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   "source": [
    "# GPT2 LM"
   ]
  },
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   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "This tutorial is available as an IPython notebook at [Malaya/example/gpt2-lm](https://github.com/huseinzol05/Malaya/tree/master/example/gpt2-lm).\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/husein/dev/malaya/malaya/tokenizer.py:202: FutureWarning: Possible nested set at position 3361\n",
      "  self.tok = re.compile(r'({})'.format('|'.join(pipeline)))\n",
      "/home/husein/dev/malaya/malaya/tokenizer.py:202: FutureWarning: Possible nested set at position 3879\n",
      "  self.tok = re.compile(r'({})'.format('|'.join(pipeline)))\n"
     ]
    }
   ],
   "source": [
    "import malaya"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dependency\n",
    "\n",
    "Make sure you already installed,\n",
    "\n",
    "```bash\n",
    "pip3 install transformers\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List available GPT2 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size (MB)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mesolitica/gpt2-117m-bahasa-cased</th>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
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       "                                   Size (MB)\n",
       "mesolitica/gpt2-117m-bahasa-cased        454"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya.language_model.available_gpt2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load GPT2 LM model\n",
    "\n",
    "```python\n",
    "def gpt2(model: str = 'mesolitica/gpt2-117m-bahasa-cased', force_check: bool = True, **kwargs):\n",
    "    \"\"\"\n",
    "    Load GPT2 language model.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model: str, optional (default='mesolitica/gpt2-117m-bahasa-cased')\n",
    "        Check available models at `malaya.language_model.available_gpt2()`.\n",
    "    force_check: bool, optional (default=True)\n",
    "        Force check model one of malaya model.\n",
    "        Set to False if you have your own huggingface model.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result: malaya.torch_model.gpt2_lm.LM class\n",
    "    \"\"\"\n",
    "```\n",
    "\n",
    "If you have other models from huggingface and want to load it on `malaya.torch_model.gpt2_lm.LM`, set `force_check=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = malaya.language_model.gpt2()"
   ]
  },
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   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "model.score('saya suke awak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "model.score('saya suka awak')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "model.score('najib razak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score('najib comel')"
   ]
  }
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