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Upload self-annotated_reddit_climate_comment.py

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self-annotated_reddit_climate_comment.py ADDED
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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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+
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+ CITATION = ""
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+ _DESCRIPTION = "Demo"
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+ _HOMEPAGE = "https://huggingface.co/datasets/SarcasmNet/self-annotated_reddit_climate_comment"
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+ _LICENSE = "MIT"
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+
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+ _URL = "https://github.com/catherine-ywang/Reddit-Climate-Environment-Sarcasm-Self-Annotated-Data/raw/main/self_annotated_comments.csv"
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+
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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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+ "CommentLink": Value("int32")
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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+"/self_annotated_comments.csv"})
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+ return [train_splits]
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+
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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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+ # Group the DataFrame by post ID
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+ grouped_df = df.groupby('PostID')
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+
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+ for post_id, group in grouped_df:
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+ post_data = group.iloc[0] # Get the data for the post
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+
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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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+
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+ comments = []
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+
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+ # Iterate over each unique comment ID
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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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+
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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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+ comment_label = comment_data['CommentLabel']
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+
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+ # Add comment with its replies to the list
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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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+ "CommentLabel": comment_label
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+ }
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+ comments.append(comment)
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+
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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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+
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+ yield post_id, example
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+
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+
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+