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Datasets:
devopsmarc
/
my-issues-dataset

Tasks:
Sentence Similarity
Modalities:
Text
Formats:
parquet
Sub-tasks:
semantic-similarity-classification
Languages:
English
Size:
1K - 10K
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
devopsmarc commited on
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448c5dd
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1 Parent(s): c3306ef

Delete Github_Issues_Dataset.ipynb

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  1. Github_Issues_Dataset.ipynb +0 -750
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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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- "id": "26c2c9a4",
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- "metadata": {
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- "scrolled": true
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- },
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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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- "Requirement already satisfied: requests in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (2.29.0)\n",
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- "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from requests) (2.0.4)\n",
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- "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from requests) (3.4)\n",
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- "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from requests) (1.26.16)\n",
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- "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from requests) (2023.5.7)\n"
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- ]
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- }
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- ],
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- "source": [
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- "!pip install requests"
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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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- "id": "29c220c8",
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "import requests\n",
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- "\n",
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- "url = \"https://api.github.com/repos/huggingface/datasets/issues?page=1&per_page=1\"\n",
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- "response = requests.get(url)\n"
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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": 3,
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- "id": "915c0703",
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- "execution_count": 3,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "response.status_code"
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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": 4,
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- "id": "b8440767",
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/6151',\n",
71
- " 'repository_url': 'https://api.github.com/repos/huggingface/datasets',\n",
72
- " 'labels_url': 'https://api.github.com/repos/huggingface/datasets/issues/6151/labels{/name}',\n",
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- " 'comments_url': 'https://api.github.com/repos/huggingface/datasets/issues/6151/comments',\n",
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- " 'events_url': 'https://api.github.com/repos/huggingface/datasets/issues/6151/events',\n",
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- " 'html_url': 'https://github.com/huggingface/datasets/issues/6151',\n",
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- " 'id': 1851497818,\n",
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- " 'node_id': 'I_kwDODunzps5uW51a',\n",
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- " 'number': 6151,\n",
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- " 'title': 'Faster sorting for single key items',\n",
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- " 'user': {'login': 'jackapbutler',\n",
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- " 'id': 47942453,\n",
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- " 'node_id': 'MDQ6VXNlcjQ3OTQyNDUz',\n",
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- " 'avatar_url': 'https://avatars.githubusercontent.com/u/47942453?v=4',\n",
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- " 'gravatar_id': '',\n",
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- " 'url': 'https://api.github.com/users/jackapbutler',\n",
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- " 'html_url': 'https://github.com/jackapbutler',\n",
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- " 'followers_url': 'https://api.github.com/users/jackapbutler/followers',\n",
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- " 'following_url': 'https://api.github.com/users/jackapbutler/following{/other_user}',\n",
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- " 'gists_url': 'https://api.github.com/users/jackapbutler/gists{/gist_id}',\n",
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- " 'starred_url': 'https://api.github.com/users/jackapbutler/starred{/owner}{/repo}',\n",
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- " 'subscriptions_url': 'https://api.github.com/users/jackapbutler/subscriptions',\n",
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- " 'organizations_url': 'https://api.github.com/users/jackapbutler/orgs',\n",
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- " 'repos_url': 'https://api.github.com/users/jackapbutler/repos',\n",
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- " 'events_url': 'https://api.github.com/users/jackapbutler/events{/privacy}',\n",
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- " 'received_events_url': 'https://api.github.com/users/jackapbutler/received_events',\n",
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- " 'type': 'User',\n",
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- " 'site_admin': False},\n",
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- " 'labels': [{'id': 1935892871,\n",
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- " 'node_id': 'MDU6TGFiZWwxOTM1ODkyODcx',\n",
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- " 'url': 'https://api.github.com/repos/huggingface/datasets/labels/enhancement',\n",
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- " 'name': 'enhancement',\n",
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- " 'color': 'a2eeef',\n",
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- " 'default': True,\n",
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- " 'description': 'New feature or request'}],\n",
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- " 'state': 'open',\n",
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- " 'locked': False,\n",
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- " 'assignee': None,\n",
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- " 'assignees': [],\n",
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- " 'milestone': None,\n",
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- " 'comments': 0,\n",
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- " 'created_at': '2023-08-15T14:02:31Z',\n",
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- " 'updated_at': '2023-08-15T14:17:09Z',\n",
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- " 'closed_at': None,\n",
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- " 'author_association': 'NONE',\n",
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- " 'active_lock_reason': None,\n",
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- " 'body': '### Feature request\\r\\n\\r\\nA faster way to sort a dataset which contains a large number of rows.\\r\\n\\r\\n### Motivation\\r\\n\\r\\nThe current sorting implementations took significantly longer than expected when I was running on a dataset trying to sort by timestamps. \\r\\n\\r\\n**Code snippet:**\\r\\n```python\\r\\nds = datasets.load_dataset( \"json\", **{\"data_files\": {\"train\": \"path-to-jsonlines\"}, \"split\": \"train\"}, num_proc=os.cpu_count(), keep_in_memory=True) \\r\\nsorted_ds = ds.sort(\"pubDate\", keep_in_memory=True)\\r\\n```\\r\\n\\r\\nHowever, once I switched to a different method which\\r\\n1. unpacked to a list of tuples\\r\\n2. sorted tuples by key\\r\\n3. run `.select` with the sorted list of indices\\r\\nIt was significantly faster (orders of magnitude, especially with M\\'s of rows)\\r\\n\\r\\n### Your contribution\\r\\n\\r\\nI\\'d be happy to implement a crude single key sorting algorithm so that other users can benefit from this trick. Broadly, this would take a `Dataset` and perform;\\r\\n\\r\\n```python\\r\\n# ds is a Dataset object\\r\\n# key_name is the sorting key\\r\\n\\r\\nclass Dataset:\\r\\n ...\\r\\n def _sort(key_name: str) -> Dataset:\\r\\n index_keys = [(i,x) for i,x in enumerate(self[key_name])]\\r\\n sorted_rows = sorted(row_pubdate, key=lambda x: x[1])\\r\\n sorted_indicies = [x[0] for x in sorted_rows]\\r\\n return self.select(sorted_indicies)\\r\\n```',\n",
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- " 'reactions': {'url': 'https://api.github.com/repos/huggingface/datasets/issues/6151/reactions',\n",
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- " 'total_count': 0,\n",
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- " '+1': 0,\n",
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- " '-1': 0,\n",
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- " 'laugh': 0,\n",
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- " 'hooray': 0,\n",
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- " 'confused': 0,\n",
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- " 'heart': 0,\n",
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- " 'rocket': 0,\n",
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- " 'eyes': 0},\n",
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- " 'timeline_url': 'https://api.github.com/repos/huggingface/datasets/issues/6151/timeline',\n",
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- " 'performed_via_github_app': None,\n",
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- " 'state_reason': None}]"
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- ]
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- },
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- "execution_count": 4,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "response.json()"
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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": 5,
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- "id": "96ab6dc2",
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "GITHUB_TOKEN = \"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\"\n",
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- "headers = {\"Authorization\": f\"token {GITHUB_TOKEN}\"}"
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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": 6,
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- "id": "c5cd6f0f",
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "import time\n",
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- "import math\n",
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- "from pathlib import Path\n",
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- "import pandas as pd\n",
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- "from tqdm.notebook import tqdm\n",
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- "\n",
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- "\n",
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- "def fetch_issues(\n",
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- " owner=\"huggingface\",\n",
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- " repo=\"datasets\",\n",
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- " num_issues=10_000,\n",
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- " rate_limit=5_000,\n",
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- " issues_path=Path(\".\"),\n",
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- "):\n",
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- " if not issues_path.is_dir():\n",
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- " issues_path.mkdir(exist_ok=True)\n",
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- "\n",
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- " batch = []\n",
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- " all_issues = []\n",
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- " per_page = 100 # Number of issues to return per page\n",
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- " num_pages = math.ceil(num_issues / per_page)\n",
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- " base_url = \"https://api.github.com/repos\"\n",
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- "\n",
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- " for page in tqdm(range(num_pages)):\n",
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- " # Query with state=all to get both open and closed issues\n",
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- " query = f\"issues?page={page}&per_page={per_page}&state=all\"\n",
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- " issues = requests.get(f\"{base_url}/{owner}/{repo}/{query}\", headers=headers)\n",
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- " batch.extend(issues.json())\n",
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- "\n",
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- " if len(batch) > rate_limit and len(all_issues) < num_issues:\n",
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- " all_issues.extend(batch)\n",
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- " batch = [] # Flush batch for next time period\n",
191
- " print(f\"Reached GitHub rate limit. Sleeping for one hour ...\")\n",
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- " time.sleep(60 * 60 + 1)\n",
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- "\n",
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- " all_issues.extend(batch)\n",
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- " df = pd.DataFrame.from_records(all_issues)\n",
196
- " df.to_json(f\"{issues_path}/{repo}-issues.jsonl\", orient=\"records\", lines=True)\n",
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- " print(\n",
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- " f\"Downloaded all the issues for {repo}! Dataset stored at {issues_path}/{repo}-issues.jsonl\"\n",
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- " )"
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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": 7,
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- "id": "3cb793df",
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- "outputs": [
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- {
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- "data": {
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- "application/vnd.jupyter.widget-view+json": {
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- "model_id": "12f444a712834ac1bea766e6175344ac",
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- "version_major": 2,
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- "metadata": {},
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "Reached GitHub rate limit. Sleeping for one hour ...\n",
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- "Downloaded all the issues for datasets! Dataset stored at ./datasets-issues.jsonl\n"
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- ]
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- }
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- ],
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- "source": [
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- "fetch_issues()"
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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": 8,
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- "id": "50e56012",
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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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- "Requirement already satisfied: pandas in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (1.5.3)\n",
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- "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
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- "Requirement already satisfied: pytz>=2020.1 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from pandas) (2022.7)\n",
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- "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from pandas) (1.24.3)\n",
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- "Requirement already satisfied: six>=1.5 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
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- ]
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- }
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- ],
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- "source": [
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- "!pip install pandas\n"
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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": 9,
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- "id": "ab9674e9",
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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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- "The file is located at: C:\\Users\\060971CA8\\a_HF_Spaces\\datasets-issues.jsonl\n"
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- ]
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- }
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- ],
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- "source": [
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- "import os\n",
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- "\n",
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- "# Get the current working directory\n",
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- "current_dir = os.getcwd()\n",
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- "\n",
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- "# Construct the absolute path to the file\n",
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- "file_path = os.path.join(current_dir, \"datasets-issues.jsonl\")\n",
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- "\n",
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- "print(f\"The file is located at: {file_path}\")\n"
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- {
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- "execution_count": 10,
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- "outputs": [
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- "Requirement already satisfied: datasets in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (2.13.1)\n",
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- "Requirement already satisfied: numpy>=1.17 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (1.24.3)\n",
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- "Requirement already satisfied: dill<0.3.7,>=0.3.0 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (0.3.6)\n",
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- "Requirement already satisfied: pandas in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (1.5.3)\n",
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- "Requirement already satisfied: xxhash in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (3.2.0)\n",
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- "Requirement already satisfied: multiprocess in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (0.70.14)\n",
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- "Requirement already satisfied: fsspec[http]>=2021.11.1 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (2023.3.0)\n",
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- "Requirement already satisfied: huggingface-hub<1.0.0,>=0.11.0 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (0.16.4)\n",
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- "Requirement already satisfied: packaging in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (23.0)\n",
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- "Requirement already satisfied: multidict<7.0,>=4.5 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from aiohttp->datasets) (6.0.2)\n",
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- "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from aiohttp->datasets) (4.0.2)\n",
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- "Requirement already satisfied: yarl<2.0,>=1.0 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from aiohttp->datasets) (1.8.1)\n",
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- "Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from aiohttp->datasets) (1.3.3)\n",
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- "Requirement already satisfied: aiosignal>=1.1.2 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from aiohttp->datasets) (1.2.0)\n",
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- "Requirement already satisfied: filelock in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (3.9.0)\n",
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- "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (4.7.1)\n",
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- "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from requests>=2.19.0->datasets) (3.4)\n",
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- "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from requests>=2.19.0->datasets) (1.26.16)\n",
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- "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from requests>=2.19.0->datasets) (2023.5.7)\n",
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- "Requirement already satisfied: colorama in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from tqdm>=4.62.1->datasets) (0.4.6)\n",
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- "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from pandas->datasets) (2.8.2)\n",
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- "Requirement already satisfied: pytz>=2020.1 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from pandas->datasets) (2022.7)\n",
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- "Note: you may need to restart the kernel to use updated packages.\n"
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- ]
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- }
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- ],
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- "source": [
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- "pip install datasets\n"
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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": 11,
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- "id": "141a71d1",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "text": [
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- "Requirement already satisfied: datasets in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (2.13.1)\n",
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- "Collecting datasets\n",
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- " Downloading datasets-2.14.4-py3-none-any.whl (519 kB)\n",
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- " 0.0/519.3 kB ? eta -:--:--\n",
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- " -------- 112.6/519.3 kB 3.3 MB/s eta 0:00:01\n",
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- "Requirement already satisfied: numpy>=1.17 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (1.24.3)\n",
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- "Requirement already satisfied: pyarrow>=8.0.0 in c:\\users\\060971ca8\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (11.0.0)\n",
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- " Attempting uninstall: datasets\n",
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- " Found existing installation: datasets 2.13.1\n",
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- " Uninstalling datasets-2.13.1:\n",
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- " Successfully uninstalled datasets-2.13.1\n",
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- "Note: you may need to restart the kernel to use updated packages.\n"
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- ]
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- }
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- ],
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- "source": [
387
- "pip install datasets --upgrade"
388
- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 12,
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- "id": "319bdad1",
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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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- "from datasets import Dataset\n"
399
- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 15,
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- "id": "eb77eadb",
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "model_id": "fca0976311064de6bea95943252def8c",
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- "version_major": 2,
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- "model_id": "56ebb5c3c00a492581e2f919037e6f34",
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- "data": {
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- "model_id": "783c057424a2491692779373d2dd2b03",
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- "version_major": 2,
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- "metadata": {},
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- "output_type": "display_data"
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- },
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- {
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- "ename": "DatasetGenerationError",
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- "evalue": "An error occurred while generating the dataset",
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- "output_type": "error",
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- "traceback": [
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- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
455
- "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
456
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\builder.py:1940\u001b[0m, in \u001b[0;36mArrowBasedBuilder._prepare_split_single\u001b[1;34m(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)\u001b[0m\n\u001b[0;32m 1933\u001b[0m writer \u001b[38;5;241m=\u001b[39m writer_class(\n\u001b[0;32m 1934\u001b[0m features\u001b[38;5;241m=\u001b[39mwriter\u001b[38;5;241m.\u001b[39m_features,\n\u001b[0;32m 1935\u001b[0m path\u001b[38;5;241m=\u001b[39mfpath\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSSSSS\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mshard_id\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m05d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJJJJJ\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mjob_id\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m05d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1938\u001b[0m embed_local_files\u001b[38;5;241m=\u001b[39membed_local_files,\n\u001b[0;32m 1939\u001b[0m )\n\u001b[1;32m-> 1940\u001b[0m writer\u001b[38;5;241m.\u001b[39mwrite_table(table)\n\u001b[0;32m 1941\u001b[0m num_examples_progress_update \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(table)\n",
457
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\arrow_writer.py:572\u001b[0m, in \u001b[0;36mArrowWriter.write_table\u001b[1;34m(self, pa_table, writer_batch_size)\u001b[0m\n\u001b[0;32m 571\u001b[0m pa_table \u001b[38;5;241m=\u001b[39m pa_table\u001b[38;5;241m.\u001b[39mcombine_chunks()\n\u001b[1;32m--> 572\u001b[0m pa_table \u001b[38;5;241m=\u001b[39m table_cast(pa_table, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_schema)\n\u001b[0;32m 573\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed_local_files:\n",
458
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:2328\u001b[0m, in \u001b[0;36mtable_cast\u001b[1;34m(table, schema)\u001b[0m\n\u001b[0;32m 2327\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m table\u001b[38;5;241m.\u001b[39mschema \u001b[38;5;241m!=\u001b[39m schema:\n\u001b[1;32m-> 2328\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast_table_to_schema(table, schema)\n\u001b[0;32m 2329\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m table\u001b[38;5;241m.\u001b[39mschema\u001b[38;5;241m.\u001b[39mmetadata \u001b[38;5;241m!=\u001b[39m schema\u001b[38;5;241m.\u001b[39mmetadata:\n",
459
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:2287\u001b[0m, in \u001b[0;36mcast_table_to_schema\u001b[1;34m(table, schema)\u001b[0m\n\u001b[0;32m 2286\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt cast\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mtable\u001b[38;5;241m.\u001b[39mschema\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mto\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfeatures\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mbecause column names don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 2287\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [cast_array_to_feature(table[name], feature) \u001b[38;5;28;01mfor\u001b[39;00m name, feature \u001b[38;5;129;01min\u001b[39;00m features\u001b[38;5;241m.\u001b[39mitems()]\n\u001b[0;32m 2288\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mTable\u001b[38;5;241m.\u001b[39mfrom_arrays(arrays, schema\u001b[38;5;241m=\u001b[39mschema)\n",
460
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:2287\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 2286\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt cast\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mtable\u001b[38;5;241m.\u001b[39mschema\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mto\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfeatures\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mbecause column names don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 2287\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [cast_array_to_feature(table[name], feature) \u001b[38;5;28;01mfor\u001b[39;00m name, feature \u001b[38;5;129;01min\u001b[39;00m features\u001b[38;5;241m.\u001b[39mitems()]\n\u001b[0;32m 2288\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mTable\u001b[38;5;241m.\u001b[39mfrom_arrays(arrays, schema\u001b[38;5;241m=\u001b[39mschema)\n",
461
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:1831\u001b[0m, in \u001b[0;36m_wrap_for_chunked_arrays.<locals>.wrapper\u001b[1;34m(array, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1830\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(array, pa\u001b[38;5;241m.\u001b[39mChunkedArray):\n\u001b[1;32m-> 1831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mchunked_array([func(chunk, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m array\u001b[38;5;241m.\u001b[39mchunks])\n\u001b[0;32m 1832\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
462
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:1831\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 1830\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(array, pa\u001b[38;5;241m.\u001b[39mChunkedArray):\n\u001b[1;32m-> 1831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mchunked_array([func(chunk, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m array\u001b[38;5;241m.\u001b[39mchunks])\n\u001b[0;32m 1832\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
463
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:2073\u001b[0m, in \u001b[0;36mcast_array_to_feature\u001b[1;34m(array, feature, allow_number_to_str)\u001b[0m\n\u001b[0;32m 2072\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m array\n\u001b[1;32m-> 2073\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [_c(array\u001b[38;5;241m.\u001b[39mfield(name), subfeature) \u001b[38;5;28;01mfor\u001b[39;00m name, subfeature \u001b[38;5;129;01min\u001b[39;00m feature\u001b[38;5;241m.\u001b[39mitems()]\n\u001b[0;32m 2074\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mStructArray\u001b[38;5;241m.\u001b[39mfrom_arrays(arrays, names\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlist\u001b[39m(feature), mask\u001b[38;5;241m=\u001b[39marray\u001b[38;5;241m.\u001b[39mis_null())\n",
464
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:2073\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 2072\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m array\n\u001b[1;32m-> 2073\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [_c(array\u001b[38;5;241m.\u001b[39mfield(name), subfeature) \u001b[38;5;28;01mfor\u001b[39;00m name, subfeature \u001b[38;5;129;01min\u001b[39;00m feature\u001b[38;5;241m.\u001b[39mitems()]\n\u001b[0;32m 2074\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mStructArray\u001b[38;5;241m.\u001b[39mfrom_arrays(arrays, names\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlist\u001b[39m(feature), mask\u001b[38;5;241m=\u001b[39marray\u001b[38;5;241m.\u001b[39mis_null())\n",
465
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:1833\u001b[0m, in \u001b[0;36m_wrap_for_chunked_arrays.<locals>.wrapper\u001b[1;34m(array, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1832\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1833\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(array, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
466
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:2143\u001b[0m, in \u001b[0;36mcast_array_to_feature\u001b[1;34m(array, feature, allow_number_to_str)\u001b[0m\n\u001b[0;32m 2142\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(feature, (Sequence, \u001b[38;5;28mdict\u001b[39m, \u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m-> 2143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m array_cast(array, feature(), allow_number_to_str\u001b[38;5;241m=\u001b[39mallow_number_to_str)\n\u001b[0;32m 2144\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt cast array of type\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00marray\u001b[38;5;241m.\u001b[39mtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mto\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfeature\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
467
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:1833\u001b[0m, in \u001b[0;36m_wrap_for_chunked_arrays.<locals>.wrapper\u001b[1;34m(array, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1832\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1833\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(array, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
468
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\table.py:2026\u001b[0m, in \u001b[0;36marray_cast\u001b[1;34m(array, pa_type, allow_number_to_str)\u001b[0m\n\u001b[0;32m 2025\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mtypes\u001b[38;5;241m.\u001b[39mis_null(pa_type) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mtypes\u001b[38;5;241m.\u001b[39mis_null(array\u001b[38;5;241m.\u001b[39mtype):\n\u001b[1;32m-> 2026\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt cast array of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00marray\u001b[38;5;241m.\u001b[39mtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpa_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 2027\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m array\u001b[38;5;241m.\u001b[39mcast(pa_type)\n",
469
- "\u001b[1;31mTypeError\u001b[0m: Couldn't cast array of type timestamp[s] to null",
470
- "\nThe above exception was the direct cause of the following exception:\n",
471
- "\u001b[1;31mDatasetGenerationError\u001b[0m Traceback (most recent call last)",
472
- "Cell \u001b[1;32mIn[15], line 5\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Correct file path\u001b[39;00m\n\u001b[0;32m 4\u001b[0m file_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC:/Users/060971CA8/a_HF_Spaces/datasets-issues.jsonl\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 5\u001b[0m issues_dataset \u001b[38;5;241m=\u001b[39m load_dataset(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjson\u001b[39m\u001b[38;5;124m\"\u001b[39m, data_files\u001b[38;5;241m=\u001b[39mfile_path, split\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(issues_dataset)\n",
473
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\load.py:2136\u001b[0m, in \u001b[0;36mload_dataset\u001b[1;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\u001b[0m\n\u001b[0;32m 2133\u001b[0m try_from_hf_gcs \u001b[38;5;241m=\u001b[39m path \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[0;32m 2135\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[1;32m-> 2136\u001b[0m builder_instance\u001b[38;5;241m.\u001b[39mdownload_and_prepare(\n\u001b[0;32m 2137\u001b[0m download_config\u001b[38;5;241m=\u001b[39mdownload_config,\n\u001b[0;32m 2138\u001b[0m download_mode\u001b[38;5;241m=\u001b[39mdownload_mode,\n\u001b[0;32m 2139\u001b[0m verification_mode\u001b[38;5;241m=\u001b[39mverification_mode,\n\u001b[0;32m 2140\u001b[0m try_from_hf_gcs\u001b[38;5;241m=\u001b[39mtry_from_hf_gcs,\n\u001b[0;32m 2141\u001b[0m num_proc\u001b[38;5;241m=\u001b[39mnum_proc,\n\u001b[0;32m 2142\u001b[0m storage_options\u001b[38;5;241m=\u001b[39mstorage_options,\n\u001b[0;32m 2143\u001b[0m )\n\u001b[0;32m 2145\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[0;32m 2146\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 2147\u001b[0m keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size)\n\u001b[0;32m 2148\u001b[0m )\n",
474
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\builder.py:954\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[1;34m(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[0;32m 952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 953\u001b[0m prepare_split_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_proc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_proc\n\u001b[1;32m--> 954\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_download_and_prepare(\n\u001b[0;32m 955\u001b[0m dl_manager\u001b[38;5;241m=\u001b[39mdl_manager,\n\u001b[0;32m 956\u001b[0m verification_mode\u001b[38;5;241m=\u001b[39mverification_mode,\n\u001b[0;32m 957\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_split_kwargs,\n\u001b[0;32m 958\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdownload_and_prepare_kwargs,\n\u001b[0;32m 959\u001b[0m )\n\u001b[0;32m 960\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[0;32m 961\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n",
475
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\builder.py:1049\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[1;34m(self, dl_manager, verification_mode, **prepare_split_kwargs)\u001b[0m\n\u001b[0;32m 1045\u001b[0m split_dict\u001b[38;5;241m.\u001b[39madd(split_generator\u001b[38;5;241m.\u001b[39msplit_info)\n\u001b[0;32m 1047\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1048\u001b[0m \u001b[38;5;66;03m# Prepare split will record examples associated to the split\u001b[39;00m\n\u001b[1;32m-> 1049\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_split(split_generator, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_split_kwargs)\n\u001b[0;32m 1050\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 1051\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[0;32m 1052\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot find data file. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1053\u001b[0m \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_download_instructions \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1054\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mOriginal error:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1055\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n\u001b[0;32m 1056\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
476
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\builder.py:1813\u001b[0m, in \u001b[0;36mArrowBasedBuilder._prepare_split\u001b[1;34m(self, split_generator, file_format, num_proc, max_shard_size)\u001b[0m\n\u001b[0;32m 1811\u001b[0m job_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 1812\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pbar:\n\u001b[1;32m-> 1813\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m job_id, done, content \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_split_single(\n\u001b[0;32m 1814\u001b[0m gen_kwargs\u001b[38;5;241m=\u001b[39mgen_kwargs, job_id\u001b[38;5;241m=\u001b[39mjob_id, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_prepare_split_args\n\u001b[0;32m 1815\u001b[0m ):\n\u001b[0;32m 1816\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m done:\n\u001b[0;32m 1817\u001b[0m result \u001b[38;5;241m=\u001b[39m content\n",
477
- "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\datasets\\builder.py:1958\u001b[0m, in \u001b[0;36mArrowBasedBuilder._prepare_split_single\u001b[1;34m(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)\u001b[0m\n\u001b[0;32m 1956\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, SchemaInferenceError) \u001b[38;5;129;01mand\u001b[39;00m e\u001b[38;5;241m.\u001b[39m__context__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1957\u001b[0m e \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39m__context__\n\u001b[1;32m-> 1958\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DatasetGenerationError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred while generating the dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m 1960\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m job_id, \u001b[38;5;28;01mTrue\u001b[39;00m, (total_num_examples, total_num_bytes, writer\u001b[38;5;241m.\u001b[39m_features, num_shards, shard_lengths)\n",
478
- "\u001b[1;31mDatasetGenerationError\u001b[0m: An error occurred while generating the dataset"
479
- ]
480
- }
481
- ],
482
- "source": [
483
- "from datasets import load_dataset\n",
484
- "\n",
485
- "# Correct file path\n",
486
- "file_path = \"C:/Users/060971CA8/a_HF_Spaces/datasets-issues.jsonl\"\n",
487
- "issues_dataset = load_dataset(\"json\", data_files=file_path, split=\"train\")\n",
488
- "\n",
489
- "print(issues_dataset)\n"
490
- ]
491
- },
492
- {
493
- "cell_type": "code",
494
- "execution_count": 16,
495
- "id": "d5ef113b",
496
- "metadata": {},
497
- "outputs": [
498
- {
499
- "name": "stdout",
500
- "output_type": "stream",
501
- "text": [
502
- "Dataset({\n",
503
- " features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'body', 'reactions', 'timeline_url', 'performed_via_github_app', 'state_reason', 'draft', 'pull_request'],\n",
504
- " num_rows: 6139\n",
505
- "})\n"
506
- ]
507
- }
508
- ],
509
- "source": [
510
- "import pandas as pd\n",
511
- "from datasets import Dataset\n",
512
- "\n",
513
- "# Correct file path\n",
514
- "file_path = \"C:/Users/060971CA8/a_HF_Spaces/datasets-issues.jsonl\"\n",
515
- "df = pd.read_json(file_path, lines=True)\n",
516
- "\n",
517
- "for col in df.columns:\n",
518
- " if df[col].dtype == 'datetime64[ns]':\n",
519
- " df[col] = df[col].astype(str)\n",
520
- "\n",
521
- "issues_dataset = Dataset.from_pandas(df)\n",
522
- "\n",
523
- "print(issues_dataset)\n"
524
- ]
525
- },
526
- {
527
- "cell_type": "code",
528
- "execution_count": 17,
529
- "id": "179b2818",
530
- "metadata": {},
531
- "outputs": [],
532
- "source": [
533
- "sample = issues_dataset.shuffle(seed=666).select(range(3))"
534
- ]
535
- },
536
- {
537
- "cell_type": "code",
538
- "execution_count": 18,
539
- "id": "ea0a0067",
540
- "metadata": {},
541
- "outputs": [
542
- {
543
- "name": "stdout",
544
- "output_type": "stream",
545
- "text": [
546
- ">> URL: https://github.com/huggingface/datasets/pull/4516\n",
547
- ">> Pull request: {'diff_url': 'https://github.com/huggingface/datasets/pull/4516.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/4516', 'merged_at': '2022-06-28T13:23:05Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/4516.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/4516'}\n",
548
- "\n",
549
- ">> URL: https://github.com/huggingface/datasets/issues/5346\n",
550
- ">> Pull request: None\n",
551
- "\n",
552
- ">> URL: https://github.com/huggingface/datasets/pull/783\n",
553
- ">> Pull request: {'diff_url': 'https://github.com/huggingface/datasets/pull/783.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/783', 'merged_at': None, 'patch_url': 'https://github.com/huggingface/datasets/pull/783.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/783'}\n",
554
- "\n"
555
- ]
556
- }
557
- ],
558
- "source": [
559
- "for url, pr in zip(sample[\"html_url\"], sample[\"pull_request\"]):\n",
560
- " print(f\">> URL: {url}\")\n",
561
- " print(f\">> Pull request: {pr}\\n\")"
562
- ]
563
- },
564
- {
565
- "cell_type": "code",
566
- "execution_count": 19,
567
- "id": "0388dabb",
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- "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/comments/897594128',\n",
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- " 'id': 897594128,\n",
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- " 'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
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- " 'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
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- " 'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
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- " 'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
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- " 'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
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- " 'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
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- " 'created_at': '2021-08-12T12:21:52Z',\n",
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- " 'updated_at': '2021-08-12T12:31:17Z',\n",
598
- " 'author_association': 'CONTRIBUTOR',\n",
599
- " 'body': \"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n def test_load_dataset(self, dataset_name):\\r\\n configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\",\n",
600
- " 'reactions': {'url': 'https://api.github.com/repos/huggingface/datasets/issues/comments/897594128/reactions',\n",
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- " 'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
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- " 'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
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- " 'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
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- " 'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
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- " 'created_at': '2021-08-13T18:28:27Z',\n",
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- " 'updated_at': '2021-08-13T18:28:27Z',\n",
636
- " 'author_association': 'CONTRIBUTOR',\n",
637
- " 'body': 'Thanks for the help, @albertvillanova! All tests are passing now.',\n",
638
- " 'reactions': {'url': 'https://api.github.com/repos/huggingface/datasets/issues/comments/898644889/reactions',\n",
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- "execution_count": 19,
652
- "metadata": {},
653
- "output_type": "execute_result"
654
- }
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- ],
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- "source": [
657
- "issue_number = 2792\n",
658
- "url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
659
- "response = requests.get(url, headers=headers)\n",
660
- "response.json()"
661
- ]
662
- },
663
- {
664
- "cell_type": "code",
665
- "execution_count": 20,
666
- "id": "2404a310",
667
- "metadata": {},
668
- "outputs": [
669
- {
670
- "data": {
671
- "text/plain": [
672
- "[\"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n def test_load_dataset(self, dataset_name):\\r\\n configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\",\n",
673
- " 'Thanks for the help, @albertvillanova! All tests are passing now.']"
674
- ]
675
- },
676
- "execution_count": 20,
677
- "metadata": {},
678
- "output_type": "execute_result"
679
- }
680
- ],
681
- "source": [
682
- "def get_comments(issue_number):\n",
683
- " url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
684
- " response = requests.get(url, headers=headers)\n",
685
- " return [r[\"body\"] for r in response.json()]\n",
686
- "\n",
687
- "\n",
688
- "# Test our function works as expected\n",
689
- "get_comments(2792)"
690
- ]
691
- },
692
- {
693
- "cell_type": "code",
694
- "execution_count": 21,
695
- "id": "092dd794",
696
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- "outputs": [
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- "data": {
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- "version_major": 2,
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- "version_minor": 0
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- },
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- "text/plain": [
706
- "Map: 0%| | 0/6139 [00:00<?, ? examples/s]"
707
- ]
708
- },
709
- "metadata": {},
710
- "output_type": "display_data"
711
- }
712
- ],
713
- "source": [
714
- "# Depending on your internet connection, this can take a few minutes...\n",
715
- "issues_with_comments_dataset = issues_dataset.map(\n",
716
- " lambda x: {\"comments\": get_comments(x[\"number\"])}\n",
717
- ")"
718
- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "id": "013b0fa9",
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- "name": "ipython",
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