Upload scifact.py

Adding a scifact data loader script.

scifact.py

@@ -0,0 +1,161 @@
+import json
+import datasets
+
+_DESCRIPTION = """
+SciFact
+
+A dataset of expert-written scientific claims paired with evidence-containing
+abstracts and annotated with labels and rationales.
+"""
+
+_CITATION = """
+@InProceedings{Wadden2020FactOF,
+  author =      {David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang,
+                 Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi},
+  title =       {Fact or Fiction: Verifying Scientific Claims},
+  booktitle =   {EMNLP},
+  year =        2020,
+}
+"""
+
+_DOWNLOAD_URL = "https://testerstories.com/files/ai_learn/data.tar.gz"
+
+
+class ScifactConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(ScifactConfig, self).__init__(
+            version=datasets.Version("1.0.0", ""), **kwargs
+        )
+
+
+class Scifact(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("0.1.0")
+
+    BUILDER_CONFIGS = [
+        ScifactConfig(name="corpus", description="The corpus of evidence documents"),
+        ScifactConfig(
+            name="claims", description="The claims are split into train, test, dev"
+        ),
+    ]
+
+    def _info(self):
+        if self.config.name == "corpus":
+            features = {
+                "doc_id": datasets.Value("int32"),
+                "title": datasets.Value("string"),
+                "abstract": datasets.features.Sequence(datasets.Value("string")),
+                "structured": datasets.Value("bool"),
+            }
+        else:
+            features = {
+                "id": datasets.Value("int32"),
+                "claim": datasets.Value("string"),
+                "evidence_doc_id": datasets.Value("string"),
+                "evidence_label": datasets.Value("string"),
+                "evidence_sentences": datasets.features.Sequence(
+                    datasets.Value("int32")
+                ),
+                "cited_doc_ids": datasets.features.Sequence(datasets.Value("int32")),
+            }
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            supervised_keys=None,
+            homepage="https://scifact.apps.allenai.org/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive = dl_manager.download(_DOWNLOAD_URL)
+
+        if self.config.name == "corpus":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": "data/corpus.jsonl",
+                        "split": "train",
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+            ]
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": "data/claims_train.jsonl",
+                        "split": "train",
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": "data/claims_test.jsonl",
+                        "split": "test",
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": "data/claims_dev.jsonl",
+                        "split": "dev",
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, filepath, split, files):
+        for path, f in files:
+            if path == filepath:
+                for id_, row in enumerate(f):
+                    data = json.loads(row.decode("utf-8"))
+
+                    if self.config.name == "corpus":
+                        yield id_, {
+                            "doc_id": int(data["doc_id"]),
+                            "title": data["title"],
+                            "abstract": data["abstract"],
+                            "structured": data["structured"],
+                        }
+                    else:
+                        if split == "test":
+                            yield id_, {
+                                "id": data["id"],
+                                "claim": data["claim"],
+                                "evidence_doc_id": "",
+                                "evidence_label": "",
+                                "evidence_sentences": [],
+                                "cited_doc_ids": [],
+                            }
+                        else:
+                            evidences = data["evidence"]
+
+                            if evidences:
+                                for id1, doc_id in enumerate(evidences):
+                                    for id2, evidence in enumerate(evidences[doc_id]):
+                                        yield str(id_) + "_" + str(id1) + "_" + str(
+                                            id2
+                                        ), {
+                                            "id": data["id"],
+                                            "claim": data["claim"],
+                                            "evidence_doc_id": doc_id,
+                                            "evidence_label": evidence["label"],
+                                            "evidence_sentences": evidence["sentences"],
+                                            "cited_doc_ids": data.get(
+                                                "cited_doc_ids", []
+                                            ),
+                                        }
+                            else:
+                                yield id_, {
+                                    "id": data["id"],
+                                    "claim": data["claim"],
+                                    "evidence_doc_id": "",
+                                    "evidence_label": "",
+                                    "evidence_sentences": [],
+                                    "cited_doc_ids": data.get("cited_doc_ids", []),
+                                }
+                break