Datasets:
divyasharma0795
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Upload dataclass_code.ipynb
Browse files- 01 Codes/dataclass_code.ipynb +101 -0
01 Codes/dataclass_code.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from datasets import load_dataset\n",
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"\n",
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"dataset = load_dataset(\"divyasharma0795/AppleVisionPro_Tweets\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"HuggingFaceDataset(id=1769458624638619691, tweetText=\"Mordecai I can't sell nft for 10 dollars, how does this nft business work? #bloodbath #AppleVisionPro #DeadpoolAndWolverine #RegularShow @JGQuintel link: https://t.co/Bq3dzV4wgR https://t.co/rbrerIFcIs\", tweetURL='https://twitter.com/harndefty/status/1769458624638619691', tweetAuthor='Harndefty 🐔🍗', handle='@harndefty', replyCount=0, quoteCount=0, retweetCount=0, likeCount=0, views='26', bookmarkCount=0, createdAt='2024-03-17 13:19:45')\n"
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]
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}
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],
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"source": [
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"from dataclasses import dataclass\n",
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"from typing import List\n",
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"from datasets import load_dataset\n",
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"\n",
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"\n",
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"@dataclass\n",
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"class HuggingFaceDataset:\n",
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" id: int\n",
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" tweetText: str\n",
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" tweetURL: str\n",
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" tweetAuthor: str\n",
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" handle: str\n",
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" replyCount: int\n",
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" quoteCount: int\n",
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" retweetCount: int\n",
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" likeCount: int\n",
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" views: int\n",
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" bookmarkCount: int\n",
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" createdAt: str\n",
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"\n",
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"\n",
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"def load_custom_dataset(dataset_name):\n",
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" dataset = load_dataset(dataset_name)\n",
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"\n",
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" # Extract relevant information and create a list of HuggingFaceDataset instances\n",
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" custom_dataset = [\n",
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" HuggingFaceDataset(\n",
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" id=row[\"id\"],\n",
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" tweetText=row[\"tweetText\"],\n",
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" tweetURL=row[\"tweetURL\"],\n",
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" tweetAuthor=row[\"tweetAuthor\"],\n",
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" handle=row[\"handle\"],\n",
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" replyCount=row[\"replyCount\"],\n",
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" quoteCount=row[\"quoteCount\"],\n",
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" retweetCount=row[\"retweetCount\"],\n",
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" likeCount=row[\"likeCount\"],\n",
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" views=row[\"views\"],\n",
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" bookmarkCount=row[\"bookmarkCount\"],\n",
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" createdAt=row[\"createdAt\"],\n",
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" )\n",
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" for row in dataset[\"train\"]\n",
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" ]\n",
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"\n",
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" return custom_dataset\n",
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"\n",
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"\n",
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"# Usage\n",
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"custom_dataset = load_custom_dataset(\"divyasharma0795/AppleVisionPro_Tweets\")\n",
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"print(custom_dataset[0]) # Print the first instance"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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