{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NUBIA: A SoTA evaluation metric for text generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import and initialize the Nubia class from nubia.py (wherever you cloned the repo)\n",
    "See our [colab notebook](https://colab.research.google.com/drive/1_K8pOB8fRRnkBPwlcmvUNHgCr4ur8rFg) as an example\n",
    "\n",
    "Note: The first time you initialize the class it will download the pretrained models from the S3 bucket, this could take a while depending on your internet connection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nubia import Nubia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading archive file pretrained/roBERTa_STS\n",
      "| [input] dictionary: 50265 types\n",
      "loading archive file pretrained/roBERTa_MNLI\n",
      "| dictionary: 50264 types\n"
     ]
    }
   ],
   "source": [
    "nubia = Nubia()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You're now ready to start evaluating! \n",
    "\n",
    "`nubia.score` takes 7 parameters: `(ref, hyp, verbose=False, get_features=False, six_dim=False, aggregator=\"agg_one\")`\n",
    "\n",
    "`ref` and `hyp` are the strings nubia will compare. \n",
    "\n",
    "Setting `get_features` to `True` will return a dictionary with additional features (semantic relation, contradiction, irrelevancy, logical agreement, and grammaticality) aside from the nubia score. `Verbose=True` prints all the features.\n",
    "\n",
    "`six_dim = True` will use a six dimensional \n",
    "\n",
    "`aggregator` is set to `agg_one` by default, but you may choose to try `agg_two` which is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9905961979783401"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nubia.score(\"The dinner was delicious.\", \"It was a tasty dinner.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.601254306957781"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nubia.score(\"The dinner was delicious.\", \"The dinner did not taste good.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Semantic relation: 1.4594818353652954/5.0\n",
      "Percent chance of contradiction: 99.90345239639282%\n",
      "Percent chance of irrelevancy or new information: 0.06429857457987964%\n",
      "Percent chance of logical agreement: 0.03225349937565625%\n",
      "\n",
      "NUBIA score: 0.601254306957781/1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'nubia_score': 0.601254306957781,\n",
       " 'features': {'semantic_relation': 1.4594818353652954,\n",
       "  'contradiction': 99.90345239639282,\n",
       "  'irrelevancy': 0.06429857457987964,\n",
       "  'logical_agreement': 0.03225349937565625,\n",
       "  'grammar_ref': 5.1724853515625,\n",
       "  'grammar_hyp': 4.905452728271484}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nubia.score(\"The dinner was delicious.\", \"The dinner did not taste good.\", verbose=True, get_features=True)"
   ]
  }
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