Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
ArXiv:
License:
Upload process.ipynb
Browse files- process.ipynb +228 -0
process.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": 23,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 7440/7440 [02:42<00:00, 45.75it/s] \n"
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]
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}
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],
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"source": [
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"import json\n",
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"import os\n",
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"from tqdm import tqdm\n",
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"from pathlib import Path\n",
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"\n",
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"\n",
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"split = \"train\" # \"test\", \"eval\"\n",
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"\n",
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"def load_json_files(filepath):\n",
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" with open(filepath, 'r') as f:\n",
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" data = json.load(f)\n",
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" return data\n",
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"\n",
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"\n",
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"folders = [os.path.join('dataset', split, f) for f in os.listdir('dataset/'+ split )]\n",
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"data = []\n",
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"for folderpath in tqdm(folders):\n",
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" if not os.path.isdir(folderpath):\n",
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" continue\n",
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" files = [os.path.join(folderpath, f) for f in os.listdir(folderpath)]\n",
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" for filepath in files:\n",
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" if filepath.endswith('.json'):\n",
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" \n",
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" data.append({\"id\": str(Path(filepath).stem), **load_json_files(filepath)})\n",
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"\n",
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"\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": 24,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 624022/624022 [00:11<00:00, 53122.61it/s]\n"
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]
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}
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],
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"source": [
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"\n",
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"results = []\n",
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"for item in tqdm(data):\n",
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" # Focal class\n",
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" focal_class = \"class\" + \" \" + item[\"focal_class\"][\"identifier\"]\n",
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"\n",
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" if item[\"focal_class\"][\"superclass\"]:\n",
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" focal_class += \" \" + item[\"focal_class\"][\"superclass\"]\n",
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"\n",
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" if item[\"focal_class\"][\"interfaces\"]:\n",
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" focal_class += \" \" + item[\"focal_class\"][\"interfaces\"]\n",
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"\n",
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" focal_class += \" {\"\n",
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"\n",
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" indent = item[\"focal_method\"][\"body\"].split(\"\\n\")[-1][:-1]\n",
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"\n",
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" # Focal method\n",
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" focal_method = indent + item[\"focal_method\"][\"body\"]\n",
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"\n",
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" # Constructors\n",
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" constructors = []\n",
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" for method in item[\"focal_class\"][\"methods\"]:\n",
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" if method[\"constructor\"]:\n",
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" constructor = indent + method[\"full_signature\"] + \";\"\n",
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" constructors.append(constructor)\n",
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"\n",
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" # Methods\n",
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" methods = []\n",
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" for method in item[\"focal_class\"][\"methods\"]:\n",
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" if item[\"focal_method\"][\"full_signature\"] == method[\"full_signature\"]:\n",
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" continue\n",
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" #if method[\"testcase\"]:\n",
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" # continue\n",
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"\n",
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" if not method[\"constructor\"]:\n",
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" method_code = indent + method[\"full_signature\"] + \";\"\n",
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" methods.append(method_code)\n",
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"\n",
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" # Fields\n",
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" fields = []\n",
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" for field in item[\"focal_class\"][\"fields\"]:\n",
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" field_code = indent\n",
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" field_code += field[\"modifier\"] + \" \" + field[\"type\"] + \" \" + field[\"var_name\"] + \";\"\n",
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" fields.append(field_code)\n",
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"\n",
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"\n",
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" # TEST\n",
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" # Test class\n",
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" test_class = \"class\" + \" \" + item[\"test_class\"][\"identifier\"]\n",
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"\n",
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" if item[\"test_class\"][\"superclass\"]:\n",
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" test_class += \" \" + item[\"focal_class\"][\"superclass\"]\n",
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"\n",
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" if item[\"test_class\"][\"interfaces\"]:\n",
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" test_class += \" \" + item[\"focal_class\"][\"interfaces\"]\n",
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"\n",
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" test_class += \" {\"\n",
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"\n",
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" indent = item[\"test_case\"][\"body\"].split(\"\\n\")[-1][:-1]\n",
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" # Test case\n",
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" fields = []\n",
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" for field in item[\"test_class\"][\"fields\"]:\n",
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" field_code = indent\n",
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" field_code += field[\"modifier\"] + \" \" + field[\"type\"] + \" \" + field[\"var_name\"] + \";\"\n",
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" fields.append(field_code)\n",
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" test_case = indent + item[\"test_case\"][\"body\"]\n",
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"\n",
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" d = {\n",
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" 'id': item['id'],\n",
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" 't': test_case,\n",
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" 't_tc': \"\\n\\n\".join(filter(None, [test_class, \"\\n\".join(fields), test_case, \"}\"])),\n",
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" 'fm': focal_method,\n",
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" 'fm_fc': \"\\n\\n\".join(filter(None, [focal_class, focal_method, \"}\"])),\n",
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" 'fm_fc_c': \"\\n\\n\".join(filter(None, [focal_class, focal_method, \"\\n\".join(constructors), \"}\"])),\n",
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" 'fm_fc_c_m': \"\\n\\n\".join(filter(None, [focal_class, focal_method, \"\\n\".join(constructors), \"\\n\".join(methods), \"}\"])),\n",
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" 'fm_fc_c_m_f': \"\\n\\n\".join(filter(None, [focal_class, focal_method, \"\\n\".join(constructors), \"\\n\".join(methods), \"\\n\".join(fields), \"}\"])),\n",
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" }\n",
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" results.append(d)\n",
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"\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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"# sort by id\n",
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"results_sorted = sorted(results, key=lambda k: int(k['id']))"
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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": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"from datasets import Dataset\n",
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"train_dataset = Dataset.from_list(results_sorted)"
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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": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"from datasets import DatasetDict\n",
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"\n",
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"dataset_dict = DatasetDict({'train': train_dataset, 'test': test_dataset, 'validation': eval_dataset})"
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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": 30,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Creating parquet from Arrow format: 100%|██████████| 57/57 [00:00<00:00, 134.38ba/s]\n",
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"Uploading the dataset shards: 100%|██████████| 11/11 [01:26<00:00, 7.90s/it]\n",
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"Creating parquet from Arrow format: 100%|██████████| 40/40 [00:00<00:00, 155.49ba/s]\n",
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"Creating parquet from Arrow format: 100%|██████████| 40/40 [00:00<00:00, 139.08ba/s]\n",
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"Uploading the dataset shards: 100%|██████████| 2/2 [00:12<00:00, 6.28s/it]\n",
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"README.md: 100%|██████████| 21.0/21.0 [00:00<00:00, 8.60kB/s]\n"
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]
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}
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],
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"source": [
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"dataset_dict.push_to_hub('andstor/methods2test')"
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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": ".venv",
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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": "3.9.13"
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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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