diff --git a/main.ipynb b/main.ipynb
index 9c62c6d..69945b3 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -24,7 +24,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "metadata": {
     "tags": []
    },
@@ -36,23 +36,28 @@
     "# dev_data = pd.read_csv('dev-0/in.tsv', sep='\\t', names=columns, engine='python', quotechar='\"', error_bad_lines=False)\n",
     "# dev_expected = pd.read_csv('dev-0/expected.tsv', sep='\\t', engine='python', quotechar='\"', error_bad_lines=False)\n",
     "dev_data = list()\n",
-    "with open('dev-0/in.tsv', \"r\") as f:\n",
+    "data_path = 'dev-0/in.tsv'\n",
+    "expected_path = 'dev-0/expected.tsv'\n",
+    "out_path = 'dev-0/out.tsv'\n",
+    "\n",
+    "with open(data_path, \"r\") as f:\n",
     "    for line in f.readlines():\n",
     "        dev_data.append(line.split('\\t')[-2])\n",
     "\n",
     "dev_expected = list()\n",
-    "with open('dev-0/expected.tsv', \"r\") as f:\n",
+    "with open(expected_path, \"r\") as f:\n",
     "    for line in f.readlines():\n",
     "        dev_expected.append(line.replace('\\n',''))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
     "from nltk.tokenize import word_tokenize \n",
+    "import re\n",
     "\n",
     "def createBigram(data, expected):\n",
     "   listOfBigrams = []\n",
@@ -62,12 +67,15 @@
     "   for i in range(len(data)):\n",
     "       tokenized = word_tokenize(data[i])\n",
     "       word = tokenized[-1]\n",
-    "       listOfBigrams.append((word, expected[i]))\n",
-    "       if (word, expected[i]) in bigramCounts:\n",
-    "        bigramCounts[(word, expected[i])] += 1\n",
+    "       word = word.lower()\n",
+    "       word = re.sub('\\W+','', word)\n",
+    "       exp = expected[i].lower()\n",
+    "       listOfBigrams.append((word, exp))\n",
+    "       if (word, exp) in bigramCounts:\n",
+    "        bigramCounts[(word, exp)] += 1\n",
     "       else:\n",
-    "        bigramCounts[(word, expected[i])] = 1\n",
-    "       if data[i] in unigramCounts:\n",
+    "        bigramCounts[(word, exp)] = 1\n",
+    "       if word in unigramCounts:\n",
     "        unigramCounts[word] += 1\n",
     "       else:\n",
     "        unigramCounts[word] = 1\n",
@@ -79,13 +87,13 @@
     "    for bigram in listOfBigrams:\n",
     "        word1 = bigram[0]\n",
     "        word2 = bigram[1]\n",
-    "        listOfProb[bigram] = (bigramCounts.get(bigram))/(unigramCounts.get(word1))\n",
+    "        listOfProb[bigram] = (bigramCounts.get(bigram))/(sum(unigramCounts.values()))\n",
     "    return listOfProb"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,791 +102,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 91,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
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-       " (',', 'although'): 1.0,\n",
-       " ('by', 'their'): 1.0,\n",
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-       " ('In', 'the'): 6.0,\n",
-       " ('his', 'duly'): 2.0,\n",
-       " ('I\\\\nhad', 'seen'): 1.0,\n",
-       " ('all', 'his'): 1.0,\n",
-       " ('who\\\\n', 'is'): 1.0,\n",
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-       " ('sentiments', 'to-'): 1.0,\n",
-       " ('court\\\\n', 'and'): 1.0,\n",
-       " ('turh', 'uias:niliccul'): 1.0,\n",
-       " ('capacities', 'for'): 1.0,\n",
-       " ('the', 'hospital.'): 1.0,\n",
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-       " ('the', 'bond,'): 1.0,\n",
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-       " ('and', 'competent,'): 1.0,\n",
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-       " ('duty.\\\\n', 'Resolved,'): 1.0,\n",
-       " ('an', 'infetlor'): 1.0,\n",
-       " ('enough', 'of'): 1.0,\n",
-       " ('his', 'native'): 1.0,\n",
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-       " ('the', 'appearance'): 2.0,\n",
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-       " ('anil', 'exhibits,'): 1.0,\n",
-       " ('keeping', 'myself'): 1.0,\n",
-       " ('hand', 'of'): 1.0,\n",
-       " ('as', 'I'): 1.0,\n",
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-       " ('remote', 'objects,'): 1.0,\n",
-       " ('certain', 'Masons'): 1.0,\n",
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-       " ('e', ',o'): 1.0,\n",
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-       " ('the', 'lib*'): 1.0,\n",
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-       " ('Mr.\\\\n', 'Bo:tj'): 1.0,\n",
-       " ('Bunker', 'Hill'): 1.0,\n",
-       " ('judgment', 'Bhall'): 1.0,\n",
-       " ('In', 'part:'): 1.0,\n",
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-       " ('New', 'Jersey'): 1.0,\n",
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-       " ('and', 'computed'): 1.0,\n",
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-       " ('distrust\\\\n', 'each'): 1.0,\n",
-       " ('knew', 'how'): 1.0,\n",
-       " ('In', '1898'): 1.0,\n",
-       " ('republicanism', 'must'): 1.0,\n",
-       " ('posi\\xad\\\\n', 'tion'): 1.0,\n",
-       " ('break\\\\n', 'troth'): 1.0,\n",
-       " ('.\\\\n', 'Diploma'): 1.0,\n",
-       " ('did', 'Ia6t.'): 1.0,\n",
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-       " ('that\\\\n', 'Cato,'): 1.0,\n",
-       " ('.', 'in'): 1.0,\n",
-       " ('throwing', 'off'): 1.0,\n",
-       " ('have', 'been'): 9.0,\n",
-       " ('Folding', 'Cae'): 1.0,\n",
-       " ('will\\\\nhave', 'a'): 1.0,\n",
-       " ('re\\\\n', 'garding'): 1.0,\n",
-       " ('but\\\\n', 'trom'): 1.0,\n",
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-       " ('prop', 'r'): 1.0,\n",
-       " ('is', 'kept'): 2.0,\n",
-       " ('circumstance.\\\\n', 'Had'): 1.0,\n",
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-       " ('help', 'ye'): 1.0,\n",
-       " ('on\\\\neach', 'one'): 1.0,\n",
-       " ('by\\\\n', 'the'): 3.0,\n",
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-       " ('\\\\n', 'muscular,'): 1.0,\n",
-       " ('his', 'own'): 3.0,\n",
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-       " ('eradicating', 'all'): 1.0,\n",
-       " (',', 'that'): 14.0,\n",
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-       " ('lie', 'knew'): 1.0,\n",
-       " ('get', 'seed,'): 1.0,\n",
-       " ('the', 'bidder,'): 2.0,\n",
-       " ('and', 'the'): 18.0,\n",
-       " ('ef-\\\\n', 'fective.'): 1.0,\n",
-       " ('the\\\\n', 'extent'): 1.0,\n",
-       " ('quite', 'firm.'): 1.0,\n",
-       " ('Away\\\\nfrom', 'home'): 1.0,\n",
-       " (',', 'have'): 2.0,\n",
-       " ('the\\\\n', 'means'): 1.0,\n",
-       " ('indifference.\\\\n', 'Tuberculosis'): 1.0,\n",
-       " ('home\\\\n', 'with'): 1.0,\n",
-       " ('never', 'have'): 1.0,\n",
-       " ('to', 'show'): 1.0,\n",
-       " ('thence\\\\n', 'south'): 1.0,\n",
-       " ('laid', 'in'): 1.0,\n",
-       " ('and\\\\nTruekee', 'Kailroad'): 1.0,\n",
-       " ('.', 'Brown'): 1.0,\n",
-       " ('the', 'death'): 2.0,\n",
-       " ('horses', 'Mrs.'): 1.0,\n",
-       " ('statistics', 'in'): 1.0,\n",
-       " ('on', 'timothy'): 1.0,\n",
-       " ('shall', 'be'): 8.0,\n",
-       " ('the', 'prejudices'): 1.0,\n",
-       " ('stallion\\\\n', '|'): 1.0,\n",
-       " ('half', 'of'): 1.0,\n",
-       " ('it', 'is'): 9.0,\n",
-       " ('do', 'it;'): 1.0,\n",
-       " ('declares', 'that'): 1.0,\n",
-       " ('themselves', 'f-atriots'): 1.0,\n",
-       " ('which', 'you'): 2.0,\n",
-       " ('this\\\\n', 'tide'): 1.0,\n",
-       " ('en\\xad\\\\ntitled', 'to'): 1.0,\n",
-       " ('all', 'round.'): 1.0,\n",
-       " ('the', 'free.'): 1.0,\n",
-       " (',', 'be'): 1.0,\n",
-       " ('oi', 'till'): 1.0,\n",
-       " ('utterest\\\\n', 'scorn'): 1.0,\n",
-       " ('was', 'over'): 1.0,\n",
-       " ('the', 'besl'): 1.0,\n",
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-       " ('admission', 'that'): 1.0,\n",
-       " ('been', 'put'): 1.0,\n",
-       " ('164', 'perchei'): 1.0,\n",
-       " ('no', 'an-'): 1.0,\n",
-       " ('twenty', 'lings'): 1.0,\n",
-       " ('It', 'is'): 6.0,\n",
-       " ('hard', 'to'): 1.0,\n",
-       " (',', 'is'): 4.0,\n",
-       " ('sides', 'and'): 1.0,\n",
-       " ('deprived', 'the'): 1.0,\n",
-       " ('elo\\xad\\\\n', 'quently'): 1.0,\n",
-       " ('s', 'who'): 1.0,\n",
-       " ('advantages\\\\nwould', 'not'): 1.0,\n",
-       " ('Foushec', 'ft.'): 1.0,\n",
-       " ('.', 'Clair’s'): 1.0,\n",
-       " ('the\\\\n', 'Young'): 1.0,\n",
-       " ('was', 'clear,'): 1.0,\n",
-       " ('food\\\\nfrom', 'a'): 1.0,\n",
-       " ('not', 'only'): 3.0,\n",
-       " ('blood-stained', 'weap'): 1.0,\n",
-       " ('property\\\\n', 'has'): 1.0,\n",
-       " ('aro', 'perfectly'): 1.0,\n",
-       " ('the', 'iumily'): 1.0,\n",
-       " (',', 'reed*,'): 1.0,\n",
-       " ('just', 'for'): 1.0,\n",
-       " ('the', 'D'): 1.0,\n",
-       " ('had', 'evidently'): 1.0,\n",
-       " ('the', 'measures'): 1.0,\n",
-       " ('I\\\\n', 'finally'): 1.0,\n",
-       " ('people', 'of'): 2.0,\n",
-       " ('the', 'expense'): 1.0,\n",
-       " ('felt', 'if'): 1.0,\n",
-       " ('jour-\\\\nnalism', 'which'): 1.0,\n",
-       " ('as', 'to'): 7.0,\n",
-       " ('47', 'deg.'): 1.0,\n",
-       " (\"'\", 'after'): 1.0,\n",
-       " ('.', 'He'): 11.0,\n",
-       " ('About\\\\n', 'midnight'): 1.0,\n",
-       " ('my', 'wife'): 2.0,\n",
-       " ('effect', 'npon'): 1.0,\n",
-       " ('throughout', 'the\"'): 1.0,\n",
-       " ('the', 'last'): 4.0,\n",
-       " ('of', 'Kennehee'): 1.0,\n",
-       " ('.V', 'York.'): 1.0,\n",
-       " ('of\\\\n', 'Inue.'): 1.0,\n",
-       " ('are', 'awarded'): 1.0,\n",
-       " ('bound', 'to'): 1.0,\n",
-       " ('the', 'upper'): 1.0,\n",
-       " ('species', 'of'): 1.0,\n",
-       " ('day', 'ol'): 1.0,\n",
-       " ('a', 'fair'): 3.0,\n",
-       " ('public', 'trustee'): 1.0,\n",
-       " ('has', 'become'): 1.0,\n",
-       " ('lading', 'and,'): 1.0,\n",
-       " ('Another', 'potent'): 1.0,\n",
-       " ('to', 'ihe'): 1.0,\n",
-       " (';', 'the'): 3.0,\n",
-       " ('a', 'large'): 4.0,\n",
-       " ('II.\\\\n', 'one'): 1.0,\n",
-       " ('ben-\\\\n', 'efit'): 1.0,\n",
-       " ('which\\\\nwaa', 'fastened'): 1.0,\n",
-       " ('is', 'laid'): 1.0,\n",
-       " ('in', 'Smith'): 1.0,\n",
-       " ('the\\\\nbest', 'Mexico'): 1.0,\n",
-       " ('each', 'three'): 1.0,\n",
-       " ('a', 'moun-'): 1.0,\n",
-       " ...}"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 17
-    }
-   ],
+   "outputs": [],
    "source": [
-    "probs = calcBigramProb(bigrams, uniCounts, biCounts)\n",
-    "probs"
+    "probs = calcBigramProb(bigrams, uniCounts, biCounts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_results(probs, in_data):\n",
+    "    with open(out_path, 'w') as f:\n",
+    "        for i in range(len(in_data)):\n",
+    "            tokenized = word_tokenize(in_data[i])\n",
+    "            word = tokenized[-1]\n",
+    "            word = word.lower()\n",
+    "            word = re.sub('\\W+','', word)\n",
+    "            word_probs = dict(filter(lambda elem: elem[0][0] == word, probs.items()))\n",
+    "            rest = 1.0 - sum(word_probs.values())\n",
+    "            word_probs = list(map(lambda elem: elem[0][0] + \":\"  + str(elem[1]), list(word_probs.items())))\n",
+    "            word_probs.append(':'+str(rest))\n",
+    "            word_probs = ' '.join(word_probs)\n",
+    "            f.write(word_probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "save_results(probs, dev_data)"
    ]
   }
  ]