# 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""" 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 from datasets.exceptions import DefunctDatasetError 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 logger.info(f"downloading {f_url} {time() - st_time:.2f}") f_downloaded_path = dl_manager.download(f_url) logger.info(f"decompressing and filtering {f_url} {time() - st_time:.2f}") 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") logger.info("tokenizing and selecting {f_url} {time() - st_time:.2f}") 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] logger.info(f"Total found {sum([len(ls) for ls in processed_items.values()])} {mode} {time() - st_time:.2f}") 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: logger.info(f"Could not find submissions dump file for year {year:4d} month {month:2d}") # 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: logger.info(f"Could not find comments dump file for year {year:4d} month {month:2d}") # 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): raise DefunctDatasetError( "Dataset 'eli5' is defunct and no longer accessible due to unavailability of the source data" ) 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(f"generating examples from = {subreddit_name}, {split} set") 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(f"_URL_{j}_", f"_URL_{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, }