{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from BART_utils import get_prob, judge_mbti, compute_score, mbti_translator, device\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sukhy\\.virtualenvs\\mbti_translator_demo-C9tdDvXD\\lib\\site-packages\\transformers\\tokenization_utils_base.py:2284: FutureWarning: The `truncation_strategy` argument is deprecated and will be removed in a future version, use `truncation=True` to truncate examples to a max length. You can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to truncate to the maximal input size of the model (e.g. 512 for Bert).  If you have pairs of inputs, you can give a specific truncation strategy selected among `truncation='only_first'` (will only truncate the first sentence in the pairs) `truncation='only_second'` (will only truncate the second sentence in the pairs) or `truncation='longest_first'` (will iteratively remove tokens from the longest sentence in the pairs).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('ISTP',\n",
       " ({'E': 29.336015080590734, 'I': 70.66398491940927},\n",
       "  {'N': 25.69581522631164, 'S': 74.30418477368836},\n",
       "  {'T': 50.32619548200952, 'F': 49.67380451799048},\n",
       "  {'P': 56.57599646556062, 'J': 43.42400353443938}))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text=\"I Stayed home all day\"\n",
    "mbti_translator(text)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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  },
  "kernelspec": {
   "display_name": "Python 3.9.6 ('mbti_bart-OwFxlXtE')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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