update

.gitattributes

@@ -1,3 +1,4 @@
+*.jsonl filter=lfs diff=lfs merge=lfs -text
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text

data/arc_challenge.jsonl

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+size 240469

data/arc_easy.jsonl

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+oid sha256:376d29e4f199c6899ec3ea22f34f35e62a4b59dbf722d194b02f151b2dca7293
+size 423646

data/boolq.jsonl

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+size 6332634

data/hellaswag.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2dc59132b7c9ed7513eb428d8bde1f5214c24aaa196a404c05acee36cc5c9838
+size 15630002

notebooks/.ipynb_checkpoints/convert-lm-eval-harness-checkpoint.ipynb

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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/[email protected]/.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/[email protected]/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-aec62727e66b6615.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/[email protected]/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-e645cff193ea8a1d.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/[email protected]/.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'../data/{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/[email protected]/.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/[email protected]/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5/cache-b77c77fd8280863e.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/[email protected]/.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'../data/{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/[email protected]/.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/[email protected]/.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'../data/{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/[email protected]/.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/[email protected]/.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'../data/{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": 10,
+   "id": "b3b98d73-4729-40a1-a5ea-51a3bcfd7ffe",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "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/]**']' at /var/cr06_data/[email protected]/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[10], 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;43m../\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:1165\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   1159\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m LocalDatasetModuleFactoryWithScript(\n\u001b[1;32m   1160\u001b[0m         combined_path, download_mode\u001b[38;5;241m=\u001b[39mdownload_mode, dynamic_modules_path\u001b[38;5;241m=\u001b[39mdynamic_modules_path\n\u001b[1;32m   1161\u001b[0m     )\u001b[38;5;241m.\u001b[39mget_module()\n\u001b[1;32m   1162\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(path):\n\u001b[1;32m   1163\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mLocalDatasetModuleFactoryWithoutScript\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1164\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\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\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\u001b[43m \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\n\u001b[0;32m-> 1165\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1166\u001b[0m \u001b[38;5;66;03m# Try remotely\u001b[39;00m\n\u001b[1;32m   1167\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_relative_path(path) \u001b[38;5;129;01mand\u001b[39;00m path\u001b[38;5;241m.\u001b[39mcount(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
+      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:642\u001b[0m, in \u001b[0;36mLocalDatasetModuleFactoryWithoutScript.get_module\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    638\u001b[0m base_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_dir) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_dir \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath\n\u001b[1;32m    639\u001b[0m patterns \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    640\u001b[0m     sanitize_patterns(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_files) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_files \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 get_data_patterns_locally(base_path)\n\u001b[1;32m    641\u001b[0m )\n\u001b[0;32m--> 642\u001b[0m data_files \u001b[38;5;241m=\u001b[39m \u001b[43mDataFilesDict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_local_or_remote\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    643\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpatterns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    644\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbase_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    645\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallowed_extensions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mALL_ALLOWED_EXTENSIONS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    647\u001b[0m split_modules \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    648\u001b[0m     split: infer_module_for_data_files(data_files_list) \u001b[38;5;28;01mfor\u001b[39;00m split, data_files_list \u001b[38;5;129;01min\u001b[39;00m data_files\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    649\u001b[0m }\n\u001b[1;32m    650\u001b[0m module_name, builder_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(split_modules\u001b[38;5;241m.\u001b[39mvalues()))\n",
+      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/data_files.py:792\u001b[0m, in \u001b[0;36mDataFilesDict.from_local_or_remote\u001b[0;34m(cls, patterns, base_path, allowed_extensions, use_auth_token)\u001b[0m\n\u001b[1;32m    789\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m()\n\u001b[1;32m    790\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, patterns_for_key \u001b[38;5;129;01min\u001b[39;00m patterns\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m    791\u001b[0m     out[key] \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 792\u001b[0m         \u001b[43mDataFilesList\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_local_or_remote\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatterns_for_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbase_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m            \u001b[49m\u001b[43mallowed_extensions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallowed_extensions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\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    797\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    798\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(patterns_for_key, DataFilesList)\n\u001b[1;32m    799\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m patterns_for_key\n\u001b[1;32m    800\u001b[0m     )\n\u001b[1;32m    801\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/data_files.py:748\u001b[0m, in \u001b[0;36mDataFilesList.from_local_or_remote\u001b[0;34m(cls, patterns, base_path, allowed_extensions, use_auth_token)\u001b[0m\n\u001b[1;32m    739\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m    740\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_local_or_remote\u001b[39m(\n\u001b[1;32m    741\u001b[0m     \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    745\u001b[0m     use_auth_token: Optional[Union[\u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28mstr\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    746\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataFilesList\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    747\u001b[0m     base_path \u001b[38;5;241m=\u001b[39m base_path \u001b[38;5;28;01mif\u001b[39;00m base_path \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 \u001b[38;5;28mstr\u001b[39m(Path()\u001b[38;5;241m.\u001b[39mresolve())\n\u001b[0;32m--> 748\u001b[0m     data_files \u001b[38;5;241m=\u001b[39m \u001b[43mresolve_patterns_locally_or_by_urls\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpatterns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallowed_extensions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    749\u001b[0m     origin_metadata \u001b[38;5;241m=\u001b[39m _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token\u001b[38;5;241m=\u001b[39muse_auth_token)\n\u001b[1;32m    750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(data_files, origin_metadata)\n",
+      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/data_files.py:355\u001b[0m, in \u001b[0;36mresolve_patterns_locally_or_by_urls\u001b[0;34m(base_path, patterns, allowed_extensions)\u001b[0m\n\u001b[1;32m    353\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m allowed_extensions \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[1;32m    354\u001b[0m         error_msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m with any supported extension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(allowed_extensions)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 355\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(error_msg)\n\u001b[1;32m    356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data_files\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: 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/]**']' at /var/cr06_data/[email protected]/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('../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fe88a29-86df-4060-8e61-c39b88d2d10e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nebula-fav2",
+   "language": "python",
+   "name": "nebula-fav2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/convert-lm-eval-harness.ipynb

@@ -0,0 +1,242 @@
+{
+ "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/[email protected]/.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/[email protected]/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-aec62727e66b6615.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/[email protected]/.cache/huggingface/datasets/hellaswag/default/0.1.0/512a66dd8b1b1643ab4a48aa4f150d04c91680da6a4096498a5e5f799623d5ae/cache-e645cff193ea8a1d.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/[email protected]/.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'../data/{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/[email protected]/.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/[email protected]/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5/cache-b77c77fd8280863e.arrow\n",
+      "Loading cached shuffled indices for dataset at /home/[email protected]/.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'../data/{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/[email protected]/.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/[email protected]/.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'../data/{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/[email protected]/.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/[email protected]/.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'../data/{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": 10,
+   "id": "b3b98d73-4729-40a1-a5ea-51a3bcfd7ffe",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "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/]**']' at /var/cr06_data/[email protected]/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[10], 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;43m../\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:1165\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   1159\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m LocalDatasetModuleFactoryWithScript(\n\u001b[1;32m   1160\u001b[0m         combined_path, download_mode\u001b[38;5;241m=\u001b[39mdownload_mode, dynamic_modules_path\u001b[38;5;241m=\u001b[39mdynamic_modules_path\n\u001b[1;32m   1161\u001b[0m     )\u001b[38;5;241m.\u001b[39mget_module()\n\u001b[1;32m   1162\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(path):\n\u001b[1;32m   1163\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mLocalDatasetModuleFactoryWithoutScript\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1164\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\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\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\u001b[43m \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\n\u001b[0;32m-> 1165\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1166\u001b[0m \u001b[38;5;66;03m# Try remotely\u001b[39;00m\n\u001b[1;32m   1167\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_relative_path(path) \u001b[38;5;129;01mand\u001b[39;00m path\u001b[38;5;241m.\u001b[39mcount(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
+      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/load.py:642\u001b[0m, in \u001b[0;36mLocalDatasetModuleFactoryWithoutScript.get_module\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    638\u001b[0m base_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_dir) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_dir \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath\n\u001b[1;32m    639\u001b[0m patterns \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    640\u001b[0m     sanitize_patterns(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_files) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_files \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 get_data_patterns_locally(base_path)\n\u001b[1;32m    641\u001b[0m )\n\u001b[0;32m--> 642\u001b[0m data_files \u001b[38;5;241m=\u001b[39m \u001b[43mDataFilesDict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_local_or_remote\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    643\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpatterns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    644\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbase_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    645\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallowed_extensions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mALL_ALLOWED_EXTENSIONS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    647\u001b[0m split_modules \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    648\u001b[0m     split: infer_module_for_data_files(data_files_list) \u001b[38;5;28;01mfor\u001b[39;00m split, data_files_list \u001b[38;5;129;01min\u001b[39;00m data_files\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    649\u001b[0m }\n\u001b[1;32m    650\u001b[0m module_name, builder_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(split_modules\u001b[38;5;241m.\u001b[39mvalues()))\n",
+      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/data_files.py:792\u001b[0m, in \u001b[0;36mDataFilesDict.from_local_or_remote\u001b[0;34m(cls, patterns, base_path, allowed_extensions, use_auth_token)\u001b[0m\n\u001b[1;32m    789\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m()\n\u001b[1;32m    790\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, patterns_for_key \u001b[38;5;129;01min\u001b[39;00m patterns\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m    791\u001b[0m     out[key] \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 792\u001b[0m         \u001b[43mDataFilesList\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_local_or_remote\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatterns_for_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbase_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m            \u001b[49m\u001b[43mallowed_extensions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallowed_extensions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\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    797\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    798\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(patterns_for_key, DataFilesList)\n\u001b[1;32m    799\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m patterns_for_key\n\u001b[1;32m    800\u001b[0m     )\n\u001b[1;32m    801\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/data_files.py:748\u001b[0m, in \u001b[0;36mDataFilesList.from_local_or_remote\u001b[0;34m(cls, patterns, base_path, allowed_extensions, use_auth_token)\u001b[0m\n\u001b[1;32m    739\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m    740\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_local_or_remote\u001b[39m(\n\u001b[1;32m    741\u001b[0m     \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    745\u001b[0m     use_auth_token: Optional[Union[\u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28mstr\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    746\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataFilesList\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    747\u001b[0m     base_path \u001b[38;5;241m=\u001b[39m base_path \u001b[38;5;28;01mif\u001b[39;00m base_path \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 \u001b[38;5;28mstr\u001b[39m(Path()\u001b[38;5;241m.\u001b[39mresolve())\n\u001b[0;32m--> 748\u001b[0m     data_files \u001b[38;5;241m=\u001b[39m \u001b[43mresolve_patterns_locally_or_by_urls\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpatterns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallowed_extensions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    749\u001b[0m     origin_metadata \u001b[38;5;241m=\u001b[39m _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token\u001b[38;5;241m=\u001b[39muse_auth_token)\n\u001b[1;32m    750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(data_files, origin_metadata)\n",
+      "File \u001b[0;32m~/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/datasets/data_files.py:355\u001b[0m, in \u001b[0;36mresolve_patterns_locally_or_by_urls\u001b[0;34m(base_path, patterns, allowed_extensions)\u001b[0m\n\u001b[1;32m    353\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m allowed_extensions \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[1;32m    354\u001b[0m         error_msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m with any supported extension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(allowed_extensions)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 355\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(error_msg)\n\u001b[1;32m    356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data_files\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: 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/]**']' at /var/cr06_data/[email protected]/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('../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fe88a29-86df-4060-8e61-c39b88d2d10e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nebula-fav2",
+   "language": "python",
+   "name": "nebula-fav2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}