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