diff --git a/tetragram_final.ipynb b/tetragram_final.ipynb
new file mode 100644
index 0000000..abcc21d
--- /dev/null
+++ b/tetragram_final.ipynb
@@ -0,0 +1,301 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lzma\n",
+    "import pickle\n",
+    "import os\n",
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_line(line):\n",
+    "    parts = line.split('\\t')\n",
+    "    prefix = parts[6].replace(r'\\n', ' ')\n",
+    "    suffix = parts[7].replace(r'\\n', ' ')\n",
+    "    return prefix + ' ' + suffix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_words(path):\n",
+    "    with lzma.open(path, 'rt', encoding='utf-8') as f:\n",
+    "        for line in f:\n",
+    "            text = get_line(line)\n",
+    "            for word in text.split():\n",
+    "                yield word"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict\n",
+    "import bisect\n",
+    "import itertools\n",
+    "\n",
+    "\n",
+    "def ngrams(words, n=1):\n",
+    "    ngrams_counts = defaultdict(int)\n",
+    "    sum_counts = defaultdict(int)\n",
+    "    \n",
+    "    for i in range(len(words) - n + 1):\n",
+    "        ngram = tuple(words[i:i+n])\n",
+    "        ngrams_counts[ngram] += 1\n",
+    "        for j in range(1, n):\n",
+    "            sum_counts[ngram[:j]] += 1\n",
+    "    \n",
+    "    for key, value in ngrams_counts.items():\n",
+    "        sum_counts[key[:-1]] += value\n",
+    "    \n",
+    "    return ngrams_counts, sum_counts\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vocab_size = 2000\n",
+    "if os.path.exists('model/vocab_top_1000.pkl'):\n",
+    "    with open('model/vocab_top_1000.pkl', 'rb') as f:\n",
+    "        vocab_top_1000 = pickle.load(f)\n",
+    "else:\n",
+    "    counter = Counter(read_words('train/in.tsv.xz'))\n",
+    "    vocab_top_1000 = dict(counter.most_common(vocab_size))\n",
+    "    unk = 0\n",
+    "    for word, count in counter.items():\n",
+    "        if not vocab_top_1000.get(word):\n",
+    "            unk += count\n",
+    "    vocab_top_1000['<unk>'] = unk\n",
+    "    with open('model/vocab_top_1000.pkl', 'wb') as f:\n",
+    "        pickle.dump(vocab_top_1000, f, protocol=pickle.HIGHEST_PROTOCOL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ngrams(filename, V: dict, n: int):\n",
+    "    with lzma.open(filename, mode='rt', encoding='utf-8') as fid:\n",
+    "        print(f'{n}-grams')\n",
+    "        ngram_func = {\n",
+    "            1: lambda w: (w,),\n",
+    "            2: lambda w1, w2: (w1, w2),\n",
+    "            3: lambda w1, w2, w3: (w1, w2, w3),\n",
+    "            4: lambda w1, w2, w3, w4: (w1, w2, w3, w4),\n",
+    "        }[n]\n",
+    "        for line in fid:\n",
+    "            text = get_line(line)\n",
+    "            words = [''] * (n-1)\n",
+    "            for word in text.split():\n",
+    "                if V.get(word) is None:\n",
+    "                    word = '<unk>'\n",
+    "                words.append(word)\n",
+    "                yield ngram_func(*words[-n:])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if os.path.exists('model/bigrams.pkl'):\n",
+    "    with open('model/bigrams.pkl', 'rb') as f:\n",
+    "        bigrams = pickle.load(f)\n",
+    "else:\n",
+    "    bigrams = Counter(ngrams('train/in.tsv.xz', vocab_top_1000, 2))\n",
+    "    with open('model/bigrams.pkl', 'wb') as f:\n",
+    "        pickle.dump(bigrams, f, protocol=pickle.HIGHEST_PROTOCOL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if os.path.exists('model/trigrams.pkl'):\n",
+    "    with open('model/trigrams.pkl', 'rb') as f:\n",
+    "        trigrams = pickle.load(f)\n",
+    "else:\n",
+    "    trigrams = Counter(ngrams('train/in.tsv.xz', vocab_top_1000, 3))\n",
+    "    with open('model/trigrams.pkl', 'wb') as f:\n",
+    "        pickle.dump(trigrams, f, protocol=pickle.HIGHEST_PROTOCOL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if os.path.exists('model/tetragrams.pkl'):\n",
+    "    with open('model/tetragrams.pkl', 'rb') as f:\n",
+    "        tetragrams = pickle.load(f)\n",
+    "else:\n",
+    "    tetragrams = Counter(ngrams('train/in.tsv.xz', vocab_top_1000, 4))\n",
+    "    with open('model/tetragrams.pkl', 'wb') as f:\n",
+    "        pickle.dump(tetragrams, f, protocol=pickle.HIGHEST_PROTOCOL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict\n",
+    "import bisect\n",
+    "import itertools\n",
+    "\n",
+    "def probability(first, second=None, third=None, fourth=None):\n",
+    "# Unigram\n",
+    "    if not second:\n",
+    "        return vocab_top_1000.get(first, 0) / sum(vocab_top_1000.values())\n",
+    "    \n",
+    "    # Bigram\n",
+    "    bigram_key = (first, second)\n",
+    "    if bigram_key in bigrams:\n",
+    "        if not third:\n",
+    "            return bigrams[bigram_key] / vocab_top_1000.get(first, 0)\n",
+    "        \n",
+    "        # Trigram\n",
+    "        trigram_key = (first, second, third)\n",
+    "        if trigram_key in trigrams:\n",
+    "            if not fourth:\n",
+    "                return trigrams[trigram_key] / bigrams.get(bigram_key, 0)\n",
+    "            \n",
+    "            # Tetragram\n",
+    "            tetragram_key = (first, second, third, fourth)\n",
+    "            if tetragram_key in tetragrams:\n",
+    "                return tetragrams[tetragram_key] / trigrams.get(trigram_key, 0)\n",
+    "    \n",
+    "    # Key not found\n",
+    "    return 0\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def interpolate(tetragram):\n",
+    "    first, second, third, fourth = tetragram\n",
+    "    if first and second and third and fourth:\n",
+    "        return 0.4 * probability(first, second, third, fourth) + 0.3 * probability(second, third, fourth) + 0.2 * probability(third, fourth) + 0.1 * probability(fourth)\n",
+    "    elif first and second and third:\n",
+    "        return 0.5 * probability(first, second, third) + 0.3 * probability(second, third) + 0.2 * probability(third)\n",
+    "    elif first and second:\n",
+    "        return 0.6 * probability(first, second) + 0.4 * probability(second) \n",
+    "    return probability(first)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def consider_context(left_context, right_context):\n",
+    "    first, second, third = left_context\n",
+    "    fifth, sixth, seventh = right_context\n",
+    "    \n",
+    "    probs = []\n",
+    "    for word in vocab_top_1000:\n",
+    "        p1 = interpolate((first, second, third, word))\n",
+    "        p2 = interpolate((second, third, word, fifth))\n",
+    "        p3 = interpolate((third, word, fifth, sixth)) \n",
+    "        p4 = interpolate((word, fifth, sixth, seventh))\n",
+    "        prob = p1 * p2 * p3 * p4\n",
+    "        probs.append((word, prob))\n",
+    "    \n",
+    "    probs = sorted(probs, key=lambda x: x[1], reverse=True)[:5]\n",
+    "    total_prob = sum(prob for _, prob in probs)\n",
+    "    \n",
+    "    norm = [(word, prob/total_prob) for word, prob in probs]\n",
+    "    for index, elem in enumerate(norm):\n",
+    "        if elem[0] == '<unk>':\n",
+    "            norm.pop(index)\n",
+    "            norm.append(('', elem[1]))\n",
+    "            break\n",
+    "    else:\n",
+    "        norm[-1] = ('', norm[-1][1])\n",
+    "        \n",
+    "    return ' '.join([f'{x[0]}:{x[1]}' for x in norm])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def execute(path):\n",
+    "    with lzma.open(f'{path}/in.tsv.xz', 'rt', encoding='utf-8') as f, \\\n",
+    "        open(f'{path}/out.tsv', 'w', encoding='utf-8') as out:\n",
+    "        for line in f:\n",
+    "            prefix, suffix = line.split('\\t')[6:8]\n",
+    "            prefix = prefix.replace(r'\\n', ' ').split()[-3:]\n",
+    "            suffix = suffix.replace(r'\\n', ' ').split()[:3]\n",
+    "            left = [vocab_top_1000.get(x, '<unk>') for x in prefix]\n",
+    "            right = [vocab_top_1000.get(x, '<unk>') for x in suffix]\n",
+    "            result = consider_context(left, right)\n",
+    "            out.write(f\"{result}\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "execute('dev-0')\n",
+    "execute('test-A')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}