{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c975c670-97cc-453e-bba6-3639cf8d5e89",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jue@together.xyz/miniconda3/envs/nebula-fav2/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import json\n",
"from datasets import load_dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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, 53.00it/s]\n"
]
}
],
"source": [
"task_name = 'hellaswag'\n",
"data = load_dataset(task_name)\n",
"data.shuffle(seed=42)\n",
"with open(f'../seed_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": 3,
"id": "c42aae00-be85-4b64-9325-f6a6139c6ee6",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.38k/3.38k [00:00<00:00, 32.1MB/s]\n",
"Downloading metadata: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.91k/1.91k [00:00<00:00, 20.0MB/s]\n",
"Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.60k/6.60k [00:00<00:00, 45.1MB/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset boolq/default to /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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"Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.24M/2.24M [00:00<00:00, 2.61MB/s]\u001b[A\n",
"Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:03<00:00, 1.85s/it]\n",
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset boolq downloaded and prepared to /home/jue@together.xyz/.cache/huggingface/datasets/boolq/default/0.1.0/bf0dd57da941c50de94ae3ce3cef7fea48c08f337a4b7aac484e9dddc5aa24e5. Subsequent calls will reuse this data.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 139.08it/s]\n"
]
}
],
"source": [
"task_name = 'boolq'\n",
"data = load_dataset(task_name)\n",
"data.shuffle(seed=42)\n",
"with open(f'../seed_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": 4,
"id": "878216f4-74e4-46ba-bfcd-c95348c10415",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset ai2_arc (/home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Challenge/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6)\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 624.09it/s]\n"
]
}
],
"source": [
"task_name = 'arc_challenge'\n",
"data = load_dataset('ai2_arc', 'ARC-Challenge')\n",
"data.shuffle(seed=42)\n",
"with open(f'../seed_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": 5,
"id": "7cb51285-d5ce-4ae2-bd7f-ac15e87c4fb7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset ai2_arc (/home/jue@together.xyz/.cache/huggingface/datasets/ai2_arc/ARC-Easy/1.0.0/1569c2591ea2683779581d9fb467203d9aa95543bb9b75dcfde5da92529fd7f6)\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 426.87it/s]\n"
]
}
],
"source": [
"task_name = 'arc_easy'\n",
"data = load_dataset('ai2_arc', 'ARC-Easy')\n",
"data.shuffle(seed=42)\n",
"with open(f'../seed_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": 6,
"id": "9347c339-4559-4111-bf44-ce2b624edc9e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset super_glue (/home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed)\n",
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1055.52it/s]\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed/cache-06e156489a3d17ef.arrow\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed/cache-f4bd523109c343ed.arrow\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/super_glue/copa/1.0.3/bb9675f958ebfee0d5d6dc5476fafe38c79123727a7258d515c450873dbdbbed/cache-b8d0981688b46f46.arrow\n"
]
}
],
"source": [
"task_name = 'copa'\n",
"data = load_dataset('super_glue', task_name)\n",
"data.shuffle(seed=42)\n",
"with open(f'../seed_data{task_name}.jsonl', 'w') as f:\n",
" for i_item, item in enumerate(data['train']):\n",
" i_a = item['label']\n",
" premise = item['premise']\n",
" question = item['question']\n",
" choice = item['choice1'] if i_a == 0 else item['choice2']\n",
" choice = choice[0].lower() + choice[1:]\n",
"\n",
" connector = {\n",
" \"cause\": \"because\",\n",
" \"effect\": \"therefore\",\n",
" }[question]\n",
"\n",
" text = premise.strip()[:-1] + f\" {connector}\" + ' ' + choice\n",
" f.write(\n",
" json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dc24ba3b-aa6a-4665-87aa-08471a87de9b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset piqa (/home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011)\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 910.95it/s]\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011/cache-64434cd628e6e9e0.arrow\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011/cache-97a6e3c466ced62d.arrow\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/piqa/plain_text/1.1.0/6c611c1a9bf220943c4174e117d3b660859665baf1d43156230116185312d011/cache-6b1e5924150382c7.arrow\n"
]
}
],
"source": [
"task_name = 'piqa'\n",
"data = load_dataset(task_name)\n",
"data.shuffle(seed=42)\n",
"with open(f'../seed_data{task_name}.jsonl', 'w') as f:\n",
" for i_item, item in enumerate(data['train']):\n",
" i_a = item['label']\n",
" goal = item['goal']\n",
" sol = item['sol1'] if i_a == 0 else item['sol2']\n",
"\n",
" text = \"Question: \" + goal + \"\\nAnswer: \" + sol\n",
" f.write(\n",
" json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "501744bd-0260-4a3c-89dc-df264ff3306e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset winogrande (/home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2)\n",
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1014.75it/s]\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2/cache-ebcf1ebbadaf243c.arrow\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2/cache-ea79c2b85e9d846f.arrow\n",
"Loading cached shuffled indices for dataset at /home/jue@together.xyz/.cache/huggingface/datasets/winogrande/winogrande_xl/1.1.0/a826c3d3506aefe0e9e9390dcb53271070536586bab95849876b2c1743df56e2/cache-5aca6c830a1dfa33.arrow\n"
]
}
],
"source": [
"task_name = 'winogrande'\n",
"data = load_dataset(task_name, 'winogrande_xl')\n",
"data.shuffle(seed=42)\n",
"with open(f'../seed_data{task_name}.jsonl', 'w') as f:\n",
" for i_item, item in enumerate(data['train']):\n",
" i_a = item['answer']\n",
" sentence = item['sentence']\n",
" option = item['option1'] if i_a == 1 else item['option2']\n",
" \n",
" text = sentence.replace('_', option)\n",
" f.write(\n",
" json.dumps({'text': text, 'source': task_name}) + '\\n'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "65d4b641-2b78-476f-89ac-6ecb48e7d044",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset json (/home/jue@together.xyz/.cache/huggingface/datasets/juewang___json/juewang--target-data-72e09ed206c54e99/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)\n"
]
}
],
"source": [
"dataset = load_dataset(\"juewang/target-data\", data_files='seed_data/*.jsonl', split='train')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a0554b4f-c5c0-4621-8b91-3daf05e6fbdf",
"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
}