| """Wikipedia snippets in parquet format""" |
| import math |
| import pyarrow.parquet as pq |
| import datasets |
|
|
| logger = datasets.logging.get_logger(__name__) |
| _CITATION = """\ |
| @ONLINE {wikidump, |
| author = {Wikimedia Foundation}, |
| title = {Wikimedia Downloads}, |
| url = {https://dumps.wikimedia.org} |
| } |
| """ |
| _DESCRIPTION = """\ |
| The dataset was built from the Wikipedia dump (https://dumps.wikimedia.org/). |
| Each example contains the content of one full Wikipedia article with cleaning to strip |
| markdown and unwanted sections (references, etc.). |
| """ |
| _LICENSE = ( |
| "This work is licensed under the Creative Commons Attribution-ShareAlike " |
| "3.0 Unported License. To view a copy of this license, visit " |
| "http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to " |
| "Creative Commons, PO Box 1866, Mountain View, CA 94042, USA." |
| ) |
|
|
|
|
| class WikipediaSnippetsStreamed(datasets.GeneratorBasedBuilder): |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "wiki_id": datasets.Value("string"), |
| "start_paragraph": datasets.Value("int32"), |
| "start_character": datasets.Value("int32"), |
| "end_paragraph": datasets.Value("int32"), |
| "end_character": datasets.Value("int32"), |
| "article_title": datasets.Value("string"), |
| "section_title": datasets.Value("string"), |
| "passage_text": datasets.Value("string"), |
| } |
| ), |
| supervised_keys=None, |
| homepage="https://dumps.wikimedia.org", |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| url = "https://storage.googleapis.com/huggingface-nlp/cache/datasets/wiki40b/en/1.1.0/wiki40b-train.parquet" |
| downloaded_file = dl_manager.download(url) |
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file}), |
| ] |
|
|
| def wiki40b_article_snippets(self, article, passage_len=100, overlap=0): |
| paragraphs = article["text"].split("\n") |
| aticle_idx = paragraphs.index("_START_ARTICLE_") + 1 |
| article_title = paragraphs[aticle_idx] if aticle_idx < len(paragraphs) else "" |
| section_indices = [i + 1 for i, par in enumerate(paragraphs[:-1]) if par == "_START_SECTION_"] |
| par_tabs = [par.split(" ") for par in paragraphs] |
| word_map = [ |
| (i, len(" ".join(par[:j])), w) |
| for i, par in enumerate(par_tabs) |
| if not par[0].startswith("_START_") |
| for j, w in enumerate(par) |
| if i > 0 |
| ] |
| step_size = passage_len - overlap |
| passages = [] |
| for i in range(math.ceil(len(word_map) / step_size)): |
| pre_toks = word_map[i * step_size: i * step_size + passage_len] |
| start_section_id = max([0] + [j for j in section_indices if j <= pre_toks[0][0]]) |
| section_ids = [j for j in section_indices if j >= start_section_id and j <= pre_toks[-1][0]] |
| section_ids = section_ids if len(section_ids) > 0 else [0] |
| passage_text = " ".join([w for p_id, s_id, w in pre_toks]) |
| passages += [ |
| { |
| "article_title": article_title, |
| "section_title": " & ".join([paragraphs[j] for j in section_ids]), |
| "wiki_id": article["wikidata_id"], |
| "start_paragraph": pre_toks[0][0], |
| "start_character": pre_toks[0][1], |
| "end_paragraph": pre_toks[-1][0], |
| "end_character": pre_toks[-1][1] + len(pre_toks[-1][2]) + 1, |
| "passage_text": passage_text.replace("_NEWLINE_", "\n"), |
| } |
| ] |
| return passages |
|
|
| def _generate_examples(self, filepath): |
| logger.info("generating examples from = %s", filepath) |
| passage_counter = 0 |
| with open(filepath, "rb") as f: |
| pf = pq.ParquetFile(f) |
| for i in range(pf.num_row_groups): |
| batch_table_dict = pf.read_row_group(i).to_pydict() |
| for idx, article_text in enumerate(batch_table_dict["text"]): |
| article = {"text": article_text, "wikidata_id": batch_table_dict["wikidata_id"][idx]} |
| passages = self.wiki40b_article_snippets(article) |
| for passage in passages: |
| yield ++passage_counter, passage |
|
|
