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import csv |
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import json |
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import os |
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from datasets import GeneratorBasedBuilder, Features, Value, Sequence, SplitGenerator, BuilderConfig, DatasetInfo, Split, Image |
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import logging |
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import pandas as pd |
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from typing import Dict |
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CITATION = "@InProceedings{huggingface:dataset,title = {Reddit Climate Comment},author={Catherine Wang},year={2024}}" |
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_DESCRIPTION = "This new dataset is designed to solve this great NLP task and is crafted with a lot of care." |
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_HOMEPAGE = "https://huggingface.co/datasets/cathw/reddit_climate_comment" |
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_LICENSE = "MIT" |
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_URL = "https://github.com/catherine-ywang/reddit_climate_comment_data/raw/main/climate_comments.csv.zip" |
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class NewDataset(GeneratorBasedBuilder): |
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def _info(self): |
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return DatasetInfo( |
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description=_DESCRIPTION, |
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features=Features({ |
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"id": Value("string"), |
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"post_title": Value("string"), |
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"post_author": Value("string"), |
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"post_body": Value("string"), |
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"post_url": Value("string"), |
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"post_pic": Image(), |
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"subreddit": Value("string"), |
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"post_timestamp": Value("string"), |
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"post_upvotes": Value("int32"), |
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"post_permalink": Value("string"), |
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"comments": Sequence({ |
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"CommentID": Value("string"), |
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"CommentAuthor": Value("string"), |
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"CommentBody": Value("string"), |
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"CommentTimestamp": Value("string"), |
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"CommentUpvotes": Value("int32"), |
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"CommentPermalink": Value("string"), |
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"replies": Sequence({ |
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"ReplyID": Value("string"), |
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"ReplyAuthor": Value("string"), |
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"ReplyBody": Value("string"), |
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"ReplyTimestamp": Value("string"), |
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"ReplyUpvotes": Value("int32"), |
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"ReplyPermalink": Value("string"), |
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}) |
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}) |
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}), |
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homepage=_HOMEPAGE, |
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) |
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def _split_generators(self, dl_manager): |
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path = dl_manager.download_and_extract(_URL) |
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train_splits = SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": path+"/climate_comments.csv"}) |
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return [train_splits] |
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def _generate_examples(self, filepath): |
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df = pd.read_csv(filepath) |
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for column in df.columns: |
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df[column] = df[column].replace({pd.NA: None}) |
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grouped_df = df.groupby('PostID') |
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for post_id, group in grouped_df: |
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post_data = group.iloc[0] |
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post_title = post_data['PostTitle'] |
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post_author = post_data['PostAuthor'] |
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post_body = post_data['PostBody'] |
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post_url = post_data['PostUrl'] |
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post_pic = post_data['PostPic'] |
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subreddit = post_data['Subreddit'] |
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post_timestamp = post_data['PostTimestamp'] |
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post_upvotes = post_data['PostUpvotes'] |
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post_permalink = post_data['PostPermalink'] |
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comments = [] |
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for comment_id in group['CommentID'].unique(): |
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comment_data = group[group['CommentID'] == comment_id].iloc[0] |
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comment_author = comment_data['CommentAuthor'] |
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comment_body = comment_data['CommentBody'] |
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comment_timestamp = comment_data['CommentTimestamp'] |
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comment_upvotes = comment_data['CommentUpvotes'] |
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comment_permalink = comment_data['CommentPermalink'] |
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replies = [] |
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reply_group = df[df['CommentID'] == comment_id] |
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for _, reply_data in reply_group.iterrows(): |
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reply_id = reply_data['ReplyID'] |
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reply_author = reply_data['ReplyAuthor'] |
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reply_body = reply_data['ReplyBody'] |
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reply_timestamp = reply_data['ReplyTimestamp'] |
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reply_upvotes = reply_data['ReplyUpvotes'] |
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reply_permalink = reply_data['ReplyPermalink'] |
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reply = { |
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"ReplyID": reply_id, |
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"ReplyAuthor": reply_author, |
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"ReplyBody": reply_body, |
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"ReplyTimestamp": reply_timestamp, |
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"ReplyUpvotes": reply_upvotes, |
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"ReplyPermalink": reply_permalink |
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} |
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replies.append(reply) |
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comment = { |
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"CommentID": comment_id, |
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"CommentAuthor": comment_author, |
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"CommentBody": comment_body, |
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"CommentTimestamp": comment_timestamp, |
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"CommentUpvotes": comment_upvotes, |
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"CommentPermalink": comment_permalink, |
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"replies": replies |
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} |
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comments.append(comment) |
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example = { |
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"id": post_id, |
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"post_title": post_title, |
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"post_author": post_author, |
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"post_body": post_body, |
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"post_url": post_url, |
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"post_pic": post_pic, |
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"subreddit": subreddit, |
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"post_timestamp": post_timestamp, |
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"post_upvotes": post_upvotes, |
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"post_permalink": post_permalink, |
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"comments": comments |
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} |
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yield post_id, example |
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