# coding=utf-8 # Copyright 2020 Facebook, Inc. and the HuggingFace Datasets Authors. # # 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 """ELI5: Long Form Question Answering dataset""" from __future__ import absolute_import, division, print_function import bz2 import io import json import lzma import os import re from os.path import isfile from os.path import join as pjoin from time import time import datasets logger = datasets.logging.get_logger(__name__) _SUB_REDDITS = ["explainlikeimfive", "askscience", "AskHistorians"] _REDDIT_URL = "https://files.pushshift.io/reddit/" # pylint: disable=line-too-long _URL_REGEX = r"""(?i)\b((?:https?:(?:/{1,3}|[a-z0-9%])|[a-z0-9.\-]+[.](?:com|net|org|edu|gov|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|name|post|pro|tel|travel|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|ee|eg|eh|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mm|mn|mo|mp|mq|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|om|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|py|qa|re|ro|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|Ja|sk|sl|sm|sn|so|sr|ss|st|su|sv|sx|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw)/)(?:[^\s()<>{}\[\]]+|\([^\s()]*?\([^\s()]+\)[^\s()]*?\)|\([^\s]+?\))+(?:\([^\s()]*?\([^\s()]+\)[^\s()]*?\)|\([^\s]+?\)|[^\s`!()\[\]{};:'".,<>?«»“”‘’])|(?:(? "), ("<", " < "), ] # removes URLs (kept in separate list) def _extract_urls_from_text(stp): url_list = list(set(re.findall(_URL_REGEX, stp))) for i, url in enumerate(url_list): stp = stp.replace(url, "_URL_%d_" % (i,)) for a, b in _HTML_PAIRS: stp = stp.replace(a, b) return (stp, url_list) # collects URLs for monthly dumps, has to be robust to file type changes def _gather_dump_urls(base_url, mode, dl_manager): from bs4 import BeautifulSoup page_path = dl_manager.download(_REDDIT_URL + mode) page_f = open(page_path, encoding="utf-8") page_content = page_f.read() page_f.close() soup = BeautifulSoup(page_content, "lxml") files = [it for it in soup.find_all(attrs={"class": "file"})] f_urls = [ tg.find_all(lambda x: x.has_attr("href"))[0]["href"] for tg in files if len(tg.find_all(lambda x: x.has_attr("href"))) > 0 ] date_to_url = {} for url_st in f_urls: ls = re.findall(r"20[0-9]{2}-[0-9]{2}", url_st) if len(ls) > 0: yr, mt = ls[0].split("-") date_to_url[(int(yr), int(mt))] = base_url + mode + url_st[1:] return date_to_url # select valid top-level comments def _valid_line(dct, mode): top_level = (mode == "submissions") or ( len(dct["body"].split()) > 2 and not dct["body"].startswith("Your submission has been removed") and dct["author"] != "AutoModerator" and dct["parent_id"] == dct["link_id"] ) res = dct.get("num_comments", 1) > 0 and dct.get("score", 0) and dct.get("score", 0) >= 2 and top_level return res def _open_compressed_file(f_name, f_type): import zstandard as zstd fh = None if f_type == "xz": f = lzma.open(f_name, "rt") elif f_type == "bz2": f = bz2.open(f_name, "rt") elif f_type == "zst": fh = open(f_name, "rb") dctx = zstd.ZstdDecompressor() stream_reader = dctx.stream_reader(fh) f = io.TextIOWrapper(stream_reader, encoding="utf-8") else: raise NotImplementedError return f, fh # download a file, extract posts from desired subreddit, then remove from disk def _download_and_select_lines(dl_manager, f_url, mode, st_time): # download and pre-process original posts print("downloading {} {:.2f}".format(f_url, time() - st_time)) f_downloaded_path = dl_manager.download(f_url) print("decompressing and filtering {} {:.2f}".format(f_url, time() - st_time)) f, fh = _open_compressed_file(f_downloaded_path, f_url.split(".")[-1]) lines = dict([(name, []) for name in _SUB_REDDITS]) for line in f: line_dct = json.loads(line) if any([line_dct.get("subreddit", "") == name for name in _SUB_REDDITS]): lines[line_dct["subreddit"]] += [line_dct] f.close() if f_url.split(".")[-1] == "zst": fh.close() os.remove(f_downloaded_path) os.remove(f_downloaded_path + ".json") os.remove(f_downloaded_path + ".lock") print("tokenizing and selecting {} {:.2f}".format(f_url, time() - st_time)) processed_items = dict([(name, []) for name in _SUB_REDDITS]) if mode == "submissions": key_list = ["id", "score", "url", "title", "selftext", "subreddit"] else: key_list = ["id", "link_id", "parent_id", "score", "body"] for name in _SUB_REDDITS: for line in lines[name]: if _valid_line(line, mode): reddit_res = {} for k in key_list: if k in ["title", "selftext", "body"]: reddit_res[k] = _extract_urls_from_text(line[k]) else: reddit_res[k] = line[k] processed_items[name] += [reddit_res] print("Total found {} {} {:.2f}".format(sum([len(ls) for ls in processed_items.values()]), mode, time() - st_time)) return processed_items # post-process ELI5 questions and de-duplicate answers def _post_process(reddit_dct, name=""): # remove the ELI5 at the start of explainlikeimfive questions start_re = re.compile(r"""\A[\[|\(]?[ ]?eli[5f][ ]?[\]|\)]?[]?[:,]?""", re.IGNORECASE) if name == "explainlikeimfive": title, uls = reddit_dct["title"] title = start_re.sub("", title.strip()).strip() reddit_dct["title"] = [title, uls] # dedupe and filter comments comments = [ c for i, c in enumerate(reddit_dct["comments"]) if len(c["body"][0].split()) >= 8 and c["id"] not in [x["id"] for x in reddit_dct["comments"][:i]] ] comments = sorted(comments, key=lambda c: (c["score"], len(c["body"][0].split()), c["id"]), reverse=True) reddit_dct["comments"] = comments return reddit_dct def _download_and_filter_reddit(dl_manager, start_year=2011, start_month=7, end_year=2019, end_month=7): # collect submissions and comments monthly URLs date_to_url_submissions = _gather_dump_urls(_REDDIT_URL, "submissions", dl_manager) date_to_url_comments = _gather_dump_urls(_REDDIT_URL, "comments", dl_manager) # download, filter, process, remove st_time = time() qa_dict = dict([(name, {}) for name in _SUB_REDDITS]) # first download all questions for year in range(start_year, end_year + 1): start_mth = start_month if year == start_year else 1 end_mth = end_month if year == end_year else 12 months = range(start_mth, end_mth + 1) for month in months: if (year, month) in date_to_url_submissions: f_url = date_to_url_submissions[(year, month)] processed_submissions = _download_and_select_lines(dl_manager, f_url, "submissions", st_time) for name in _SUB_REDDITS: for dct in processed_submissions[name]: qa_dict[name][dct["id"]] = dct else: print("Could not find submissions dump file for year {:4d} month {:2d}".format(year, month)) # then all answers for year in range(start_year, end_year + 1): start_mth = start_month if year == start_year else 1 end_mth = end_month if year == end_year else 12 months = range(start_mth, end_mth + 1) for month in months: if (year, month) in date_to_url_comments: f_url = date_to_url_comments[(year, month)] processed_comments = _download_and_select_lines(dl_manager, f_url, "comments", st_time) # merge submissions and comments for name in _SUB_REDDITS: merged_comments = 0 for dct in processed_comments[name]: did = dct["parent_id"].split("_")[-1] if did in qa_dict[name]: merged_comments += 1 qa_dict[name][did]["comments"] = qa_dict[name][did].get("comments", []) + [dct] else: print("Could not find comments dump file for year {:4d} month {:2d}".format(year, month)) # then post-process res = {} for name in _SUB_REDDITS: qa_dct_list = [(k, _post_process(rdct, name)) for k, rdct in qa_dict[name].items() if "comments" in rdct] qa_dct_list = [x for x in qa_dct_list if len(x[1]["comments"]) > 0 and name in x[1]["url"]] res[name] = dict(qa_dct_list[:]) return res _DESCRIPTION = """\ Explain Like I'm 5 long form QA dataset """ _CITATION = """\ @inproceedings{DBLP:conf/acl/FanJPGWA19, author = {Angela Fan and Yacine Jernite and Ethan Perez and David Grangier and Jason Weston and Michael Auli}, editor = {Anna Korhonen and David R. Traum and Lluis Marquez}, title = {{ELI5:} Long Form Question Answering}, booktitle = {Proceedings of the 57th Conference of the Association for Computational Linguistics, {ACL} 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers}, pages = {3558--3567}, publisher = {Association for Computational Linguistics}, year = {2019}, url = {https://doi.org/10.18653/v1/p19-1346}, doi = {10.18653/v1/p19-1346}, } """ class Eli5Config(datasets.BuilderConfig): """BuilderConfig for ExplainLikeImFive.""" def __init__(self, **kwargs): """BuilderConfig for ExplainLikeImFive. Args: **kwargs: keyword arguments forwarded to super. """ super(Eli5Config, self).__init__(**kwargs) class Eli5(datasets.GeneratorBasedBuilder): """ELI5: Explain Like I'm Five long form question answering dataset.""" BUILDER_CONFIG_CLASS = Eli5Config _DATA_SPLIT_URL = "https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/eli5/reddit_data_split.json" BUILDER_CONFIGS = [ Eli5Config(name="LFQA_reddit", version=datasets.Version("1.0.0"), description="long from QA subreddits"), ] test_dummy_data = False def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "q_id": datasets.Value("string"), "title": datasets.Value("string"), "selftext": datasets.Value("string"), "document": datasets.Value("string"), "subreddit": datasets.Value("string"), "answers": datasets.features.Sequence( { "a_id": datasets.Value("string"), "text": datasets.Value("string"), "score": datasets.Value("int32"), } ), "title_urls": datasets.features.Sequence(datasets.Value("string")), "selftext_urls": datasets.features.Sequence(datasets.Value("string")), "answers_urls": datasets.features.Sequence(datasets.Value("string")), } ), supervised_keys=None, homepage="https://facebookresearch.github.io/ELI5/explore.html", citation=_CITATION, ) def _split_generators(self, dl_manager): qa_data_file = pjoin( self._cache_dir_root, self._relative_data_dir(with_version=False), "reddit_downloaded_qa_lists.json" ) if isfile(qa_data_file): logger.info("loading pre-computed QA list") self.filtered_reddit = json.load(open(qa_data_file)) else: self.filtered_reddit = _download_and_filter_reddit( dl_manager, start_year=2011, start_month=7, end_year=2019, end_month=7 ) logger.info("saving pre-computed QA list") json.dump(self.filtered_reddit, open(qa_data_file, "w")) # download data splits from AWS fpath_splits = dl_manager.download(self._DATA_SPLIT_URL) self.data_split = json.load(open(fpath_splits)) return [ datasets.SplitGenerator( name=datasets.Split("train_eli5"), gen_kwargs={"split": "train", "subreddit_name": "explainlikeimfive"}, ), datasets.SplitGenerator( name=datasets.Split("validation_eli5"), gen_kwargs={"split": "validation", "subreddit_name": "explainlikeimfive"}, ), datasets.SplitGenerator( name=datasets.Split("test_eli5"), gen_kwargs={"split": "test", "subreddit_name": "explainlikeimfive"}, ), datasets.SplitGenerator( name=datasets.Split("train_asks"), gen_kwargs={"split": "train", "subreddit_name": "askscience"}, ), datasets.SplitGenerator( name=datasets.Split("validation_asks"), gen_kwargs={"split": "validation", "subreddit_name": "askscience"}, ), datasets.SplitGenerator( name=datasets.Split("test_asks"), gen_kwargs={"split": "test", "subreddit_name": "askscience"}, ), datasets.SplitGenerator( name=datasets.Split("train_askh"), gen_kwargs={"split": "train", "subreddit_name": "AskHistorians"}, ), datasets.SplitGenerator( name=datasets.Split("validation_askh"), gen_kwargs={"split": "validation", "subreddit_name": "AskHistorians"}, ), datasets.SplitGenerator( name=datasets.Split("test_askh"), gen_kwargs={"split": "test", "subreddit_name": "AskHistorians"}, ), ] def _generate_examples(self, split, subreddit_name): logger.info("generating examples from = {}, {} set".format(subreddit_name, split)) if split in self.data_split.get(subreddit_name, []): id_list = self.data_split[subreddit_name][split] data = [ self.filtered_reddit[subreddit_name][q_id] for q_id in id_list if q_id in self.filtered_reddit[subreddit_name] ] elif split == "train": data = [ self.filtered_reddit[subreddit_name][q_id] for subreddit_name in self.filtered_reddit for q_id in self.filtered_reddit[subreddit_name] ] else: data = [] for example in data: id_ = example["id"] title = example["title"][0] title_urls = example["title"][1] selftext = example["selftext"][0] selftext_urls = example["selftext"][1] answer_scores = [ans["score"] for ans in example["comments"]] answer_ids = [ans["id"] for ans in example["comments"]] # flatten list of URL mappings url_maps = [(ul, i, j) for i, ans in enumerate(example["comments"]) for j, ul in enumerate(ans["body"][1])] answers_urls = [ul for ul, _, _ in url_maps] map_url_indices = dict([((i, j), k) for k, (_, i, j) in enumerate(url_maps)]) answer_texts = [] for i, ans in enumerate(example["comments"]): txt = ans["body"][0] for j, _ in enumerate(ans["body"][1]): txt = txt.replace("_URL_{}_".format(j), "_URL_{}_".format(map_url_indices[(i, j)])) answer_texts += [txt.strip()] yield id_, { "q_id": id_, "title": title, "selftext": selftext, "document": "", "subreddit": example.get("subreddit", subreddit_name), "answers": {"a_id": answer_ids, "text": answer_texts, "score": answer_scores}, "title_urls": title_urls, "selftext_urls": selftext_urls, "answers_urls": answers_urls, }