Datasets:
Tasks:
Text2Text Generation
Size Categories:
100K<n<1M
Annotations Creators:
no-annotation
Source Datasets:
extended|eli5
Tags:
License:
File size: 3,949 Bytes
0992654 |
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from pprint import pprint\n",
"from tqdm import tqdm\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"with open('data/askd_pt.json') as f:\n",
" pt = json.load(f)\n",
"\n",
"dataset = dict()\n",
"dataset['train_pt'] = pt['train_askd']\n",
"dataset['validation_pt'] = pt['validation_askd']\n",
"dataset['test_pt'] = pt['test_askd']\n",
"\n",
"with open('data/askd_en_augmented.json') as f:\n",
" en = json.load(f)\n",
"\n",
"dataset['train_en'] = en['train_askd']\n",
"dataset['validation_en'] = en['validation_askd']\n",
"dataset['test_en'] = en['test_askd']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"for split in dataset:\n",
" for item in dataset[split]:\n",
" for key in [\"selftext_urls\", \"title_urls\", \"answers_urls\"]:\n",
" if not isinstance(item[key], list):\n",
" item[key] = [item[key]]\n",
" \n",
" for key in [\"a_id\", \"score\", \"text\"]:\n",
" if not isinstance(item['answers'][key], list):\n",
" item['answers'][key] = [item['answers'][key]]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 24256/24256 [00:00<00:00, 1048176.28it/s]\n",
"100%|██████████| 5198/5198 [00:00<00:00, 1092120.03it/s]\n",
"100%|██████████| 5198/5198 [00:00<00:00, 1032878.16it/s]\n",
"100%|██████████| 141019/141019 [13:54<00:00, 169.05it/s] \n",
"100%|██████████| 30219/30219 [00:19<00:00, 1549.74it/s]\n",
"100%|██████████| 30218/30218 [00:17<00:00, 1734.32it/s]\n"
]
}
],
"source": [
"external_pt = list()\n",
"external_en = list()\n",
"\n",
"for split in dataset:\n",
" for item in tqdm(dataset[split].copy()):\n",
" if item['answers']['a_id'] == ['0']:\n",
" dataset[split].remove(item)\n",
"\n",
" if split.endswith('pt'):\n",
" external_pt.append(item)\n",
" else:\n",
" external_en.append(item)\n",
"\n",
"dataset['external_en'] = external_en\n",
"dataset['external_pt'] = external_pt"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"for split in dataset:\n",
" with open(f'data/{split}.json', 'w') as f:\n",
" json.dump(dataset[split], f)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['train_pt', 'validation_pt', 'test_pt', 'train_en', 'validation_en', 'test_en', 'external_en', 'external_pt'])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.keys()"
]
}
],
"metadata": {
"interpreter": {
"hash": "5550a332701dfaf727177dbb42680f614fbec3c4b4c54b659bea7821910aed98"
},
"kernelspec": {
"display_name": "Python 3.9.7 ('base')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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