from https://github.com/ArneBinder/pie-datasets/pull/152

scifact.py

@@ -0,0 +1,245 @@
+import json
+import logging
+import os
+from collections import defaultdict
+from copy import copy
+from typing import Any, Dict, Iterable, List
+
+import datasets
+from datasets import GeneratorBasedBuilder
+
+logger = logging.getLogger(__name__)
+
+_DESCRIPTION = """\
+SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated \\
+with labels and rationales. This version differs from `allenai/scifact` on HF because we do not have separate splits \\
+for claims and a corpus, instead we combine documents with claims that it supports or refutes, note that there are \\
+also some documents that do not have any claims associated with them as well as there are some claims that do not \\
+have any evidence. In the latter case we assign all such claims to the DUMMY document with ID -1 and without any text \\
+(i.e. abstract sentences).
+"""
+
+DATA_URL = "https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"
+SUBDIR = "data"
+
+VARIANT_DOCUMENTS = "as_documents"
+VARIANT_CLAIMS = "as_claims"
+
+
+class ScifactConfig(datasets.BuilderConfig):
+    """BuilderConfig for Scifact."""
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+class SciFact(GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        ScifactConfig(
+            name=VARIANT_DOCUMENTS,
+            description="Documents that serve as evidence for some claims that are split into train, test, dev",
+        ),
+        ScifactConfig(
+            name=VARIANT_CLAIMS,
+            description="Documents that serve as evidence for some claims that are split into train, test, dev",
+        ),
+    ]
+
+    def _info(self):
+        # Specifies the datasets.DatasetInfo object
+        if self.config.name == VARIANT_DOCUMENTS:
+            features = {
+                "doc_id": datasets.Value("int32"),  # document ID
+                "title": datasets.Value("string"),  # document title
+                "abstract": datasets.features.Sequence(
+                    datasets.Value("string")
+                ),  # document sentences
+                "structured": datasets.Value(
+                    "bool"
+                ),  # whether the abstract is structured, i.e. has OBJECTIVE, CONCLUSION, METHODS marked in the text
+                "claims": datasets.features.Sequence(
+                    feature={
+                        "id": datasets.Value(dtype="int32", id=None),
+                        "claim": datasets.Value(dtype="string", id=None),
+                        "evidence": datasets.features.Sequence(
+                            feature={
+                                "label": datasets.Value(dtype="string", id=None),
+                                "sentences": datasets.features.Sequence(
+                                    datasets.Value(dtype="int32", id=None)
+                                ),
+                            }
+                        ),
+                    }
+                ),  # list of claims associated with the document
+            }
+        elif self.config.name == VARIANT_CLAIMS:
+            features = {
+                "id": datasets.Value("int32"),  # document ID
+                "claim": datasets.Value(dtype="string", id=None),
+                "cited_docs": datasets.features.Sequence(
+                    feature={
+                        "doc_id": datasets.Value(dtype="int32", id=None),
+                        "title": datasets.Value("string"),  # document title
+                        "abstract": datasets.features.Sequence(
+                            datasets.Value("string")
+                        ),  # document sentences
+                        "structured": datasets.Value(
+                            "bool"
+                        ),  # whether the abstract is structured, i.e. has OBJECTIVE, CONCLUSION, METHODS marked in the text
+                        "evidence": datasets.features.Sequence(
+                            feature={
+                                "label": datasets.Value(dtype="string", id=None),
+                                "sentences": datasets.features.Sequence(
+                                    datasets.Value(dtype="int32", id=None)
+                                ),
+                            }
+                        ),
+                    }
+                ),  # list of claims associated with the document
+            }
+        else:
+            raise ValueError(f"unknown dataset variant: {self.config.name}")
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://scifact.apps.allenai.org/",
+        )
+
+    def _generate_examples(self, claims_filepath: str, corpus_filepath: str):
+        """Yields examples."""
+        with open(claims_filepath) as f:
+            claim_data = [json.loads(line) for line in f.readlines()]
+
+        with open(corpus_filepath) as f:
+            corpus_docs = [json.loads(line) for line in f.readlines()]
+
+        if self.config.name == VARIANT_DOCUMENTS:
+            doc_id2claims = defaultdict(list)
+            for claim in claim_data:
+                cited_doc_ids = claim.pop("cited_doc_ids", [-1])
+                evidence = claim.pop("evidence", dict())
+                for cited_doc_id in cited_doc_ids:
+                    current_claim = claim.copy()
+                    current_claim["evidence"] = evidence.get(str(cited_doc_id), [])
+                    doc_id2claims[cited_doc_id].append(current_claim)
+            dummy_doc = {"doc_id": -1, "title": "", "abstract": [], "structured": False}
+            corpus_docs = [dummy_doc] + corpus_docs
+
+            for id_, doc in enumerate(corpus_docs):
+                doc = doc.copy()
+                doc["claims"] = doc_id2claims.get(doc["doc_id"], [])
+                yield id_, doc
+        elif self.config.name == VARIANT_CLAIMS:
+            doc_id2doc = {doc["doc_id"]: doc for doc in corpus_docs}
+            for _id, claim in enumerate(claim_data):
+                evidence = claim.pop("evidence", {})
+                cited_doc_ids = claim.pop("cited_doc_ids", [])
+                claim["cited_docs"] = []
+                for cited_doc_id in cited_doc_ids:
+                    doc = copy(doc_id2doc[cited_doc_id])
+                    doc["evidence"] = evidence.get(str(cited_doc_id), [])
+                    claim["cited_docs"].append(doc)
+                yield _id, claim
+        else:
+            raise ValueError(f"unknown dataset variant: {self.config.name}")
+
+    def _split_generators(self, dl_manager):
+        """We handle string, list and dicts in datafiles."""
+        if dl_manager.manual_dir is None:
+            data_dir = os.path.join(dl_manager.download_and_extract(DATA_URL), SUBDIR)
+        else:
+            # Absolute path of the manual_dir
+            data_dir = os.path.abspath(dl_manager.manual_dir)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "claims_filepath": os.path.join(data_dir, "claims_train.jsonl"),
+                    "corpus_filepath": os.path.join(data_dir, "corpus.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "claims_filepath": os.path.join(data_dir, "claims_dev.jsonl"),
+                    "corpus_filepath": os.path.join(data_dir, "corpus.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "claims_filepath": os.path.join(data_dir, "claims_test.jsonl"),
+                    "corpus_filepath": os.path.join(data_dir, "corpus.jsonl"),
+                },
+            ),
+        ]
+
+    def _convert_to_output_eval_format(
+        self, data: Iterable[Dict[str, Any]]
+    ) -> List[Dict[str, Any]]:
+        """Output should have the format as specified here:
+
+        https://github.com/allenai/scifact/blob/68b98a56d93e0f9da0d2aab4e6c3294699a0f72e/doc/evaluation.md#submission-format
+        Each claim is represented as Dict with:
+            "id": int An integer claim ID.
+            "evidence": Dict[str, Dict] The evidence for the claim.
+            "doc_id": Dict[str, Any] The sentences and label for a single document.
+                 "sentences": List[int]
+                 "label": str
+        """
+        if self.config.name == VARIANT_DOCUMENTS:
+            # Collect all claim-level annotations from all documents
+            claim2doc2sent_with_label = dict()
+            for document in data:
+                doc_id = document["doc_id"]
+                # Skip if document does not have any related claims
+                if len(document["claims"]["claim"]) == 0:
+                    continue
+                for idx in range(len(document["claims"]["claim"])):
+                    claim_id = document["claims"]["id"][idx]
+                    claim_text = document["claims"]["claim"][idx]
+                    claim_evidence = document["claims"]["evidence"][idx]
+                    if claim_id not in claim2doc2sent_with_label:
+                        claim2doc2sent_with_label[claim_id] = dict()
+                    if doc_id not in claim2doc2sent_with_label[claim_id]:
+                        if len(claim_evidence["label"]) > 0:
+                            ev_label = claim_evidence["label"][0]
+                            claim2doc2sent_with_label[claim_id][doc_id] = {
+                                "label": ev_label,
+                                "sentences": [],
+                            }
+                            for ev_sentences in claim_evidence["sentences"]:
+                                claim2doc2sent_with_label[claim_id][doc_id]["sentences"].extend(
+                                    ev_sentences
+                                )
+
+            outputs = []
+            for claim_id in claim2doc2sent_with_label:
+                claim_dict = {"id": claim_id, "evidence": dict()}
+                for doc_id in claim2doc2sent_with_label[claim_id]:
+                    claim_dict["evidence"][doc_id] = {
+                        "sentences": claim2doc2sent_with_label[claim_id][doc_id]["sentences"],
+                        "label": claim2doc2sent_with_label[claim_id][doc_id]["label"],
+                    }
+                outputs.append((int(claim_id), claim_dict.copy()))
+
+            outputs_sorted_by_claim_ids = [
+                claim for claim_id, claim in sorted(outputs, key=lambda x: x[0])
+            ]
+
+            return outputs_sorted_by_claim_ids
+
+        elif self.config.name == VARIANT_CLAIMS:
+            raise NotImplementedError(
+                f"_convert_to_output_eval_format is not yet implemented for dataset variant {self.config.name}"
+            )
+        else:
+            raise ValueError(f"unknown dataset variant: {self.config.name}")