# Copyright 2020 The HuggingFace Datasets Authors and # the Johns Hopkins University (JHU) Human Language Technology # Center of Excellence. # # 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. """ This file provides a HuggingFace dataset loader implementation for the JHU/HLTCOE MegaWika dataset. MegaWika is a multi- and crosslingual text dataset containing 30 million Wikipedia passages with their scraped and cleaned web citations. The passages span 50 Wikipedias in 50 languages, and the articles in which the passages were originally embedded are included for convenience. Where a Wikipedia passage is in a non-English language, an automated English translation is provided. Furthermore, nearly 130 million English question/answer pairs were extracted from the passages, and FrameNet events occurring in the passages are detected using the LOME FrameNet parser. """ import csv import json import os import re import pathlib from pathlib import Path import yaml from ast import literal_eval import datasets # import gzip # try: # import lzma as xz # except ImportError: # import pylzma as xz # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @article{barham2023megawika, title={MegaWika: Millions of reports and their sources across 50 diverse languages}, author={Barham, Samuel and Weller, Orion and Yuan, Michelle and Murray, Kenton and Yarmohammadi, Mahsa and Jiang, Zhengping and Vashishtha, Siddharth and Martin, Alexander and Liu, Anqi and White, Aaron Steven and Boyd-Graber, Jordan and Van Durme, Benjamin }, journal={INSERT ARXIV PREPRINT ID HERE}, year={2023} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ MegaWika is a multi- and crosslingual text dataset containing 30 million Wikipedia passages with their scraped and cleaned web citations. The passages span 50 Wikipedias in 50 languages, and the articles in which the passages were originally embedded are included for convenience. Where a Wikipedia passage is in a non-English language, an automated English translation is provided. Furthermore, nearly 130 million English question/answer pairs were extracted from the passages, and FrameNet events occurring in the passages are detected using the LOME FrameNet parser. """ _HOMEPAGE = "https://huggingface.co/datasets/hltcoe/megawika" _LICENSE = "cc-by-sa-4.0" _URL = "https://huggingface.co/datasets/hltcoe/megawika" # Load the file paths for all the splits (per language currently) file_list_url = "https://huggingface.co/datasets/hltcoe/megawika/raw/main/files.yml" import urllib.request with urllib.request.urlopen(file_list_url) as f: try: fnames = yaml.safe_load(f) except yaml.YAMLError as exc: print("Error loading the file paths for the dataset splits. Aborting.") exit(1) _DATA_URL = fnames['fnames'] _VARIANTS = ["default"] + list(_DATA_URL.keys()) class MegaWika(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "article_title": datasets.Value("string"), "article_text": datasets.Value("string"), "entries": datasets.features.Sequence( { "id": datasets.Value("string"), # Wiki passage "passage": { "text": [datasets.Value("string")], "parse": datasets.Value("string"), "en_tokens": [datasets.Value("string")], "lang_tokens": [datasets.Value("string")], "en_lang_token_map": [[datasets.Value("int32")]] # list of pairs }, # MT "mt": { "original": datasets.Value("string"), "original_sents": [datasets.Value("string")], "translation": datasets.Value("string"), "translation_sents": [datasets.Value("string")], "translation_probs": [[datasets.Value("float32")]], "repetitious_translation": datasets.Value("bool") }, # Source document "source_lang": datasets.Value("string"), "source_url": datasets.Value("string"), "source_text": datasets.Value("string"), # Question/answer pairs "qa_pairs": datasets.Sequence( { "question": datasets.Value("string"), "en_answer": datasets.Value("string"), "lang_answer": datasets.Value("string"), "frames": datasets.Sequence( { "frame": datasets.Value("string"), "argument": datasets.Value("string") } ), "en_matches_in_source": [[datasets.Value("int32")]], # list of pair of int indices "en_match_in_passage": [datasets.Value("int32")], # pair of int indices "lang_matches_in_source": [[datasets.Value("int32")]], # list of pair of int indices "lang_match_in_passage": [datasets.Value("int32")], # pair of int indices "passage": [datasets.Value("string")], "en_answer_tokens": [datasets.Value("string")], "match_disambiguated_question": datasets.Value("string"), } ) } ) } ), supervised_keys=None, homepage=_URL, citation=_CITATION, ) def _split_generators(self, dl_manager): if self.config.name == "default": data_sources = _DATA_URL else: data_sources = {self.config.name: _DATA_URL[self.config.name]} return [ datasets.SplitGenerator( name=lang, gen_kwargs={ "filepaths": dl_manager.download(data_sources[lang]) } ) for lang in data_sources ] def _get_qa_pair_list_features(self, qa_pair, feature_name): res = [] if feature_name in qa_pair: if qa_pair[feature_name]: return qa_pair[feature_name] else: if feature_name.startswith('en'): feature_name = '_'.join(feature_name.split('_')[1:]) return self._get_qa_pair_list_features(qa_pair, feature_name) return res def _generate_examples(self, filepaths): """This function returns the examples in the raw (text) form by iterating on all the files.""" id_ = 0 for filepath in filepaths: # logger.info("Generating examples from = %s", filepath) try: with open(filepath, "r", encoding="utf-8") as f: for line in f: if line: example = json.loads(line) if example is not None and isinstance(example, dict): yield id_, { "article_title": example.get("article_title", ""), "article_text": example.get("article_text", ""), "entries": [ { "id": entry.get("id", "").lower(), "passage": { "text": entry['passage'].get("text", []), "parse": json.dumps(entry['passage'].get("parse", [{}])), "en_tokens": list(entry['passage'].get( "en_tokens", { token: token for tokens in entry['passage'].get("tokens", {}) for token in tokens } ).values()), "lang_tokens": list(entry['passage'].get("lang_tokens", {}).values()), "en_lang_token_map": [ (int(item[0]), int(item[1])) for item in entry['passage'].get("en_lang_token_map", {}).items() ] }, "mt": { "original": entry.get("original", ""), "original_sents": entry.get("original_sents", []), "translation": entry.get("translation", ""), "translation_sents": entry.get("translation_sents", []), "translation_probs": entry.get("translation_probs", [[]]), "repetitious_translation": entry.get("repetitious_translation", None) }, "source_lang": entry.get("source_lang", ""), "source_url": entry.get("source_url", ""), "source_text": entry.get("source_text", ""), "qa_pairs": [ { "question": qa_pair.get('question', ""), "en_answer": qa_pair.get('en_answer', qa_pair.get('answer', "")), 'lang_answer': qa_pair.get('lang_answer', ''), 'frames': qa_pair.get('frames', []), "en_matches_in_source": self._get_qa_pair_list_features(qa_pair, "en_matches_in_source"), "en_match_in_passage": self._get_qa_pair_list_features(qa_pair, "en_match_in_passage"), "lang_matches_in_source": self._get_qa_pair_list_features(qa_pair, "lang_matches_in_source"), "lang_match_in_passage": self._get_qa_pair_list_features(qa_pair, "lang_match_in_passage"), "passage": qa_pair.get('passage', []), "en_answer_tokens": qa_pair.get('en_answer_tokens', qa_pair.get('answer_tokens', [])), "match_disambiguated_question": qa_pair.get('match_disambiguated_question', ""), } for qa_pair in entry.get('qa_pairs', []) ] } for entry in example.get("entries", []) ] } id_ += 1 except: print("Error reading file:", filepath) # "entries": datasets.features.Sequence( # { # "qa_pairs": datasets.Sequence( # { # "question": datasets.Value("string"), # "answer": datasets.Value("string"), # } # ) # }