Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
English
Size:
100K - 1M
License:
Download fetch_stack_exchange.py from hoskinson-center/proof-pile: direct link, hf CLI and curl.
- Browser
- Download file 7.89 kB
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https://huggingface.co/datasets/hoskinson-center/proof-pile/resolve/36a88ace1807d855e79a09118418a5bf696a265b/fetch_stack_exchange.py
- Command line
-
hf download hf://datasets/hoskinson-center/proof-pile@36a88ace1807d855e79a09118418a5bf696a265b/fetch_stack_exchange.py
-
curl -L -o fetch_stack_exchange.py https://huggingface.co/datasets/hoskinson-center/proof-pile/resolve/36a88ace1807d855e79a09118418a5bf696a265b/fetch_stack_exchange.py
7.89 kB
| from dataclasses import dataclass, field, fields | |
| from functools import lru_cache | |
| from xml.etree import ElementTree | |
| from datetime import datetime | |
| from enum import Enum | |
| import typing | |
| from typing import List, Optional, Union | |
| import os.path | |
| from itertools import groupby | |
| import dataclasses | |
| from tqdm import tqdm | |
| from bs4 import BeautifulSoup | |
| import sys | |
| from pathlib import Path | |
| import tarfile | |
| import random | |
| import ndjson | |
| import json | |
| from utils import make_archive | |
| """ | |
| Author: E.W.Ayers | |
| This code takes a dump of math overflow XML and produces | |
| a structured set of questions with answers. | |
| 1. Get mathoverflow.net.7z file | |
| 2. Extract this to `DATA_DIR = 'data/mathoverflow.net'` | |
| 3. Run `questions()` and run it to get a dictionary of mathoverflow questions. | |
| Each question has an `Answers` field that contains a list of answers for the given q. | |
| """ | |
| def batch_loader(seq, size): | |
| """ | |
| Iterator that takes in a list `seq` and returns | |
| chunks of size `size` | |
| """ | |
| return [seq[pos : pos + size] for pos in range(0, len(seq), size)] | |
| DOC_SEP = "<|endoftext|>" | |
| # source: https://meta.stackexchange.com/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede | |
| class PostType(Enum): | |
| Question = 1 | |
| Answer = 2 | |
| OrphanedTagWiki = 3 | |
| TagWikiExcerpt = 4 | |
| TagWiki = 5 | |
| ModeratorNomination = 6 | |
| WikiPlaceholder = 7 | |
| PrivilegeWiki = 8 | |
| def is_optional(field): | |
| return typing.get_origin(field) is Union and type(None) in typing.get_args(field) | |
| def fromXML(cls, element): | |
| out = {} | |
| for field in fields(cls): | |
| field_key = field.name | |
| field_type = field.type | |
| f = field.metadata.get("from_xml") | |
| if f == "skip": | |
| continue | |
| attr_key = f["key"] if (f is not None and f["key"] is not None) else field_key | |
| v = element.attrib.get(attr_key) | |
| if v is None: | |
| if field.default is not dataclasses.MISSING: | |
| out[field_key] = field.default | |
| elif field.default_factory is not dataclasses.MISSING: | |
| out[field_key] = field.default_factory() # type: ignore | |
| elif is_optional(field_type): | |
| out[field_key] = None | |
| else: | |
| raise Exception(f"Missing field {attr_key}") | |
| continue | |
| if is_optional(field_type): | |
| field_type = typing.get_args(field_type)[0] | |
| if f is not None and f["fn"] is not None: | |
| out[field_key] = f["fn"](v) | |
| elif field_type is int: | |
| out[field_key] = int(v) | |
| elif field_type is str: | |
| out[field_key] = str(v) | |
| elif field_type is datetime: | |
| out[field_key] = datetime.fromisoformat(v) | |
| else: | |
| raise Exception(f"Don't know how to decode {field_type}") | |
| return cls(**out) | |
| def use(fn, key=None): | |
| return field(metadata={"from_xml": {"fn": fn, "key": key}}) | |
| def skip(default): | |
| return field(default=default, metadata={"from_xml": "skip"}) | |
| def iter_rows(path): | |
| for [_, element] in ElementTree.iterparse(path, events=["start"]): | |
| if element.tag == "row": | |
| yield element | |
| DATA_DIR = "nothing" | |
| class Comment: | |
| Id: int | |
| PostId: int | |
| Score: int | |
| Text: str | |
| CreationDate: datetime | |
| UserId: Optional[int] | |
| # lru_cache() | |
| def comments(): | |
| path = os.path.join(DATA_DIR, "Comments.xml") | |
| out = {} | |
| for element in iter_rows(path): | |
| x: Comment = fromXML(Comment, element) | |
| out[x.Id] = x | |
| print(f"Processed {len(out)} comments.") | |
| return out | |
| class Post: | |
| Id: int | |
| CreationDate: datetime | |
| DeletionDate: Optional[datetime] | |
| Score: int | |
| Body: str # in html; need to parse out? | |
| Title: Optional[str] | |
| OwnerUserId: Optional[int] | |
| ViewCount: Optional[int] | |
| AcceptedAnswerId: Optional[int] | |
| ParentId: Optional[int] | |
| PostType: "PostType" = use(lambda x: PostType(int(x)), "PostTypeId") | |
| Comments: List[Comment] = skip(None) | |
| Answers: Optional[List["Post"]] = skip(None) | |
| Tags: str = field(default="") | |
| # @lru_cache() | |
| def questions(): | |
| path = os.path.join(DATA_DIR, "Posts.xml") | |
| cs = {} | |
| for k, c in groupby(comments().values(), lambda c: c.PostId): | |
| x = list(c) | |
| x.sort(key=lambda x: -x.Score) | |
| cs[k] = x | |
| qs = {} | |
| answers = {} | |
| for element in iter_rows(path): | |
| post = fromXML(Post, element) | |
| post.Comments = cs.get(post.Id, []) | |
| if post.PostType is PostType.Question: | |
| post.Answers = [] | |
| qs[post.Id] = post | |
| elif post.PostType is PostType.Answer: | |
| answers[post.Id] = post | |
| for qk, aa in groupby(answers.values(), lambda a: a.ParentId): | |
| x = list(aa) | |
| x.sort(key=lambda x: -x.Score) | |
| qs[qk].Answers = x | |
| print(f"Processed {len(qs)} questions with {len(answers)} answers.") | |
| return qs | |
| def strip_html(string): | |
| soup = BeautifulSoup(string, "html.parser") | |
| return soup.get_text() | |
| def text_of_post(post): | |
| text = "" | |
| if post.Title: | |
| text += "TITLE: " + post.Title | |
| text += f"\nQUESTION [{post.Score} upvotes]: {strip_html(post.Body).strip()}" | |
| commented = False | |
| answered = False | |
| for answer in post.Answers: | |
| if answer.Score >= 2: | |
| answered = True | |
| text += ( | |
| f"\n\nREPLY [{answer.Score} votes]: {strip_html(answer.Body).strip()}" | |
| ) | |
| return text, post.Score, post.Id, answered | |
| def get_and_format(url, save_dir): | |
| VAL_RATE = 0.05 | |
| Path(save_dir).mkdir(exist_ok=True, parents=True) | |
| archive_path = os.path.join(save_dir, "archive.7z") | |
| os.system(f"wget -O {archive_path} {url}") | |
| global DATA_DIR | |
| DATA_DIR = os.path.join(save_dir, "xml") | |
| print(f"DATA DIR {DATA_DIR}") | |
| os.system(f"7z e {archive_path} -o{DATA_DIR}") | |
| print("parsing xml...") | |
| qs = questions() | |
| print("converting xml to text...") | |
| qs_texts = [text_of_post(qs[key]) for key in tqdm(qs.keys())] | |
| for post, score, eyed, answered in tqdm(qs_texts): | |
| if score >= 5 and answered: | |
| if random.random() > VAL_RATE: | |
| shard_path = os.path.join(save_dir, "train.jsonl") | |
| else: | |
| shard_path = os.path.join(save_dir, "val.jsonl") | |
| with open(shard_path, "a+") as f: | |
| instance = { | |
| "text": post, | |
| "meta": { | |
| "set_name": "stack_exchange", | |
| "score": score, | |
| "question_id": eyed, | |
| }, | |
| } | |
| f.write(json.dumps(instance)) | |
| f.write("\n") | |
| os.system("gzip " + " ".join(os.path.join(save_dir, x) | |
| for x in ("train.jsonl", "val.jsonl"))) | |
| os.system(f"rm -r {DATA_DIR}") | |
| os.remove(archive_path) | |
| if __name__ == "__main__": | |
| get_and_format( | |
| "https://archive.org/download/stackexchange/mathoverflow.net.7z", | |
| save_dir="stack-exchange/math_overflow", | |
| ) | |
| get_and_format( | |
| "https://archive.org/download/stackexchange/math.stackexchange.com.7z", | |
| "stack-exchange/math_stack_exchange", | |
| ) | |
| get_and_format( | |
| "https://archive.org/download/stackexchange/physics.stackexchange.com.7z", | |
| "stack-exchange/physics_stack_exchange", | |
| ) | |
| get_and_format( | |
| "https://archive.org/download/stackexchange/cstheory.stackexchange.com.7z", | |
| "stack-exchange/cstheory_stack_exchange", | |
| ) | |
| get_and_format( | |
| "https://archive.org/download/stackexchange/datascience.stackexchange.com.7z", | |
| "stack-exchange/datascience_stack_exchange", | |
| ) | |
| get_and_format( | |
| "https://archive.org/download/stackexchange/proofassistants.stackexchange.com.7z", | |
| "stack-exchange/proofassistants_stack_exchange", | |
| ) | |