polls/test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in /usr/local/lib/python3.10/site-packages (2.19.2)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/site-packages (from datasets) (1.26.4)\n",
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      "Requirement already satisfied: fsspec[http]<=2024.3.1,>=2023.1.0 in /usr/local/lib/python3.10/site-packages (from datasets) (2024.3.1)\n",
      "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/site-packages (from datasets) (0.6)\n",
      "Requirement already satisfied: requests>=2.32.1 in /usr/local/lib/python3.10/site-packages (from datasets) (2.32.3)\n",
      "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/site-packages (from datasets) (0.3.8)\n",
      "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/site-packages (from datasets) (3.9.5)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/site-packages (from datasets) (3.14.0)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/site-packages (from datasets) (24.0)\n",
      "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/site-packages (from datasets) (0.70.16)\n",
      "Requirement already satisfied: huggingface-hub>=0.21.2 in /usr/local/lib/python3.10/site-packages (from datasets) (0.23.3)\n",
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      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/site-packages (from datasets) (6.0.1)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.10/site-packages (from datasets) (2.2.2)\n",
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      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/site-packages (from requests>=2.32.1->datasets) (2.2.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/site-packages (from pandas->datasets) (2024.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/site-packages (from pandas->datasets) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/site-packages (from pandas->datasets) (2024.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/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 pandas as pd\n",
    "from datasets import Dataset\n",
    "\n",
    "# QAペアのデータセットを作成\n",
    "data = {\n",
    "    \"question\": [\"What is the capital of France?\", \"Who wrote 1984?\", \"What is the largest planet in our solar system?\"],\n",
    "    \"answer\": [\"Paris\", \"George Orwell\", \"Jupiter\"]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "dataset = Dataset.from_pandas(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "Some weights of DistilBertForQuestionAnswering were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForQuestionAnswering, Trainer, TrainingArguments\n",
    "\n",
    "model_name = \"distilbert-base-uncased\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForQuestionAnswering.from_pretrained(model_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Map: 100%|██████████| 3/3 [00:00<00:00, 576.25 examples/s]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The model did not return a loss from the inputs, only the following keys: start_logits,end_logits. For reference, the inputs it received are input_ids,attention_mask.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/user/app/polls/test.ipynb Cell 4\u001b[0m line \u001b[0;36m2\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=9'>10</a>\u001b[0m training_args \u001b[39m=\u001b[39m TrainingArguments(\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=10'>11</a>\u001b[0m     output_dir\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m./results\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=11'>12</a>\u001b[0m     evaluation_strategy\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mepoch\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=15'>16</a>\u001b[0m     weight_decay\u001b[39m=\u001b[39m\u001b[39m0.01\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=16'>17</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=18'>19</a>\u001b[0m trainer \u001b[39m=\u001b[39m Trainer(\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=19'>20</a>\u001b[0m     model\u001b[39m=\u001b[39mmodel,\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=20'>21</a>\u001b[0m     args\u001b[39m=\u001b[39mtraining_args,\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=21'>22</a>\u001b[0m     train_dataset\u001b[39m=\u001b[39mtokenized_dataset,\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=22'>23</a>\u001b[0m     eval_dataset\u001b[39m=\u001b[39mtokenized_dataset,\n\u001b[1;32m     <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=23'>24</a>\u001b[0m )\n\u001b[0;32m---> <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=25'>26</a>\u001b[0m trainer\u001b[39m.\u001b[39;49mtrain()\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/transformers/trainer.py:1885\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1883\u001b[0m         hf_hub_utils\u001b[39m.\u001b[39menable_progress_bars()\n\u001b[1;32m   1884\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1885\u001b[0m     \u001b[39mreturn\u001b[39;00m inner_training_loop(\n\u001b[1;32m   1886\u001b[0m         args\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m   1887\u001b[0m         resume_from_checkpoint\u001b[39m=\u001b[39;49mresume_from_checkpoint,\n\u001b[1;32m   1888\u001b[0m         trial\u001b[39m=\u001b[39;49mtrial,\n\u001b[1;32m   1889\u001b[0m         ignore_keys_for_eval\u001b[39m=\u001b[39;49mignore_keys_for_eval,\n\u001b[1;32m   1890\u001b[0m     )\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/transformers/trainer.py:2216\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2213\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcontrol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_handler\u001b[39m.\u001b[39mon_step_begin(args, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcontrol)\n\u001b[1;32m   2215\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maccelerator\u001b[39m.\u001b[39maccumulate(model):\n\u001b[0;32m-> 2216\u001b[0m     tr_loss_step \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtraining_step(model, inputs)\n\u001b[1;32m   2218\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m   2219\u001b[0m     args\u001b[39m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m   2220\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m is_torch_xla_available()\n\u001b[1;32m   2221\u001b[0m     \u001b[39mand\u001b[39;00m (torch\u001b[39m.\u001b[39misnan(tr_loss_step) \u001b[39mor\u001b[39;00m torch\u001b[39m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m   2222\u001b[0m ):\n\u001b[1;32m   2223\u001b[0m     \u001b[39m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m   2224\u001b[0m     tr_loss \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m tr_loss \u001b[39m/\u001b[39m (\u001b[39m1\u001b[39m \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mglobal_step \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_globalstep_last_logged)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/transformers/trainer.py:3238\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   3235\u001b[0m     \u001b[39mreturn\u001b[39;00m loss_mb\u001b[39m.\u001b[39mreduce_mean()\u001b[39m.\u001b[39mdetach()\u001b[39m.\u001b[39mto(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39margs\u001b[39m.\u001b[39mdevice)\n\u001b[1;32m   3237\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcompute_loss_context_manager():\n\u001b[0;32m-> 3238\u001b[0m     loss \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcompute_loss(model, inputs)\n\u001b[1;32m   3240\u001b[0m \u001b[39mdel\u001b[39;00m inputs\n\u001b[1;32m   3241\u001b[0m torch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mempty_cache()\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/transformers/trainer.py:3282\u001b[0m, in \u001b[0;36mTrainer.compute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m   3280\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   3281\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(outputs, \u001b[39mdict\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mloss\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m outputs:\n\u001b[0;32m-> 3282\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   3283\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mThe model did not return a loss from the inputs, only the following keys: \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   3284\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m,\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin(outputs\u001b[39m.\u001b[39mkeys())\u001b[39m}\u001b[39;00m\u001b[39m. For reference, the inputs it received are \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m,\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin(inputs\u001b[39m.\u001b[39mkeys())\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   3285\u001b[0m         )\n\u001b[1;32m   3286\u001b[0m     \u001b[39m# We don't use .loss here since the model may return tuples instead of ModelOutput.\u001b[39;00m\n\u001b[1;32m   3287\u001b[0m     loss \u001b[39m=\u001b[39m outputs[\u001b[39m\"\u001b[39m\u001b[39mloss\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(outputs, \u001b[39mdict\u001b[39m) \u001b[39melse\u001b[39;00m outputs[\u001b[39m0\u001b[39m]\n",
      "\u001b[0;31mValueError\u001b[0m: The model did not return a loss from the inputs, only the following keys: start_logits,end_logits. For reference, the inputs it received are input_ids,attention_mask."
     ]
    }
   ],
   "source": [
    "def preprocess_function(examples):\n",
    "    questions = examples[\"question\"]\n",
    "    answers = examples[\"answer\"]\n",
    "    inputs = tokenizer(questions, truncation=True, padding=True)\n",
    "    inputs[\"labels\"] = tokenizer(answers, truncation=True, padding=True)[\"input_ids\"]\n",
    "    return inputs\n",
    "\n",
    "tokenized_dataset = dataset.map(preprocess_function, batched=True)\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=\"./results\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=2,\n",
    "    num_train_epochs=3,\n",
    "    weight_decay=0.01,\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_dataset,\n",
    "    eval_dataset=tokenized_dataset,\n",
    ")\n",
    "\n",
    "trainer.train()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in /usr/local/lib/python3.10/site-packages (4.41.2)\n",
      "Requirement already satisfied: datasets in /usr/local/lib/python3.10/site-packages (2.19.2)\n",
      "Collecting faiss-cpu\n",
      "  Downloading faiss_cpu-1.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.0 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m64.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/site-packages (from transformers) (6.0.1)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/site-packages (from transformers) (2024.5.15)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.10/site-packages (from transformers) (2.32.3)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/site-packages (from transformers) (3.14.0)\n",
      "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/site-packages (from transformers) (0.19.1)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/site-packages (from transformers) (0.23.3)\n",
      "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/site-packages (from transformers) (4.66.4)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/site-packages (from transformers) (1.26.4)\n",
      "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/site-packages (from transformers) (0.4.3)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/site-packages (from transformers) (24.0)\n",
      "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/site-packages (from datasets) (0.70.16)\n",
      "Requirement already satisfied: fsspec[http]<=2024.3.1,>=2023.1.0 in /usr/local/lib/python3.10/site-packages (from datasets) (2024.3.1)\n",
      "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/site-packages (from datasets) (0.6)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.10/site-packages (from datasets) (2.2.2)\n",
      "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/site-packages (from datasets) (3.4.1)\n",
      "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/site-packages (from datasets) (16.1.0)\n",
      "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/site-packages (from datasets) (0.3.8)\n",
      "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/site-packages (from datasets) (3.9.5)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (4.0.3)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (1.4.1)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (23.2.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (1.3.1)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (1.9.4)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (6.0.5)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.10.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/site-packages (from requests->transformers) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/site-packages (from requests->transformers) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/site-packages (from requests->transformers) (2.2.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/site-packages (from requests->transformers) (2024.2.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/site-packages (from pandas->datasets) (2024.1)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/site-packages (from pandas->datasets) (2024.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/site-packages (from pandas->datasets) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
      "Installing collected packages: faiss-cpu\n",
      "Successfully installed faiss-cpu-1.8.0\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers datasets faiss-cpu\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Loading wiki_dpr requires you to execute the dataset script in that repo on your local machine. Make sure you have read the code there to avoid malicious use, then set the option `trust_remote_code=True` to remove this error.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/user/app/polls/test.ipynb Cell 6\u001b[0m line \u001b[0;36m4\n\u001b[1;32m      <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W5sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdatasets\u001b[39;00m \u001b[39mimport\u001b[39;00m load_dataset\n\u001b[1;32m      <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W5sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# データセットのロード\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W5sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m dataset \u001b[39m=\u001b[39m load_dataset(\u001b[39m'\u001b[39;49m\u001b[39mwiki_dpr\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mpsgs_w100\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/load.py:2592\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, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[1;32m   2587\u001b[0m verification_mode \u001b[39m=\u001b[39m VerificationMode(\n\u001b[1;32m   2588\u001b[0m     (verification_mode \u001b[39mor\u001b[39;00m VerificationMode\u001b[39m.\u001b[39mBASIC_CHECKS) \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m save_infos \u001b[39melse\u001b[39;00m VerificationMode\u001b[39m.\u001b[39mALL_CHECKS\n\u001b[1;32m   2589\u001b[0m )\n\u001b[1;32m   2591\u001b[0m \u001b[39m# Create a dataset builder\u001b[39;00m\n\u001b[0;32m-> 2592\u001b[0m builder_instance \u001b[39m=\u001b[39m load_dataset_builder(\n\u001b[1;32m   2593\u001b[0m     path\u001b[39m=\u001b[39;49mpath,\n\u001b[1;32m   2594\u001b[0m     name\u001b[39m=\u001b[39;49mname,\n\u001b[1;32m   2595\u001b[0m     data_dir\u001b[39m=\u001b[39;49mdata_dir,\n\u001b[1;32m   2596\u001b[0m     data_files\u001b[39m=\u001b[39;49mdata_files,\n\u001b[1;32m   2597\u001b[0m     cache_dir\u001b[39m=\u001b[39;49mcache_dir,\n\u001b[1;32m   2598\u001b[0m     features\u001b[39m=\u001b[39;49mfeatures,\n\u001b[1;32m   2599\u001b[0m     download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m   2600\u001b[0m     download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m   2601\u001b[0m     revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m   2602\u001b[0m     token\u001b[39m=\u001b[39;49mtoken,\n\u001b[1;32m   2603\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m   2604\u001b[0m     trust_remote_code\u001b[39m=\u001b[39;49mtrust_remote_code,\n\u001b[1;32m   2605\u001b[0m     _require_default_config_name\u001b[39m=\u001b[39;49mname \u001b[39mis\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m   2606\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mconfig_kwargs,\n\u001b[1;32m   2607\u001b[0m )\n\u001b[1;32m   2609\u001b[0m \u001b[39m# Return iterable dataset in case of streaming\u001b[39;00m\n\u001b[1;32m   2610\u001b[0m \u001b[39mif\u001b[39;00m streaming:\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/load.py:2264\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, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)\u001b[0m\n\u001b[1;32m   2262\u001b[0m     download_config \u001b[39m=\u001b[39m download_config\u001b[39m.\u001b[39mcopy() \u001b[39mif\u001b[39;00m download_config \u001b[39melse\u001b[39;00m DownloadConfig()\n\u001b[1;32m   2263\u001b[0m     download_config\u001b[39m.\u001b[39mstorage_options\u001b[39m.\u001b[39mupdate(storage_options)\n\u001b[0;32m-> 2264\u001b[0m dataset_module \u001b[39m=\u001b[39m dataset_module_factory(\n\u001b[1;32m   2265\u001b[0m     path,\n\u001b[1;32m   2266\u001b[0m     revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m   2267\u001b[0m     download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m   2268\u001b[0m     download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m   2269\u001b[0m     data_dir\u001b[39m=\u001b[39;49mdata_dir,\n\u001b[1;32m   2270\u001b[0m     data_files\u001b[39m=\u001b[39;49mdata_files,\n\u001b[1;32m   2271\u001b[0m     cache_dir\u001b[39m=\u001b[39;49mcache_dir,\n\u001b[1;32m   2272\u001b[0m     trust_remote_code\u001b[39m=\u001b[39;49mtrust_remote_code,\n\u001b[1;32m   2273\u001b[0m     _require_default_config_name\u001b[39m=\u001b[39;49m_require_default_config_name,\n\u001b[1;32m   2274\u001b[0m     _require_custom_configs\u001b[39m=\u001b[39;49m\u001b[39mbool\u001b[39;49m(config_kwargs),\n\u001b[1;32m   2275\u001b[0m )\n\u001b[1;32m   2276\u001b[0m \u001b[39m# Get dataset builder class from the processing script\u001b[39;00m\n\u001b[1;32m   2277\u001b[0m builder_kwargs \u001b[39m=\u001b[39m dataset_module\u001b[39m.\u001b[39mbuilder_kwargs\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/load.py:1915\u001b[0m, in \u001b[0;36mdataset_module_factory\u001b[0;34m(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)\u001b[0m\n\u001b[1;32m   1910\u001b[0m             \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(e1, \u001b[39mFileNotFoundError\u001b[39;00m):\n\u001b[1;32m   1911\u001b[0m                 \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m   1912\u001b[0m                     \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCouldn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt find a dataset script at \u001b[39m\u001b[39m{\u001b[39;00mrelative_to_absolute_path(combined_path)\u001b[39m}\u001b[39;00m\u001b[39m or any data file in the same directory. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1913\u001b[0m                     \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCouldn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt find \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mpath\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m on the Hugging Face Hub either: \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(e1)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m: \u001b[39m\u001b[39m{\u001b[39;00me1\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1914\u001b[0m                 ) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 1915\u001b[0m             \u001b[39mraise\u001b[39;00m e1 \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1916\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1917\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m   1918\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCouldn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt find a dataset script at \u001b[39m\u001b[39m{\u001b[39;00mrelative_to_absolute_path(combined_path)\u001b[39m}\u001b[39;00m\u001b[39m or any data file in the same directory.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1919\u001b[0m     )\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/load.py:1888\u001b[0m, in \u001b[0;36mdataset_module_factory\u001b[0;34m(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)\u001b[0m\n\u001b[1;32m   1879\u001b[0m             \u001b[39mpass\u001b[39;00m\n\u001b[1;32m   1880\u001b[0m     \u001b[39m# Otherwise we must use the dataset script if the user trusts it\u001b[39;00m\n\u001b[1;32m   1881\u001b[0m     \u001b[39mreturn\u001b[39;00m HubDatasetModuleFactoryWithScript(\n\u001b[1;32m   1882\u001b[0m         path,\n\u001b[1;32m   1883\u001b[0m         revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m   1884\u001b[0m         download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m   1885\u001b[0m         download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m   1886\u001b[0m         dynamic_modules_path\u001b[39m=\u001b[39;49mdynamic_modules_path,\n\u001b[1;32m   1887\u001b[0m         trust_remote_code\u001b[39m=\u001b[39;49mtrust_remote_code,\n\u001b[0;32m-> 1888\u001b[0m     )\u001b[39m.\u001b[39;49mget_module()\n\u001b[1;32m   1889\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1890\u001b[0m     \u001b[39mreturn\u001b[39;00m HubDatasetModuleFactoryWithoutScript(\n\u001b[1;32m   1891\u001b[0m         path,\n\u001b[1;32m   1892\u001b[0m         revision\u001b[39m=\u001b[39mrevision,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1896\u001b[0m         download_mode\u001b[39m=\u001b[39mdownload_mode,\n\u001b[1;32m   1897\u001b[0m     )\u001b[39m.\u001b[39mget_module()\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/load.py:1537\u001b[0m, in \u001b[0;36mHubDatasetModuleFactoryWithScript.get_module\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1526\u001b[0m         _create_importable_file(\n\u001b[1;32m   1527\u001b[0m             local_path\u001b[39m=\u001b[39mlocal_path,\n\u001b[1;32m   1528\u001b[0m             local_imports\u001b[39m=\u001b[39mlocal_imports,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1534\u001b[0m             download_mode\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdownload_mode,\n\u001b[1;32m   1535\u001b[0m         )\n\u001b[1;32m   1536\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1537\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   1538\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mLoading \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m requires you to execute the dataset script in that\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1539\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39m repo on your local machine. Make sure you have read the code there to avoid malicious use, then\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1540\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39m set the option `trust_remote_code=True` to remove this error.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1541\u001b[0m         )\n\u001b[1;32m   1542\u001b[0m module_path, \u001b[39mhash\u001b[39m \u001b[39m=\u001b[39m _load_importable_file(\n\u001b[1;32m   1543\u001b[0m     dynamic_modules_path\u001b[39m=\u001b[39mdynamic_modules_path,\n\u001b[1;32m   1544\u001b[0m     module_namespace\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mdatasets\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m   1545\u001b[0m     subdirectory_name\u001b[39m=\u001b[39m\u001b[39mhash\u001b[39m,\n\u001b[1;32m   1546\u001b[0m     name\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname,\n\u001b[1;32m   1547\u001b[0m )\n\u001b[1;32m   1548\u001b[0m \u001b[39m# make the new module to be noticed by the import system\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: Loading wiki_dpr requires you to execute the dataset script in that repo on your local machine. Make sure you have read the code there to avoid malicious use, then set the option `trust_remote_code=True` to remove this error."
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "# データセットのロード\n",
    "dataset = load_dataset('wiki_dpr', 'psgs_w100')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "BuilderConfig 'psgs_w100' not found. Available: ['psgs_w100.nq.exact', 'psgs_w100.nq.compressed', 'psgs_w100.nq.no_index', 'psgs_w100.multiset.exact', 'psgs_w100.multiset.compressed', 'psgs_w100.multiset.no_index', 'psgs_w100.nq.exact.no_embeddings', 'psgs_w100.nq.compressed.no_embeddings', 'psgs_w100.nq.no_index.no_embeddings', 'psgs_w100.multiset.exact.no_embeddings', 'psgs_w100.multiset.compressed.no_embeddings', 'psgs_w100.multiset.no_index.no_embeddings']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/user/app/polls/test.ipynb Cell 7\u001b[0m line \u001b[0;36m4\n\u001b[1;32m      <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdatasets\u001b[39;00m \u001b[39mimport\u001b[39;00m load_dataset\n\u001b[1;32m      <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# データセットのロード\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m dataset \u001b[39m=\u001b[39m load_dataset(\u001b[39m'\u001b[39;49m\u001b[39mwiki_dpr\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mpsgs_w100\u001b[39;49m\u001b[39m'\u001b[39;49m, trust_remote_code\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/load.py:2592\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, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[1;32m   2587\u001b[0m verification_mode \u001b[39m=\u001b[39m VerificationMode(\n\u001b[1;32m   2588\u001b[0m     (verification_mode \u001b[39mor\u001b[39;00m VerificationMode\u001b[39m.\u001b[39mBASIC_CHECKS) \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m save_infos \u001b[39melse\u001b[39;00m VerificationMode\u001b[39m.\u001b[39mALL_CHECKS\n\u001b[1;32m   2589\u001b[0m )\n\u001b[1;32m   2591\u001b[0m \u001b[39m# Create a dataset builder\u001b[39;00m\n\u001b[0;32m-> 2592\u001b[0m builder_instance \u001b[39m=\u001b[39m load_dataset_builder(\n\u001b[1;32m   2593\u001b[0m     path\u001b[39m=\u001b[39;49mpath,\n\u001b[1;32m   2594\u001b[0m     name\u001b[39m=\u001b[39;49mname,\n\u001b[1;32m   2595\u001b[0m     data_dir\u001b[39m=\u001b[39;49mdata_dir,\n\u001b[1;32m   2596\u001b[0m     data_files\u001b[39m=\u001b[39;49mdata_files,\n\u001b[1;32m   2597\u001b[0m     cache_dir\u001b[39m=\u001b[39;49mcache_dir,\n\u001b[1;32m   2598\u001b[0m     features\u001b[39m=\u001b[39;49mfeatures,\n\u001b[1;32m   2599\u001b[0m     download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m   2600\u001b[0m     download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m   2601\u001b[0m     revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m   2602\u001b[0m     token\u001b[39m=\u001b[39;49mtoken,\n\u001b[1;32m   2603\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m   2604\u001b[0m     trust_remote_code\u001b[39m=\u001b[39;49mtrust_remote_code,\n\u001b[1;32m   2605\u001b[0m     _require_default_config_name\u001b[39m=\u001b[39;49mname \u001b[39mis\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m   2606\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mconfig_kwargs,\n\u001b[1;32m   2607\u001b[0m )\n\u001b[1;32m   2609\u001b[0m \u001b[39m# Return iterable dataset in case of streaming\u001b[39;00m\n\u001b[1;32m   2610\u001b[0m \u001b[39mif\u001b[39;00m streaming:\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/load.py:2301\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, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)\u001b[0m\n\u001b[1;32m   2299\u001b[0m builder_cls \u001b[39m=\u001b[39m get_dataset_builder_class(dataset_module, dataset_name\u001b[39m=\u001b[39mdataset_name)\n\u001b[1;32m   2300\u001b[0m \u001b[39m# Instantiate the dataset builder\u001b[39;00m\n\u001b[0;32m-> 2301\u001b[0m builder_instance: DatasetBuilder \u001b[39m=\u001b[39m builder_cls(\n\u001b[1;32m   2302\u001b[0m     cache_dir\u001b[39m=\u001b[39;49mcache_dir,\n\u001b[1;32m   2303\u001b[0m     dataset_name\u001b[39m=\u001b[39;49mdataset_name,\n\u001b[1;32m   2304\u001b[0m     config_name\u001b[39m=\u001b[39;49mconfig_name,\n\u001b[1;32m   2305\u001b[0m     data_dir\u001b[39m=\u001b[39;49mdata_dir,\n\u001b[1;32m   2306\u001b[0m     data_files\u001b[39m=\u001b[39;49mdata_files,\n\u001b[1;32m   2307\u001b[0m     \u001b[39mhash\u001b[39;49m\u001b[39m=\u001b[39;49mdataset_module\u001b[39m.\u001b[39;49mhash,\n\u001b[1;32m   2308\u001b[0m     info\u001b[39m=\u001b[39;49minfo,\n\u001b[1;32m   2309\u001b[0m     features\u001b[39m=\u001b[39;49mfeatures,\n\u001b[1;32m   2310\u001b[0m     token\u001b[39m=\u001b[39;49mtoken,\n\u001b[1;32m   2311\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m   2312\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mbuilder_kwargs,\n\u001b[1;32m   2313\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mconfig_kwargs,\n\u001b[1;32m   2314\u001b[0m )\n\u001b[1;32m   2315\u001b[0m builder_instance\u001b[39m.\u001b[39m_use_legacy_cache_dir_if_possible(dataset_module)\n\u001b[1;32m   2317\u001b[0m \u001b[39mreturn\u001b[39;00m builder_instance\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/builder.py:374\u001b[0m, in \u001b[0;36mDatasetBuilder.__init__\u001b[0;34m(self, cache_dir, dataset_name, config_name, hash, base_path, info, features, token, use_auth_token, repo_id, data_files, data_dir, storage_options, writer_batch_size, name, **config_kwargs)\u001b[0m\n\u001b[1;32m    372\u001b[0m     config_kwargs[\u001b[39m\"\u001b[39m\u001b[39mdata_dir\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m data_dir\n\u001b[1;32m    373\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig_kwargs \u001b[39m=\u001b[39m config_kwargs\n\u001b[0;32m--> 374\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig_id \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_builder_config(\n\u001b[1;32m    375\u001b[0m     config_name\u001b[39m=\u001b[39;49mconfig_name,\n\u001b[1;32m    376\u001b[0m     custom_features\u001b[39m=\u001b[39;49mfeatures,\n\u001b[1;32m    377\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mconfig_kwargs,\n\u001b[1;32m    378\u001b[0m )\n\u001b[1;32m    380\u001b[0m \u001b[39m# prepare info: DatasetInfo are a standardized dataclass across all datasets\u001b[39;00m\n\u001b[1;32m    381\u001b[0m \u001b[39m# Prefill datasetinfo\u001b[39;00m\n\u001b[1;32m    382\u001b[0m \u001b[39mif\u001b[39;00m info \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    383\u001b[0m     \u001b[39m# TODO FOR PACKAGED MODULES IT IMPORTS DATA FROM src/packaged_modules which doesn't make sense\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/builder.py:599\u001b[0m, in \u001b[0;36mDatasetBuilder._create_builder_config\u001b[0;34m(self, config_name, custom_features, **config_kwargs)\u001b[0m\n\u001b[1;32m    597\u001b[0m     builder_config \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuilder_configs\u001b[39m.\u001b[39mget(config_name)\n\u001b[1;32m    598\u001b[0m     \u001b[39mif\u001b[39;00m builder_config \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mBUILDER_CONFIGS:\n\u001b[0;32m--> 599\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    600\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mBuilderConfig \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mconfig_name\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Available: \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlist\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuilder_configs\u001b[39m.\u001b[39mkeys())\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m    601\u001b[0m         )\n\u001b[1;32m    603\u001b[0m \u001b[39m# if not using an existing config, then create a new config on the fly\u001b[39;00m\n\u001b[1;32m    604\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m builder_config:\n",
      "\u001b[0;31mValueError\u001b[0m: BuilderConfig 'psgs_w100' not found. Available: ['psgs_w100.nq.exact', 'psgs_w100.nq.compressed', 'psgs_w100.nq.no_index', 'psgs_w100.multiset.exact', 'psgs_w100.multiset.compressed', 'psgs_w100.multiset.no_index', 'psgs_w100.nq.exact.no_embeddings', 'psgs_w100.nq.compressed.no_embeddings', 'psgs_w100.nq.no_index.no_embeddings', 'psgs_w100.multiset.exact.no_embeddings', 'psgs_w100.multiset.compressed.no_embeddings', 'psgs_w100.multiset.no_index.no_embeddings']"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "# データセットのロード\n",
    "dataset = load_dataset('wiki_dpr', 'psgs_w100', trust_remote_code=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data:   0%|          | 0/157 [02:34<?, ?files/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/tqdm/contrib/concurrent.py:51\u001b[0m, in \u001b[0;36m_executor_map\u001b[0;34m(PoolExecutor, fn, *iterables, **tqdm_kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[39mwith\u001b[39;00m PoolExecutor(max_workers\u001b[39m=\u001b[39mmax_workers, initializer\u001b[39m=\u001b[39mtqdm_class\u001b[39m.\u001b[39mset_lock,\n\u001b[1;32m     50\u001b[0m                   initargs\u001b[39m=\u001b[39m(lk,)) \u001b[39mas\u001b[39;00m ex:\n\u001b[0;32m---> 51\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39;49m(tqdm_class(ex\u001b[39m.\u001b[39;49mmap(fn, \u001b[39m*\u001b[39;49miterables, chunksize\u001b[39m=\u001b[39;49mchunksize), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs))\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1180\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m     \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m iterable:\n\u001b[1;32m   1182\u001b[0m         \u001b[39myield\u001b[39;00m obj\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/concurrent/futures/_base.py:621\u001b[0m, in \u001b[0;36mExecutor.map.<locals>.result_iterator\u001b[0;34m()\u001b[0m\n\u001b[1;32m    620\u001b[0m \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 621\u001b[0m     \u001b[39myield\u001b[39;00m _result_or_cancel(fs\u001b[39m.\u001b[39;49mpop())\n\u001b[1;32m    622\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/concurrent/futures/_base.py:319\u001b[0m, in \u001b[0;36m_result_or_cancel\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 319\u001b[0m     \u001b[39mreturn\u001b[39;00m fut\u001b[39m.\u001b[39;49mresult(timeout)\n\u001b[1;32m    320\u001b[0m \u001b[39mfinally\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/concurrent/futures/_base.py:453\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n\u001b[0;32m--> 453\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_condition\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m    455\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/threading.py:320\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    319\u001b[0m \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 320\u001b[0m     waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[1;32m    321\u001b[0m     gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/home/user/app/polls/test.ipynb Cell 8\u001b[0m line \u001b[0;36m4\n\u001b[1;32m      <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#X10sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdatasets\u001b[39;00m \u001b[39mimport\u001b[39;00m load_dataset\n\u001b[1;32m      <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#X10sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# データセットのロード\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://kenken999-fastapi-django-main--1027.hf.space/home/user/app/polls/test.ipynb#X10sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m dataset \u001b[39m=\u001b[39m load_dataset(\u001b[39m'\u001b[39;49m\u001b[39mwiki_dpr\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mpsgs_w100.nq.exact\u001b[39;49m\u001b[39m'\u001b[39;49m, trust_remote_code\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/load.py:2614\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, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[1;32m   2611\u001b[0m     \u001b[39mreturn\u001b[39;00m builder_instance\u001b[39m.\u001b[39mas_streaming_dataset(split\u001b[39m=\u001b[39msplit)\n\u001b[1;32m   2613\u001b[0m \u001b[39m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 2614\u001b[0m builder_instance\u001b[39m.\u001b[39;49mdownload_and_prepare(\n\u001b[1;32m   2615\u001b[0m     download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m   2616\u001b[0m     download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m   2617\u001b[0m     verification_mode\u001b[39m=\u001b[39;49mverification_mode,\n\u001b[1;32m   2618\u001b[0m     num_proc\u001b[39m=\u001b[39;49mnum_proc,\n\u001b[1;32m   2619\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m   2620\u001b[0m )\n\u001b[1;32m   2622\u001b[0m \u001b[39m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m   2623\u001b[0m keep_in_memory \u001b[39m=\u001b[39m (\n\u001b[1;32m   2624\u001b[0m     keep_in_memory \u001b[39mif\u001b[39;00m keep_in_memory \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m is_small_dataset(builder_instance\u001b[39m.\u001b[39minfo\u001b[39m.\u001b[39mdataset_size)\n\u001b[1;32m   2625\u001b[0m )\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/builder.py:1027\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m   1025\u001b[0m     \u001b[39mif\u001b[39;00m num_proc \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   1026\u001b[0m         prepare_split_kwargs[\u001b[39m\"\u001b[39m\u001b[39mnum_proc\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m num_proc\n\u001b[0;32m-> 1027\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_download_and_prepare(\n\u001b[1;32m   1028\u001b[0m         dl_manager\u001b[39m=\u001b[39;49mdl_manager,\n\u001b[1;32m   1029\u001b[0m         verification_mode\u001b[39m=\u001b[39;49mverification_mode,\n\u001b[1;32m   1030\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mprepare_split_kwargs,\n\u001b[1;32m   1031\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mdownload_and_prepare_kwargs,\n\u001b[1;32m   1032\u001b[0m     )\n\u001b[1;32m   1033\u001b[0m \u001b[39m# Sync info\u001b[39;00m\n\u001b[1;32m   1034\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minfo\u001b[39m.\u001b[39mdataset_size \u001b[39m=\u001b[39m \u001b[39msum\u001b[39m(split\u001b[39m.\u001b[39mnum_bytes \u001b[39mfor\u001b[39;00m split \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minfo\u001b[39m.\u001b[39msplits\u001b[39m.\u001b[39mvalues())\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/builder.py:1100\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m   1098\u001b[0m split_dict \u001b[39m=\u001b[39m SplitDict(dataset_name\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset_name)\n\u001b[1;32m   1099\u001b[0m split_generators_kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_make_split_generators_kwargs(prepare_split_kwargs)\n\u001b[0;32m-> 1100\u001b[0m split_generators \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_split_generators(dl_manager, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49msplit_generators_kwargs)\n\u001b[1;32m   1102\u001b[0m \u001b[39m# Checksums verification\u001b[39;00m\n\u001b[1;32m   1103\u001b[0m \u001b[39mif\u001b[39;00m verification_mode \u001b[39m==\u001b[39m VerificationMode\u001b[39m.\u001b[39mALL_CHECKS \u001b[39mand\u001b[39;00m dl_manager\u001b[39m.\u001b[39mrecord_checksums:\n",
      "File \u001b[0;32m~/.cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/66fd9b80f51375c02cd9010050e781ed3e8f759e868f690c31b2686a7a0eeb5c/wiki_dpr.py:143\u001b[0m, in \u001b[0;36mWikiDpr._split_generators\u001b[0;34m(self, dl_manager)\u001b[0m\n\u001b[1;32m    141\u001b[0m data_dir \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(\u001b[39m\"\u001b[39m\u001b[39mdata\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig\u001b[39m.\u001b[39mwiki_split, data_dir)\n\u001b[1;32m    142\u001b[0m files \u001b[39m=\u001b[39m [os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(data_dir, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mtrain-\u001b[39m\u001b[39m{\u001b[39;00mi\u001b[39m:\u001b[39;00m\u001b[39m05d\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m-of-\u001b[39m\u001b[39m{\u001b[39;00mnum_shards\u001b[39m:\u001b[39;00m\u001b[39m05d\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m.parquet\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_shards)]\n\u001b[0;32m--> 143\u001b[0m downloaded_files \u001b[39m=\u001b[39m dl_manager\u001b[39m.\u001b[39;49mdownload_and_extract(files)\n\u001b[1;32m    144\u001b[0m \u001b[39mreturn\u001b[39;00m [\n\u001b[1;32m    145\u001b[0m     datasets\u001b[39m.\u001b[39mSplitGenerator(name\u001b[39m=\u001b[39mdatasets\u001b[39m.\u001b[39mSplit\u001b[39m.\u001b[39mTRAIN, gen_kwargs\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mfiles\u001b[39m\u001b[39m\"\u001b[39m: downloaded_files}),\n\u001b[1;32m    146\u001b[0m ]\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/download/download_manager.py:434\u001b[0m, in \u001b[0;36mDownloadManager.download_and_extract\u001b[0;34m(self, url_or_urls)\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdownload_and_extract\u001b[39m(\u001b[39mself\u001b[39m, url_or_urls):\n\u001b[1;32m    419\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Download and extract given `url_or_urls`.\u001b[39;00m\n\u001b[1;32m    420\u001b[0m \n\u001b[1;32m    421\u001b[0m \u001b[39m    Is roughly equivalent to:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    432\u001b[0m \u001b[39m        extracted_path(s): `str`, extracted paths of given URL(s).\u001b[39;00m\n\u001b[1;32m    433\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 434\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mextract(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdownload(url_or_urls))\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/download/download_manager.py:257\u001b[0m, in \u001b[0;36mDownloadManager.download\u001b[0;34m(self, url_or_urls)\u001b[0m\n\u001b[1;32m    255\u001b[0m start_time \u001b[39m=\u001b[39m datetime\u001b[39m.\u001b[39mnow()\n\u001b[1;32m    256\u001b[0m \u001b[39mwith\u001b[39;00m stack_multiprocessing_download_progress_bars():\n\u001b[0;32m--> 257\u001b[0m     downloaded_path_or_paths \u001b[39m=\u001b[39m map_nested(\n\u001b[1;32m    258\u001b[0m         download_func,\n\u001b[1;32m    259\u001b[0m         url_or_urls,\n\u001b[1;32m    260\u001b[0m         map_tuple\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m    261\u001b[0m         num_proc\u001b[39m=\u001b[39;49mdownload_config\u001b[39m.\u001b[39;49mnum_proc,\n\u001b[1;32m    262\u001b[0m         desc\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mDownloading data files\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    263\u001b[0m         batched\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m    264\u001b[0m         batch_size\u001b[39m=\u001b[39;49m\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m,\n\u001b[1;32m    265\u001b[0m     )\n\u001b[1;32m    266\u001b[0m duration \u001b[39m=\u001b[39m datetime\u001b[39m.\u001b[39mnow() \u001b[39m-\u001b[39m start_time\n\u001b[1;32m    267\u001b[0m logger\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mDownloading took \u001b[39m\u001b[39m{\u001b[39;00mduration\u001b[39m.\u001b[39mtotal_seconds()\u001b[39m \u001b[39m\u001b[39m/\u001b[39m\u001b[39m/\u001b[39m\u001b[39m \u001b[39m\u001b[39m60\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m min\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/utils/py_utils.py:511\u001b[0m, in \u001b[0;36mmap_nested\u001b[0;34m(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc)\u001b[0m\n\u001b[1;32m    509\u001b[0m         batch_size \u001b[39m=\u001b[39m \u001b[39mmax\u001b[39m(\u001b[39mlen\u001b[39m(iterable) \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m num_proc \u001b[39m+\u001b[39m \u001b[39mint\u001b[39m(\u001b[39mlen\u001b[39m(iterable) \u001b[39m%\u001b[39m num_proc \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m), \u001b[39m1\u001b[39m)\n\u001b[1;32m    510\u001b[0m     iterable \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(iter_batched(iterable, batch_size))\n\u001b[0;32m--> 511\u001b[0m mapped \u001b[39m=\u001b[39m [\n\u001b[1;32m    512\u001b[0m     _single_map_nested((function, obj, batched, batch_size, types, \u001b[39mNone\u001b[39;00m, \u001b[39mTrue\u001b[39;00m, \u001b[39mNone\u001b[39;00m))\n\u001b[1;32m    513\u001b[0m     \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m hf_tqdm(iterable, disable\u001b[39m=\u001b[39mdisable_tqdm, desc\u001b[39m=\u001b[39mdesc)\n\u001b[1;32m    514\u001b[0m ]\n\u001b[1;32m    515\u001b[0m \u001b[39mif\u001b[39;00m batched:\n\u001b[1;32m    516\u001b[0m     mapped \u001b[39m=\u001b[39m [mapped_item \u001b[39mfor\u001b[39;00m mapped_batch \u001b[39min\u001b[39;00m mapped \u001b[39mfor\u001b[39;00m mapped_item \u001b[39min\u001b[39;00m mapped_batch]\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/utils/py_utils.py:512\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    509\u001b[0m         batch_size \u001b[39m=\u001b[39m \u001b[39mmax\u001b[39m(\u001b[39mlen\u001b[39m(iterable) \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m num_proc \u001b[39m+\u001b[39m \u001b[39mint\u001b[39m(\u001b[39mlen\u001b[39m(iterable) \u001b[39m%\u001b[39m num_proc \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m), \u001b[39m1\u001b[39m)\n\u001b[1;32m    510\u001b[0m     iterable \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(iter_batched(iterable, batch_size))\n\u001b[1;32m    511\u001b[0m mapped \u001b[39m=\u001b[39m [\n\u001b[0;32m--> 512\u001b[0m     _single_map_nested((function, obj, batched, batch_size, types, \u001b[39mNone\u001b[39;49;00m, \u001b[39mTrue\u001b[39;49;00m, \u001b[39mNone\u001b[39;49;00m))\n\u001b[1;32m    513\u001b[0m     \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m hf_tqdm(iterable, disable\u001b[39m=\u001b[39mdisable_tqdm, desc\u001b[39m=\u001b[39mdesc)\n\u001b[1;32m    514\u001b[0m ]\n\u001b[1;32m    515\u001b[0m \u001b[39mif\u001b[39;00m batched:\n\u001b[1;32m    516\u001b[0m     mapped \u001b[39m=\u001b[39m [mapped_item \u001b[39mfor\u001b[39;00m mapped_batch \u001b[39min\u001b[39;00m mapped \u001b[39mfor\u001b[39;00m mapped_item \u001b[39min\u001b[39;00m mapped_batch]\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/utils/py_utils.py:380\u001b[0m, in \u001b[0;36m_single_map_nested\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m    373\u001b[0m         \u001b[39mreturn\u001b[39;00m function(data_struct)\n\u001b[1;32m    374\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m    375\u001b[0m     batched\n\u001b[1;32m    376\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(data_struct, \u001b[39mdict\u001b[39m)\n\u001b[1;32m    377\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(data_struct, types)\n\u001b[1;32m    378\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mall\u001b[39m(\u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(v, (\u001b[39mdict\u001b[39m, types)) \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m data_struct)\n\u001b[1;32m    379\u001b[0m ):\n\u001b[0;32m--> 380\u001b[0m     \u001b[39mreturn\u001b[39;00m [mapped_item \u001b[39mfor\u001b[39;00m batch \u001b[39min\u001b[39;00m iter_batched(data_struct, batch_size) \u001b[39mfor\u001b[39;00m mapped_item \u001b[39min\u001b[39;00m function(batch)]\n\u001b[1;32m    382\u001b[0m \u001b[39m# Reduce logging to keep things readable in multiprocessing with tqdm\u001b[39;00m\n\u001b[1;32m    383\u001b[0m \u001b[39mif\u001b[39;00m rank \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m logging\u001b[39m.\u001b[39mget_verbosity() \u001b[39m<\u001b[39m logging\u001b[39m.\u001b[39mWARNING:\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/utils/py_utils.py:380\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    373\u001b[0m         \u001b[39mreturn\u001b[39;00m function(data_struct)\n\u001b[1;32m    374\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m    375\u001b[0m     batched\n\u001b[1;32m    376\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(data_struct, \u001b[39mdict\u001b[39m)\n\u001b[1;32m    377\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(data_struct, types)\n\u001b[1;32m    378\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mall\u001b[39m(\u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(v, (\u001b[39mdict\u001b[39m, types)) \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m data_struct)\n\u001b[1;32m    379\u001b[0m ):\n\u001b[0;32m--> 380\u001b[0m     \u001b[39mreturn\u001b[39;00m [mapped_item \u001b[39mfor\u001b[39;00m batch \u001b[39min\u001b[39;00m iter_batched(data_struct, batch_size) \u001b[39mfor\u001b[39;00m mapped_item \u001b[39min\u001b[39;00m function(batch)]\n\u001b[1;32m    382\u001b[0m \u001b[39m# Reduce logging to keep things readable in multiprocessing with tqdm\u001b[39;00m\n\u001b[1;32m    383\u001b[0m \u001b[39mif\u001b[39;00m rank \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m logging\u001b[39m.\u001b[39mget_verbosity() \u001b[39m<\u001b[39m logging\u001b[39m.\u001b[39mWARNING:\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/datasets/download/download_manager.py:300\u001b[0m, in \u001b[0;36mDownloadManager._download_batched\u001b[0;34m(self, url_or_filenames, download_config)\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[39mpass\u001b[39;00m\n\u001b[1;32m    296\u001b[0m     max_workers \u001b[39m=\u001b[39m (\n\u001b[1;32m    297\u001b[0m         config\u001b[39m.\u001b[39mHF_DATASETS_MULTITHREADING_MAX_WORKERS \u001b[39mif\u001b[39;00m size \u001b[39m<\u001b[39m (\u001b[39m20\u001b[39m \u001b[39m<<\u001b[39m \u001b[39m20\u001b[39m) \u001b[39melse\u001b[39;00m \u001b[39m1\u001b[39m\n\u001b[1;32m    298\u001b[0m     )  \u001b[39m# enable multithreading if files are small\u001b[39;00m\n\u001b[0;32m--> 300\u001b[0m     \u001b[39mreturn\u001b[39;00m thread_map(\n\u001b[1;32m    301\u001b[0m         download_func,\n\u001b[1;32m    302\u001b[0m         url_or_filenames,\n\u001b[1;32m    303\u001b[0m         desc\u001b[39m=\u001b[39;49mdownload_config\u001b[39m.\u001b[39;49mdownload_desc \u001b[39mor\u001b[39;49;00m \u001b[39m\"\u001b[39;49m\u001b[39mDownloading\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    304\u001b[0m         unit\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mfiles\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    305\u001b[0m         position\u001b[39m=\u001b[39;49mmultiprocessing\u001b[39m.\u001b[39;49mcurrent_process()\u001b[39m.\u001b[39;49m_identity[\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m]  \u001b[39m# contains the ranks of subprocesses\u001b[39;49;00m\n\u001b[1;32m    306\u001b[0m         \u001b[39mif\u001b[39;49;00m os\u001b[39m.\u001b[39;49menviron\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mHF_DATASETS_STACK_MULTIPROCESSING_DOWNLOAD_PROGRESS_BARS\u001b[39;49m\u001b[39m\"\u001b[39;49m) \u001b[39m==\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39m1\u001b[39;49m\u001b[39m\"\u001b[39;49m\n\u001b[1;32m    307\u001b[0m         \u001b[39mand\u001b[39;49;00m multiprocessing\u001b[39m.\u001b[39;49mcurrent_process()\u001b[39m.\u001b[39;49m_identity\n\u001b[1;32m    308\u001b[0m         \u001b[39melse\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m    309\u001b[0m         max_workers\u001b[39m=\u001b[39;49mmax_workers,\n\u001b[1;32m    310\u001b[0m         tqdm_class\u001b[39m=\u001b[39;49mtqdm,\n\u001b[1;32m    311\u001b[0m     )\n\u001b[1;32m    312\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    313\u001b[0m     \u001b[39mreturn\u001b[39;00m [\n\u001b[1;32m    314\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_download_single(url_or_filename, download_config\u001b[39m=\u001b[39mdownload_config)\n\u001b[1;32m    315\u001b[0m         \u001b[39mfor\u001b[39;00m url_or_filename \u001b[39min\u001b[39;00m url_or_filenames\n\u001b[1;32m    316\u001b[0m     ]\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/tqdm/contrib/concurrent.py:69\u001b[0m, in \u001b[0;36mthread_map\u001b[0;34m(fn, *iterables, **tqdm_kwargs)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[39mEquivalent of `list(map(fn, *iterables))`\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[39mdriven by `concurrent.futures.ThreadPoolExecutor`.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[39m    [default: max(32, cpu_count() + 4)].\u001b[39;00m\n\u001b[1;32m     67\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mconcurrent\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mfutures\u001b[39;00m \u001b[39mimport\u001b[39;00m ThreadPoolExecutor\n\u001b[0;32m---> 69\u001b[0m \u001b[39mreturn\u001b[39;00m _executor_map(ThreadPoolExecutor, fn, \u001b[39m*\u001b[39;49miterables, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mtqdm_kwargs)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/tqdm/contrib/concurrent.py:49\u001b[0m, in \u001b[0;36m_executor_map\u001b[0;34m(PoolExecutor, fn, *iterables, **tqdm_kwargs)\u001b[0m\n\u001b[1;32m     46\u001b[0m lock_name \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39mlock_name\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     47\u001b[0m \u001b[39mwith\u001b[39;00m ensure_lock(tqdm_class, lock_name\u001b[39m=\u001b[39mlock_name) \u001b[39mas\u001b[39;00m lk:\n\u001b[1;32m     48\u001b[0m     \u001b[39m# share lock in case workers are already using `tqdm`\u001b[39;00m\n\u001b[0;32m---> 49\u001b[0m     \u001b[39mwith\u001b[39;00m PoolExecutor(max_workers\u001b[39m=\u001b[39mmax_workers, initializer\u001b[39m=\u001b[39mtqdm_class\u001b[39m.\u001b[39mset_lock,\n\u001b[1;32m     50\u001b[0m                       initargs\u001b[39m=\u001b[39m(lk,)) \u001b[39mas\u001b[39;00m ex:\n\u001b[1;32m     51\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(tqdm_class(ex\u001b[39m.\u001b[39mmap(fn, \u001b[39m*\u001b[39miterables, chunksize\u001b[39m=\u001b[39mchunksize), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs))\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/concurrent/futures/_base.py:649\u001b[0m, in \u001b[0;36mExecutor.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m    648\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__exit__\u001b[39m(\u001b[39mself\u001b[39m, exc_type, exc_val, exc_tb):\n\u001b[0;32m--> 649\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mshutdown(wait\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m    650\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mFalse\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/concurrent/futures/thread.py:235\u001b[0m, in \u001b[0;36mThreadPoolExecutor.shutdown\u001b[0;34m(self, wait, cancel_futures)\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[39mif\u001b[39;00m wait:\n\u001b[1;32m    234\u001b[0m     \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_threads:\n\u001b[0;32m--> 235\u001b[0m         t\u001b[39m.\u001b[39;49mjoin()\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/threading.py:1096\u001b[0m, in \u001b[0;36mThread.join\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1093\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mcannot join current thread\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   1095\u001b[0m \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1096\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_wait_for_tstate_lock()\n\u001b[1;32m   1097\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1098\u001b[0m     \u001b[39m# the behavior of a negative timeout isn't documented, but\u001b[39;00m\n\u001b[1;32m   1099\u001b[0m     \u001b[39m# historically .join(timeout=x) for x<0 has acted as if timeout=0\u001b[39;00m\n\u001b[1;32m   1100\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_wait_for_tstate_lock(timeout\u001b[39m=\u001b[39m\u001b[39mmax\u001b[39m(timeout, \u001b[39m0\u001b[39m))\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/threading.py:1116\u001b[0m, in \u001b[0;36mThread._wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1113\u001b[0m     \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m   1115\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1116\u001b[0m     \u001b[39mif\u001b[39;00m lock\u001b[39m.\u001b[39;49macquire(block, timeout):\n\u001b[1;32m   1117\u001b[0m         lock\u001b[39m.\u001b[39mrelease()\n\u001b[1;32m   1118\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stop()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "# データセットのロード\n",
    "dataset = load_dataset('wiki_dpr', 'psgs_w100.nq.exact', trust_remote_code=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data:  90%|█████████ | 142/157 [23:35<01:01,  4.07s/files]"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration\n",
    "import faiss\n",
    "import numpy as np\n",
    "\n",
    "# データセットのロード\n",
    "dataset = load_dataset('wiki_dpr', 'psgs_w100.nq.exact', trust_remote_code=True)\n",
    "\n",
    "# モデルとトークナイザーのロード\n",
    "model_name = \"facebook/rag-sequence-nq\"\n",
    "tokenizer = RagTokenizer.from_pretrained(model_name)\n",
    "retriever = RagRetriever.from_pretrained(model_name, use_dummy_dataset=True)\n",
    "model = RagSequenceForGeneration.from_pretrained(model_name, retriever=retriever)\n",
    "\n",
    "# ドキュメントのエンコーディング\n",
    "def embed_passages(passages):\n",
    "    embeddings = []\n",
    "    for passage in passages:\n",
    "        inputs = tokenizer(passage, return_tensors=\"pt\", truncation=True, padding=\"max_length\", max_length=256)\n",
    "        outputs = model.retriever.question_encoder(**inputs)\n",
    "        embeddings.append(outputs.pooler_output.detach().numpy())\n",
    "    return np.vstack(embeddings)\n",
    "\n",
    "# ドキュメントのエンベッド\n",
    "passages = dataset['train']['text'][:1000]  # デモのため、最初の1000ドキュメントのみを使用\n",
    "passage_embeddings = embed_passages(passages)\n",
    "\n",
    "# Faissインデックスの作成\n",
    "index = faiss.IndexFlatL2(passage_embeddings.shape[1])\n",
    "index.add(passage_embeddings)\n",
    "faiss.write_index(index, \"faiss_index\")\n",
    "\n",
    "# Faissインデックスをモデルに読み込む\n",
    "retriever.index = faiss.read_index(\"faiss_index\")\n",
    "\n",
    "# 質問のトークナイズ\n",
    "question = \"What is the capital of France?\"\n",
    "inputs = tokenizer(question, return_tensors=\"pt\")\n",
    "\n",
    "# 回答の生成\n",
    "outputs = model.generate(inputs[\"input_ids\"])\n",
    "answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\n",
    "\n",
    "print(answer)\n"
   ]
  }
 ],
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