# coding=utf-8 # Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. # Copyright 2021 Phonetics and Speech Laboratory, Trinity College, Dublin # # 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. # Lint as: python3 import os from pathlib import Path import datasets from bs4 import BeautifulSoup import requests _DESCRIPTION = """\ Foinse was an Irish-language magazine site. This script uses a list of articles retrieved from the Wayback Machine to build a corpus """ _DATA_URL = "https://huggingface.co/datasets/jimregan/foinse/raw/main/urls.txt" class FoinseDataset(datasets.GeneratorBasedBuilder): """Scraper dataset for Foinse.""" VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="documents", version=VERSION, description="Plain text portion of the corpus: whole documents"), datasets.BuilderConfig(name="paragraphs", version=VERSION, description="Plain text portion of the corpus: paragraphs"), ] def _info(self): features = datasets.Features( { "title": datasets.Value("string"), "url": datasets.Value("string"), "author": datasets.Value("string"), "date_text": datasets.Value("string"), "text": datasets.Value("string"), "category": datasets.Value("string"), "subcategory": datasets.Value("string"), "summary": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" dl_path = dl_manager.download(_DATA_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "split": "train", "data_file": dl_path }, ), ] def _generate_examples( self, split, data_file ): """ Yields examples as (key, example) tuples. """ links = _get_links(data_file) _id = 1 for url in links: content = get_content(url) paras = content.get("text", []) if self.config.name == "documents": paras = ['\n'.join(paras)] for para in paras: yield _id, { "title": content.get("title", ""), "url": url, "author": content.get("author", ""), "date_text": content.get("published", ""), "category": content.get("category", ""), "subcategory": content.get("subcategory", ""), "summary": content.get("summary", ""), "text": para } _id += 1 def get_content(url): out = {} page = requests.get(url) if page.status_code != 200: return {} page_content = page.text soup = BeautifulSoup(page_content, "lxml") content = soup.find("div", {"class": "item-page"}) if not content: content = soup.find("div", {"id": "ja-main"}) if not content: return {} breadcrumbs = soup.find("div", {"class": "ja-breadcrums"}) if breadcrumbs: here = breadcrumbs.find("a", {"class": "pathway"}) if not here: here = breadcrumbs.find("span", {"class": "pathway"}) if here: out["category"] = here.text.strip() # junk jc = content.find("div", {"id": "jc"}) if jc: jc.extract() pagenav = content.find("ul", {"class": "pagenav"}) if pagenav: pagenav.extract() for js in content.find_all("script", {"type": "text/javascript"}): js.extract() h2 = content.find("h2") if h2: title = h2.text.strip() if title: out["title"] = title h2.extract() h1 = content.find("h1") if h1: heading = h1.text.strip() if heading: out["subcategory"] = heading h1.extract() published_tag = content.find("dd", {"class": "published"}) if not published_tag: published_tag = content.find("span", {"class": "createdate"}) if published_tag: out["published"] = published_tag.text.strip() author_tag = content.find("dd", {"class": "createdby"}) if not author_tag: author_tag = content.find("span", {"class": "createby"}) if author_tag: out["author"] = author_tag.text.strip() artinfo = content.find("dl", {"class": "article-info"}) if not artinfo: artinfo = content.find("div", {"class": "article-meta"}) if artinfo: artinfo.extract() paragraphs_tags = content.find_all("p") paragraphs = [p.text.replace("\xa0", " ").strip() for p in paragraphs_tags] out["text"] = paragraphs raw_text = content.text raw_out = [] for raw_line in raw_text.split("\n"): line = raw_line.replace("\xa0", " ").strip() if line == "": continue raw_out.append(line) if paragraphs != raw_out: out["text"] = raw_out summary = extract_summary(out["text"]) if summary: out["summary"] = summary out["text"] = filter_para_list(out["text"]) vocab_list = [] for vocab in content.find_all("a", {"class": "glossarylink"}): item = {} item["en"] = vocab.get("title").strip() item["ga"] = vocab.text.strip() vocab_list.append(item) out["vocab"] = vocab_list return out def extract_summary(inlist): if len(inlist) > 2: if inlist[-2] == "Did you understand this story? Here are the main points:": return inlist[-1] return "" def filter_para_list(inlist): out = [] for para in inlist: if para == "": continue elif para.strip() == "Foinse - News as Gaeilge": return out elif para.strip() == "Did you understand this story? Here are the main points:": return out else: out.append(para) return out def _get_links(scrape): links = set() if not os.path.exists(scrape): raise Exception(f"File {scrape} does not exist") with open(scrape) as f: for url in f.readlines(): links.add(url.rstrip()) return list(links)