| import json | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @article{Narayan2018DontGM, | |
| title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization}, | |
| author={Shashi Narayan and Shay B. Cohen and Mirella Lapata}, | |
| journal={ArXiv}, | |
| year={2018}, | |
| volume={abs/1808.08745} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| This is the XSUM subset of the GEM benchmark. | |
| """ | |
| _URLs = { | |
| "xsum": { | |
| "data": "http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz", | |
| "splits": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_xsum_confidence_0.8.json", | |
| "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/xsum.zip", | |
| }, | |
| } | |
| _XSUM_REMOVE_LINES = set( | |
| [ | |
| "Share this with\n", | |
| "Email\n", | |
| "Facebook\n", | |
| "Messenger\n", | |
| "Twitter\n", | |
| "Pinterest\n", | |
| "WhatsApp\n", | |
| "Linkedin\n", | |
| "LinkedIn\n", | |
| "Copy this link\n", | |
| "These are external links and will open in a new window\n", | |
| ] | |
| ) | |
| class Xsum(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name=lang, | |
| version=datasets.Version("1.0.0"), | |
| description="", | |
| ) | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features = datasets.Features( | |
| { | |
| "gem_id": datasets.Value("string"), | |
| "gem_parent_id": datasets.Value("string"), | |
| "xsum_id": datasets.Value("string"), | |
| "document": datasets.Value("string"), | |
| "target": datasets.Value("string"), | |
| "references": [datasets.Value("string")], | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage="", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| dl_dir = dl_manager.download_and_extract(_URLs[self.config.name]) | |
| challenge_sets = [ | |
| ("challenge_train_sample", "train_xsum_RandomSample500.json"), | |
| ("challenge_validation_sample", "validation_xsum_RandomSample500.json"), | |
| ("challenge_test_backtranslation", "test_xsum_BackTranslation500.json"), | |
| ("challenge_test_bfp_02", "test_xsum_ButterFingersPerturbation_p=0.02_500.json"), | |
| ("challenge_test_bfp_05", "test_xsum_ButterFingersPerturbation_p=0.05_500.json"), | |
| ("challenge_test_nopunc", "test_xsum_WithoutPunctuation500.json"), | |
| ("challenge_test_covid", f"en_test_covid19.jsonl"), | |
| ] | |
| return [ | |
| datasets.SplitGenerator( | |
| name=challenge_split, | |
| gen_kwargs={ | |
| "filepath": os.path.join(dl_dir["challenge_set"], "xsum", filename), | |
| "split": challenge_split, | |
| }, | |
| ) | |
| for challenge_split, filename in challenge_sets | |
| ] | |
| def _generate_examples(self, filepath, split, filepaths=None, lang=None): | |
| """Yields examples.""" | |
| if "challenge" in split: | |
| if "covid" in split: | |
| with open(filepath, encoding="utf-8") as f: | |
| id_ = -1 | |
| for line in f: | |
| data = json.loads(line) | |
| id_ += 1 | |
| yield id_, { | |
| "gem_id": f"{self.config.name}-{split}-{id_}", | |
| "gem_parent_id": f"{self.config.name}-{split}-{id_}", | |
| "xsum_id": data["url"], | |
| "document": data["text"], | |
| "target": data["summary"], | |
| "references": [] if split == "train" else [data["summary"]], | |
| } | |
| else: | |
| exples = json.load(open(filepath, encoding="utf-8")) | |
| if isinstance(exples, dict): | |
| assert len(exples) == 1, "multiple entries found" | |
| exples = list(exples.values())[0] | |
| for id_, exple in enumerate(exples): | |
| exple["gem_parent_id"] = exple["gem_id"] | |
| exple["gem_id"] = f"{self.config.name}-{split}-{id_}" | |
| yield id_, exple | |