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 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Masked LM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "This tutorial is available as an IPython notebook at [Malaya/example/mlm](https://github.com/huseinzol05/Malaya/tree/master/example/mlm).\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Masked Language Model Scoring, https://arxiv.org/abs/1910.14659\n",
    "\n",
    "We are able to use BERT, ALBERT, RoBERTa and DeBERTa-v2 from HuggingFace to do text scoring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/husein/dev/malaya/malaya/tokenizer.py:214: FutureWarning: Possible nested set at position 3397\n",
      "  self.tok = re.compile(r'({})'.format('|'.join(pipeline)))\n",
      "/home/husein/dev/malaya/malaya/tokenizer.py:214: FutureWarning: Possible nested set at position 3927\n",
      "  self.tok = re.compile(r'({})'.format('|'.join(pipeline)))\n"
     ]
    }
   ],
   "source": [
    "import malaya"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List available MLM models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mesolitica/bert-base-standard-bahasa-cased': {'Size (MB)': 310},\n",
       " 'mesolitica/bert-tiny-standard-bahasa-cased': {'Size (MB)': 66.1},\n",
       " 'mesolitica/roberta-base-standard-bahasa-cased': {'Size (MB)': 443},\n",
       " 'mesolitica/roberta-tiny-standard-bahasa-cased': {'Size (MB)': 66.1},\n",
       " 'mesolitica/malaysian-debertav2-base': {'Size (MB)': 228}}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya.language_model.available_mlm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load MLM model\n",
    "\n",
    "```python\n",
    "def mlm(\n",
    "    model: str = 'mesolitica/bert-tiny-standard-bahasa-cased',\n",
    "    force_check: bool = True,\n",
    "    **kwargs\n",
    "):\n",
    "    \"\"\"\n",
    "    Load Masked language model.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model: str, optional (default='mesolitica/bert-tiny-standard-bahasa-cased')\n",
    "        Check available models at `malaya.language_model.available_mlm`.\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.mask_lm.MLMScorer class\n",
    "    \"\"\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = malaya.language_model.mlm(model = 'mesolitica/bert-tiny-standard-bahasa-cased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-28.428839"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score('saya suke awak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-11.658715"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score('saya suka awak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.1320121"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score('najib razak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-19.881565"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score('najib comel')"
   ]
  }
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