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