Making progress.

scifact_entailment.py

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+"""Scientific fact-checking dataset. Verifies claims based on citation sentences
+using evidence from the cited abstracts. Formatted as a paragraph-level entailment task."""
+
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{Wadden2020FactOF,
+  title={Fact or Fiction: Verifying Scientific Claims},
+  author={David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
+  booktitle={EMNLP},
+  year={2020},
+}
+"""
+
+_DESCRIPTION = """\
+SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
+"""
+
+
+class ScifactEntailmentConfig(datasets.BuilderConfig):
+    """BuilderConfig for Scifact"""
+
+    def __init__(self, **kwargs):
+        """
+
+        Args:
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(ScifactEntailmentConfig, self).__init__(
+            version=datasets.Version("1.0.0", ""), **kwargs
+        )
+
+
+class ScifactEntailment(datasets.GeneratorBasedBuilder):
+    """TODO(scifact): Short description of my dataset."""
+
+    # TODO(scifact): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(scifact): Specifies the datasets.DatasetInfo object
+        features = {
+            "id": datasets.Value("int32"),  # An integer claim ID.
+            "claim": datasets.Value("string"),  # The text of the claim.
+            "evidence_doc_id": datasets.Value("string"),
+            "evidence_label": datasets.Value("string"),  # Label for the rationale.
+            "evidence_sentences": datasets.features.Sequence(
+                datasets.Value("int32")
+            ),  # Rationale sentences.
+            "cited_doc_ids": datasets.features.Sequence(
+                datasets.Value("int32")
+            ),  # The claim's "cited documents".
+        }
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                features
+                # 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://scifact.apps.allenai.org/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(scifact): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, split):
+        """Yields examples."""
+        # TODO(scifact): Yields (key, example) tuples from the dataset
+
+        # Load corpus and convert to dict.
+        corpus = datasets.load_dataset("bigbio/scifact", "scifact_corpus_source", split="train")
+        corpus = {x["doc_id"]: x for x in corpus}
+
+        # Load claims.
+        claims = datasets.load_dataset("bigbio/scifact", "scifact_claims_source", split=split)
+
+        for id_, claim in enumerate(claims):
+            evidence = {x["doc_id"]: x for x in claim["evidences"]}
+            for cited_doc_id in claim["cited_doc_ids"]:
+                cited_doc = corpus[cited_doc_id]
+                # Format the abstract.
+                sent_ids = [f"[{i}]" for i in range(len(cited_doc["abstract"]))]
+                # Get rid of newlines.
+                sents = [sent.strip() for sent in cited_doc["abstract"]]
+                zipped = zip(sent_ids, sents)
+                cited_abstract = " ".join(
+                    [f"{entry[0]} {entry[1]}" for entry in zipped]
+                )
+
+                if cited_doc_id in evidence:
+                    verdict = evidence[cited_doc_id]["label"]
+                    sents = evidence[cited_doc_id]["sentence_ids"]
+                else:
+                    verdict = "NEI"
+                    sents = []
+
+                instance = {
+                    "id": claim["id"],
+                    "claim": claim["claim"],
+                    "abstract_id": cited_doc_id,
+                    "title":  cited_doc["title"],
+                    "abstract": cited_abstract,
+                    "verdict": verdict,
+                    "evidence": sents,
+                }
+
+                yield id_, instance