cvd_utils.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import random\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def seq_padding(tokens, max_len, token2idx=None, symbol=None):\n",
    "    \n",
    "    if symbol is None:\n",
    "        symbol = PAD\n",
    "\n",
    "    seq = []\n",
    "    token_len = len(tokens)\n",
    "    for i in range(max_len):\n",
    "        if i < token_len:\n",
    "            seq.append(tokens[i])\n",
    "        else:\n",
    "            seq.append(symbol)\n",
    "    return seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def position_idx(tokens, symbol=SEP):\n",
    "    pos = []\n",
    "    flag = 0\n",
    "\n",
    "    for token in tokens:\n",
    "        if token == symbol:\n",
    "            pos.append(flag)\n",
    "            flag += 1\n",
    "        else:\n",
    "            pos.append(flag)\n",
    "    return pos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def index_seg(tokens, symbol=SEP):\n",
    "    flag = 0\n",
    "    seg = []\n",
    "\n",
    "    for token in tokens:\n",
    "        if token == symbol:\n",
    "            seg.append(flag)\n",
    "            if flag == 0:\n",
    "                flag = 1\n",
    "            else:\n",
    "                flag = 0\n",
    "        else:\n",
    "            seg.append(flag)\n",
    "    return seg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_folder(path):\n",
    "    if not os.path.exists(path):\n",
    "        os.makedirs(path)\n",
    "\n",
    "\n",
    "def save_obj(obj, name):\n",
    "    with open(name + '.pkl', 'wb') as f:\n",
    "        pickle.dump(obj, f)\n",
    "\n",
    "\n",
    "def load_obj(name):\n",
    "    with open(name + '.pkl', 'rb') as f:\n",
    "        return pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_mask(tokens, token2idx):\n",
    "    output_label = []\n",
    "    output_token = []\n",
    "    for i, token in enumerate(tokens):\n",
    "        prob = random.random()\n",
    "        # mask token with 15% probability\n",
    "        if prob < 0.15 and token>3:\n",
    "            prob /= 0.15\n",
    "\n",
    "            # 80% randomly change token to mask token\n",
    "            if prob < 0.8:\n",
    "                output_token.append(token2idx[\"MASK\"])\n",
    "\n",
    "            # 10% randomly change token to random token\n",
    "            elif prob < 0.9:\n",
    "                output_token.append(random.choice(list(token2idx.values())[4:]))\n",
    "\n",
    "            # -> rest 10% randomly keep current token\n",
    "\n",
    "            # append current token to output (we will predict these later\n",
    "            output_label.append(token)\n",
    "        else:\n",
    "            # no masking token (will be ignored by loss function later)\n",
    "            output_label.append(-1)\n",
    "            output_token.append(token)\n",
    "\n",
    "    return tokens, output_token, output_label"
   ]
  }
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