuilderConfig for FEVER."""
+
+    def __init__(self, homepage: str = None, citation: str = None, base_url: str = None, urls: dict = None, **kwargs):
+        """BuilderConfig for FEVEROUS.
+
+        Args:
+            homepage (`str`): Homepage.
+            citation (`str`): Citation reference.
+            base_url (`str`): Data base URL that precedes all data URLs.
+            urls (`dict`): Data URLs (each URL will pe preceded by `base_url`).
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(**kwargs)
+        self.homepage = homepage
+        self.citation = citation
+        self.base_url = base_url
+        self.urls = {key: f"{base_url}/{url}" for key, url in urls.items()}
+
+
+class FeverOUS(datasets.GeneratorBasedBuilder):
+    """FEVEROUS dataset."""
+
+    BUILDER_CONFIGS = [
+        FeverousConfig(
+            version=datasets.Version("1.0.0"),
+            description=textwrap.dedent(
+                "FEVEROUS:\n"
+                "FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) is a fact "
+                "verification dataset which consists of 87,026 verified claims. Each claim is annotated with evidence "
+                "in the form of sentences and/or cells from tables in Wikipedia, as well as a label indicating whether "
+                "this evidence supports, refutes, or does not provide enough information to reach a verdict. The "
+                "dataset also contains annotation metadata such as annotator actions (query keywords, clicks on page, "
+                "time signatures), and the type of challenge each claim poses."
+            ),
+            homepage="https://fever.ai/dataset/feverous.html",
+            citation=textwrap.dedent(
+                """\
+                @inproceedings{Aly21Feverous,
+                    author = {Aly, Rami and Guo, Zhijiang and Schlichtkrull, Michael Sejr and Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Cocarascu, Oana and Mittal, Arpit},
+                    title = {{FEVEROUS}: Fact Extraction and {VERification} Over Unstructured and Structured information},
+                    eprint={2106.05707},
+                    archivePrefix={arXiv},
+                    primaryClass={cs.CL},
+                    year = {2021}
+                }"""
+            ),
+            base_url="https://fever.ai/download/feverous",
+            urls={
+                datasets.Split.TRAIN: "feverous_train_challenges.jsonl",
+                datasets.Split.VALIDATION: "feverous_dev_challenges.jsonl",
+                datasets.Split.TEST: "feverous_test_unlabeled.jsonl",
+            },
+        ),
+    ]
+
+    def _info(self):
+        features = {
+            "id": datasets.Value("int32"),
+            "label": datasets.ClassLabel(names=["SUPPORTS", "REFUTES", "NOT ENOUGH INFO"]),
+            "claim": datasets.Value("string"),
+            "evidence": [
+                {
+                    "content": [datasets.Value("string")],
+                    "context": [[datasets.Value("string")]],
+                }
+            ],
+            "annotator_operations": [
+                {
+                    "operation": datasets.Value("string"),
+                    "value": datasets.Value("string"),
+                    "time": datasets.Value("float"),
+                }
+            ],
+            "expected_challenge": datasets.Value("string"),
+            "challenge": datasets.Value("string"),
+        }
+        return datasets.DatasetInfo(
+            description=self.config.description,
+            features=datasets.Features(features),
+            homepage=self.config.homepage,
+            citation=self.config.citation,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_paths = dl_manager.download_and_extract(self.config.urls)
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={
+                    "filepath": dl_paths[split],
+                },
+            )
+            for split in dl_paths.keys()
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                # First item in "train" has all values equal to empty strings
+                if [value for value in data.values() if value]:
+                    evidence = data.get("evidence", [])
+                    if evidence:
+                        for evidence_set in evidence:
+                            # Transform "context" from dict to list (analogue to "content")
+                            evidence_set["context"] = [
+                                evidence_set["context"][element_id] for element_id in evidence_set["content"]
+                            ]
+                    yield id_, {
+                        "id": data.get("id"),
+                        "label": data.get("label", -1),
+                        "claim": data.get("claim", ""),
+                        "evidence": evidence,
+                        "annotator_operations": data.get("annotator_operations", []),
+                        "expected_challenge": data.get("expected_challenge", ""),
+                        "challenge": data.get("challenge", ""),
+                    }