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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "c975c670-97cc-453e-bba6-3639cf8d5e89",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"from datasets import load_dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e818dca8-bd10-4b89-9fcd-5cd9252b4e07",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset hellaswag (/home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae)\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 461.50it/s]\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-aec62727e66b6615.arrow\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-e645cff193ea8a1d.arrow\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-70b4a183b087a019.arrow\n"
]
}
],
"source": [
"task_name = 'hellaswag'\n",
"data = load_dataset(task_name)\n",
"data.shuffle(seed=42)\n",
"with open(f'../{task_name}.jsonl', 'w') as f:\n",
" for i_item, item in enumerate(data['train']):\n",
" text = item['ctx'] + item['endings'][int(item['label'])]\n",
" f.write(\n",
" json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c42aae00-be85-4b64-9325-f6a6139c6ee6",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset boolq (/home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5)\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 845.37it/s]\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5/cache-b77c77fd8280863e.arrow\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5/cache-a6a68560e7f35615.arrow\n"
]
}
],
"source": [
"task_name = 'boolq'\n",
"data = load_dataset(task_name)\n",
"data.shuffle(seed=42)\n",
"with open(f'../{task_name}.jsonl', 'w') as f:\n",
" for i_item, item in enumerate(data['train']):\n",
" text = f\"{item['passage']}\\nQuestion: {item['question']}?\\nAnswer: {item['answer']}\"\n",
" f.write(\n",
" json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "878216f4-74e4-46ba-bfcd-c95348c10415",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.37k/5.37k [00:00<00:00, 38.7MB/s]\n",
"Downloading metadata: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.47k/4.47k [00:00<00:00, 32.7MB/s]\n",
"Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8.66k/8.66k [00:00<00:00, 53.6MB/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset ai2_arc/ARC-Challenge to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Challenge/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 681M/681M [00:17<00:00, 38.8MB/s]\n",
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset ai2_arc downloaded and prepared to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Challenge/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6. Subsequent calls will reuse this data.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 509.10it/s]\n"
]
}
],
"source": [
"task_name = 'arc_challenge'\n",
"data = load_dataset('ai2_arc', 'ARC-Challenge')\n",
"data.shuffle(seed=42)\n",
"with open(f'../{task_name}.jsonl', 'w') as f:\n",
" for i_item, item in enumerate(data['train']):\n",
" i_a = item['choices']['label'].index(item['answerKey'])\n",
" q = item['question']\n",
" a = item['choices']['text'][i_a]\n",
" text = \"Question: \" + q + \"\\nAnswer:\" + a\n",
" f.write(\n",
" json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "7cb51285-d5ce-4ae2-bd7f-ac15e87c4fb7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset ai2_arc/ARC-Easy to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Easy/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset ai2_arc downloaded and prepared to /home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Easy/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6. Subsequent calls will reuse this data.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 439.03it/s]\n"
]
}
],
"source": [
"task_name = 'arc_easy'\n",
"data = load_dataset('ai2_arc', 'ARC-Easy')\n",
"data.shuffle(seed=42)\n",
"with open(f'../{task_name}.jsonl', 'w') as f:\n",
" for i_item, item in enumerate(data['train']):\n",
" i_a = item['choices']['label'].index(item['answerKey'])\n",
" q = item['question']\n",
" a = item['choices']['text'][i_a]\n",
" text = \"Question: \" + q + \"\\nAnswer:\" + a\n",
" f.write(\n",
" json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "b3b98d73-4729-40a1-a5ea-51a3bcfd7ffe",
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "Couldn't find a dataset script at /var/cr06_data/jue@together.xyz/target-data/notebooks/juewang/target-data/target-data.py or any data file in the same directory. Couldn't find 'juewang/target-data' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['test[-._ 0-9/]**', '**[-._ 0-9/]test[-._ 0-9/]**', 'testing[-._ 0-9/]**', '**[-._ 0-9/]testing[-._ 0-9/]**', 'eval[-._ 0-9/]**', '**[-._ 0-9/]eval[-._ 0-9/]**', 'evaluation[-._ 0-9/]**', '**[-._ 0-9/]evaluation[-._ 0-9/]**'] in dataset repository juewang/target-data with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mjuewang/target-data\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msplit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:1785\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;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, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m 1780\u001b[0m verification_mode \u001b[38;5;241m=\u001b[39m VerificationMode(\n\u001b[1;32m 1781\u001b[0m (verification_mode \u001b[38;5;129;01mor\u001b[39;00m VerificationMode\u001b[38;5;241m.\u001b[39mBASIC_CHECKS) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m save_infos \u001b[38;5;28;01melse\u001b[39;00m VerificationMode\u001b[38;5;241m.\u001b[39mALL_CHECKS\n\u001b[1;32m 1782\u001b[0m )\n\u001b[1;32m 1784\u001b[0m \u001b[38;5;66;03m# Create a dataset builder\u001b[39;00m\n\u001b[0;32m-> 1785\u001b[0m builder_instance \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset_builder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1786\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1787\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1788\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1789\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1790\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1791\u001b[0m \u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1792\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1793\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1794\u001b[0m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1795\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1796\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1797\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mconfig_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1798\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1800\u001b[0m \u001b[38;5;66;03m# Return iterable dataset in case of streaming\u001b[39;00m\n\u001b[1;32m 1801\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m streaming:\n",
"File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:1514\u001b[0m, in \u001b[0;36mload_dataset_builder\u001b[0;34m(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m 1512\u001b[0m download_config \u001b[38;5;241m=\u001b[39m download_config\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m download_config \u001b[38;5;28;01melse\u001b[39;00m DownloadConfig()\n\u001b[1;32m 1513\u001b[0m download_config\u001b[38;5;241m.\u001b[39muse_auth_token \u001b[38;5;241m=\u001b[39m use_auth_token\n\u001b[0;32m-> 1514\u001b[0m dataset_module \u001b[38;5;241m=\u001b[39m \u001b[43mdataset_module_factory\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1515\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1516\u001b[0m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1517\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1518\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1519\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1520\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1521\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# Get dataset builder class from the processing script\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m builder_cls \u001b[38;5;241m=\u001b[39m import_main_class(dataset_module\u001b[38;5;241m.\u001b[39mmodule_path)\n",
"File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:1227\u001b[0m, in \u001b[0;36mdataset_module_factory\u001b[0;34m(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)\u001b[0m\n\u001b[1;32m 1225\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e1 \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e1, \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m):\n\u001b[0;32m-> 1227\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m 1228\u001b[0m \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 find a dataset script at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrelative_to_absolute_path(combined_path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or any data file in the same directory. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1229\u001b[0m \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 find \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m on the Hugging Face Hub either: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(e1)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me1\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1230\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1231\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e1 \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: Couldn't find a dataset script at /var/cr06_data/jue@together.xyz/target-data/notebooks/juewang/target-data/target-data.py or any data file in the same directory. Couldn't find 'juewang/target-data' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['test[-._ 0-9/]**', '**[-._ 0-9/]test[-._ 0-9/]**', 'testing[-._ 0-9/]**', '**[-._ 0-9/]testing[-._ 0-9/]**', 'eval[-._ 0-9/]**', '**[-._ 0-9/]eval[-._ 0-9/]**', 'evaluation[-._ 0-9/]**', '**[-._ 0-9/]evaluation[-._ 0-9/]**'] in dataset repository juewang/target-data with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']"
]
}
],
"source": [
"data = load_dataset('juewang/target-data', split='train')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "65d4b641-2b78-476f-89ac-6ecb48e7d044",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "raw",
"id": "1e0b7ea2-5112-4118-b4ab-f0df5ef2bdc6",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "nebula-fav2",
"language": "python",
"name": "nebula-fav2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
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|