nsmc-study/Batch.ipynb

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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Monoid\\\\anaconda3\\\\envs\\\\nn\\\\python.exe'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.executable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5861234",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List\n",
    "from torch.utils.data import Dataset\n",
    "import torch\n",
    "from transformers import PreTrainedTokenizer\n",
    "from ndata import readNsmcRawData, NsmcRawData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5accd3a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load bert tokenizer...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████| 150000/150000 [00:00<00:00, 205761.43it/s]\n"
     ]
    }
   ],
   "source": [
    "from transformers import BertTokenizer\n",
    "print(\"load bert tokenizer...\")\n",
    "PRETAINED_MODEL_NAME = 'bert-base-multilingual-cased'\n",
    "tokenizer = BertTokenizer.from_pretrained(PRETAINED_MODEL_NAME)\n",
    "\n",
    "data = readNsmcRawData(\"nsmc/nsmc-master/ratings_train.txt\",use_tqdm=True,total=150000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d10fcb83",
   "metadata": {},
   "source": [
    "data를 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "552fe555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "({'input_ids': tensor([[   101,   9519,   9074, 119005,    119,    119,   9708, 119235,   9715,\n",
      "         119230,  16439,  77884,  48549,   9284,  22333,  12692,    102,      0,\n",
      "              0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
      "              0,      0,      0,      0],\n",
      "        [   101,    100,    119,    119,    119,   9928,  58823,  30005,  11664,\n",
      "           9757, 118823,  30858,  18227, 119219,    119,    119,    119,    119,\n",
      "           9580,  41605,  25486,  12310,  20626,  23466,   8843, 118986,  12508,\n",
      "           9523,  17196,  16439,    102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "         0, 0, 0, 0, 0, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "         0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
      "         0, 0, 0, 0, 0, 0, 0],\n",
      "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1, 1, 1, 1, 1, 1, 1]])}, tensor([0, 1]))\n"
     ]
    }
   ],
   "source": [
    "def make_collate_fn(tokenzier: PreTrainedTokenizer):\n",
    "    def collate_fn(batch: List[NsmcRawData]):\n",
    "        labels = [s.label for s in batch]\n",
    "        return tokenizer([s.document for s in batch], return_tensors='pt', padding='longest', truncation=True), torch.tensor(labels)\n",
    "    return collate_fn\n",
    "\n",
    "collate = make_collate_fn(tokenizer)\n",
    "print(collate(data[0:2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cff8e03",
   "metadata": {},
   "source": [
    "간단한 collate function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89eb64d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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