Datasets:
Tasks:
Text2Text Generation
Languages:
English
| import csv | |
| # Lint as: python3 | |
| import os | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """\ | |
| @InProceedings{D17-1063, | |
| author = "Zhang, Xingxing and Lapata, Mirella", | |
| title = "Sentence Simplification with Deep Reinforcement Learning", | |
| booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", | |
| year = "2017", | |
| publisher = "Association for Computational Linguistics", | |
| pages = "595--605", | |
| location = "Copenhagen, Denmark", | |
| url = "http://aclweb.org/anthology/D17-1063" | |
| } | |
| """ | |
| _DESCRIPTION = "WikiLarge corpus for sentence simplification gathered by Zhang, Xingxing and Lapata, Mirella." | |
| _URLS = { | |
| "train_src_ori": "https://huggingface.co/datasets/waboucay/wikilarge/resolve/main/wiki.full.aner.ori.train.src?download=true", | |
| "train_dst_ori": "https://huggingface.co/datasets/waboucay/wikilarge/resolve/main/wiki.full.aner.ori.train.dst?download=true", | |
| "valid_src_ori": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.ori.valid.src", | |
| "valid_dst_ori": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.ori.valid.dst", | |
| "test_src_ori": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.ori.test.src", | |
| "test_dst_ori": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.ori.test.dst", | |
| "train_src_ner": "https://huggingface.co/datasets/waboucay/wikilarge/resolve/main/wiki.full.aner.train.src?download=true", | |
| "train_dst_ner": "https://huggingface.co/datasets/waboucay/wikilarge/resolve/main/wiki.full.aner.train.dst?download=true", | |
| "valid_src_ner": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.valid.src", | |
| "valid_dst_ner": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.valid.dst", | |
| "test_src_ner": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.test.src", | |
| "test_dst_ner": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.test.dst" | |
| } | |
| _TRAINING_FILE = "train.csv" | |
| _DEV_FILE = "valid.csv" | |
| _TEST_FILE = "test.csv" | |
| class WikiLargeConfig(datasets.BuilderConfig): | |
| """BuilderConfig for WikiLarge dataset""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for WikiLarge dataset | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(WikiLargeConfig, self).__init__(**kwargs) | |
| class WikiLarge(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0", "") | |
| BUILDER_CONFIG_CLASS = WikiLargeConfig | |
| BUILDER_CONFIGS = [ | |
| WikiLargeConfig( | |
| name="original", | |
| version=datasets.Version("1.0.0", ""), | |
| description=_DESCRIPTION, | |
| ), | |
| WikiLargeConfig( | |
| name="ner_tagged", | |
| version=datasets.Version("1.0.0", ""), | |
| description=_DESCRIPTION + "\n\nVersion with NER tags replacing named entities.", | |
| ) | |
| ] | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "complex": datasets.Value("string"), | |
| "simple": datasets.Value("string"), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| supervised_keys=None, | |
| homepage="https://github.com/XingxingZhang/dress/tree/master", | |
| ) | |
| def _split_generators(self, dl_manager): | |
| dl_files = dl_manager.download(_URLS) | |
| train_path = os.path.join(os.path.dirname(dl_files["train_src_ori"]), _TRAINING_FILE) | |
| valid_path = os.path.join(os.path.dirname(dl_files["train_src_ori"]), _DEV_FILE) | |
| test_path = os.path.join(os.path.dirname(dl_files["train_src_ori"]), _TEST_FILE) | |
| if self.config.name == "original": | |
| train_src_path = os.path.abspath(dl_files["train_src_ori"]) | |
| train_dst_path = os.path.abspath(dl_files["train_dst_ori"]) | |
| valid_src_path = os.path.abspath(dl_files["valid_src_ori"]) | |
| valid_dst_path = os.path.abspath(dl_files["valid_dst_ori"]) | |
| test_src_path = os.path.abspath(dl_files["test_src_ori"]) | |
| test_dst_path = os.path.abspath(dl_files["test_dst_ori"]) | |
| elif self.config.name == "ner_tagged": | |
| train_src_path = os.path.abspath(dl_files["train_src_ner"]) | |
| train_dst_path = os.path.abspath(dl_files["train_dst_ner"]) | |
| valid_src_path = os.path.abspath(dl_files["valid_src_ner"]) | |
| valid_dst_path = os.path.abspath(dl_files["valid_dst_ner"]) | |
| test_src_path = os.path.abspath(dl_files["test_src_ner"]) | |
| test_dst_path = os.path.abspath(dl_files["test_dst_ner"]) | |
| else: | |
| raise FileNotFoundError | |
| with open(train_src_path, encoding="utf-8") as train_src, open(train_dst_path, encoding="utf-8") as train_dst, open(train_path, "w", encoding="utf-8") as train_csv, \ | |
| open(valid_src_path, encoding="utf-8") as valid_src, open(valid_dst_path, encoding="utf-8") as valid_dst, open(valid_path, "w", encoding="utf-8") as valid_csv, \ | |
| open(test_src_path, encoding="utf-8") as test_src, open(test_dst_path, encoding="utf-8") as test_dst, open(test_path, "w", encoding="utf-8") as test_csv: | |
| field_names = ["complex", "simple"] | |
| train_writer = csv.DictWriter(train_csv, fieldnames=field_names) | |
| valid_writer = csv.DictWriter(valid_csv, fieldnames=field_names) | |
| test_writer = csv.DictWriter(test_csv, fieldnames=field_names) | |
| train_writer.writeheader() | |
| valid_writer.writeheader() | |
| test_writer.writeheader() | |
| for src, dst in zip(train_src.readlines(), train_dst.readlines()): | |
| train_writer.writerow({"complex": src.strip(), "simple": dst.strip()}) | |
| for src, dst in zip(valid_src.readlines(), valid_dst.readlines()): | |
| valid_writer.writerow({"complex": src.strip(), "simple": dst.strip()}) | |
| for src, dst in zip(test_src.readlines(), test_dst.readlines()): | |
| test_writer.writerow({"complex": src.strip(), "simple": dst.strip()}) | |
| data_files = { | |
| "train": train_path, | |
| "valid": valid_path, | |
| "test": test_path, | |
| } | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["valid"]}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """This function returns the examples in the raw (text) form.""" | |
| with open(filepath, encoding="utf-8") as f: | |
| guid = 0 | |
| reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_MINIMAL) | |
| for row in reader: | |
| yield guid, { | |
| "complex": row["complex"], | |
| "simple": row["simple"] | |
| } | |
| guid += 1 | |