# Modified by Nora Belrose of EleutherAI (2023) # coding=utf-8 # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """FEVER dataset.""" import json import os import textwrap import datasets class FeverConfig(datasets.BuilderConfig): """BuilderConfig for FEVER.""" def __init__(self, homepage: str = None, citation: str = None, base_url: str = None, urls: dict = None, **kwargs): """BuilderConfig for FEVER. 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 Fever(datasets.GeneratorBasedBuilder): """Fact Extraction and VERification Dataset.""" BUILDER_CONFIGS = [ FeverConfig( name="v1.0", version=datasets.Version("1.0.0"), description=textwrap.dedent( "FEVER v1.0\n" "FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences " "extracted from Wikipedia and subsequently verified without knowledge of the sentence they were " "derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two " "classes, the annotators also recorded the sentence(s) forming the necessary evidence for their " "judgment." ), homepage="https://fever.ai/dataset/fever.html", citation=textwrap.dedent( """\ @inproceedings{Thorne18Fever, author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit}, title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}}, booktitle = {NAACL-HLT}, year = {2018} }""" ), base_url="https://fever.ai/download/fever", urls={ datasets.Split.TRAIN: "train.jsonl", "dev": "shared_task_dev.jsonl", "paper_dev": "paper_dev.jsonl", "paper_test": "paper_test.jsonl", }, ), FeverConfig( name="v2.0", version=datasets.Version("2.0.0"), description=textwrap.dedent( "FEVER v2.0:\n" "The FEVER 2.0 Dataset consists of 1174 claims created by the submissions of participants in the " "Breaker phase of the 2019 shared task. Participants (Breakers) were tasked with generating " "adversarial examples that induce classification errors for the existing systems. Breakers submitted " "a dataset of up to 1000 instances with equal number of instances for each of the three classes " "(Supported, Refuted NotEnoughInfo). Only novel claims (i.e. not contained in the original FEVER " "dataset) were considered as valid entries to the shared task. The submissions were then manually " "evaluated for Correctness (grammatical, appropriately labeled and meet the FEVER annotation " "guidelines requirements)." ), homepage="https://fever.ai/dataset/adversarial.html", citation=textwrap.dedent( """\ @inproceedings{Thorne19FEVER2, author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit}, title = {The {FEVER2.0} Shared Task}, booktitle = {Proceedings of the Second Workshop on {Fact Extraction and VERification (FEVER)}}, year = {2018} }""" ), base_url="https://fever.ai/download/fever2.0", urls={ datasets.Split.VALIDATION: "fever2-fixers-dev.jsonl", }, ), FeverConfig( name="wiki_pages", version=datasets.Version("1.0.0"), description=textwrap.dedent( "Wikipedia pages for FEVER v1.0:\n" "FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences " "extracted from Wikipedia and subsequently verified without knowledge of the sentence they were " "derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two " "classes, the annotators also recorded the sentence(s) forming the necessary evidence for their " "judgment." ), homepage="https://fever.ai/dataset/fever.html", citation=textwrap.dedent( """\ @inproceedings{Thorne18Fever, author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit}, title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}}, booktitle = {NAACL-HLT}, year = {2018} }""" ), base_url="https://fever.ai/download/fever", urls={ "wikipedia_pages": "wiki-pages.zip", }, ), ] def _info(self): if self.config.name == "wiki_pages": features = { "id": datasets.Value("string"), "text": datasets.Value("string"), "lines": datasets.Value("string"), } elif self.config.name == "v1.0" or self.config.name == "v2.0": features = { "id": datasets.Value("int32"), "label": datasets.ClassLabel(names=["REFUTES", "SUPPORTS"]), "claim": datasets.Value("string"), "evidence_annotation_id": datasets.Value("int32"), "evidence_id": datasets.Value("int32"), "evidence_wiki_url": datasets.Value("string"), "evidence_sentence_id": datasets.Value("int32"), } 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): """Returns SplitGenerators.""" dl_paths = dl_manager.download_and_extract(self.config.urls) return [ datasets.SplitGenerator( name=split, gen_kwargs={ "filepath": dl_paths[split] if self.config.name != "wiki_pages" else dl_manager.iter_files(os.path.join(dl_paths[split], "wiki-pages")), }, ) for split in dl_paths.keys() ] def _generate_examples(self, filepath): """Yields examples.""" if self.config.name == "v1.0" or self.config.name == "v2.0": with open(filepath, encoding="utf-8") as f: for row_id, row in enumerate(f): data = json.loads(row) id_ = data["id"] label = data.get("label", "") # Drop the examples with label "NOT ENOUGH INFO" if label not in ("REFUTES", "SUPPORTS"): continue claim = data["claim"] evidences = data.get("evidence", []) if len(evidences) > 0: for i in range(len(evidences)): for j in range(len(evidences[i])): annot_id = evidences[i][j][0] if evidences[i][j][0] else -1 evidence_id = evidences[i][j][1] if evidences[i][j][1] else -1 wiki_url = evidences[i][j][2] if evidences[i][j][2] else "" sent_id = evidences[i][j][3] if evidences[i][j][3] else -1 yield str(row_id) + "_" + str(i) + "_" + str(j), { "id": id_, "label": label, "claim": claim, "evidence_annotation_id": annot_id, "evidence_id": evidence_id, "evidence_wiki_url": wiki_url, "evidence_sentence_id": sent_id, } else: yield row_id, { "id": id_, "label": label, "claim": claim, "evidence_annotation_id": -1, "evidence_id": -1, "evidence_wiki_url": "", "evidence_sentence_id": -1, } elif self.config.name == "wiki_pages": for file_id, file in enumerate(filepath): with open(file, encoding="utf-8") as f: for row_id, row in enumerate(f): data = json.loads(row) yield f"{file_id}_{row_id}", data