diff --git a/dev-0/out.tsv b/dev-0/out.tsv
index 9fc2099..b6a3053 100644
--- a/dev-0/out.tsv
+++ b/dev-0/out.tsv
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 :1
diff --git a/run.ipynb b/run.ipynb
index f96d35d..ab8ae68 100644
--- a/run.ipynb
+++ b/run.ipynb
@@ -1,213 +1,5671 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2a4fb731",
-   "metadata": {},
-   "source": [
-    "MODEL TRIGRAMOWY - uwzględniamy dwa poprzednie słowa"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "c16d72a6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import lzma\n",
-    "import csv\n",
-    "import re\n",
-    "import math"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a1ff03c8",
-   "metadata": {},
+   "id": "489ac00e",
+   "metadata": {
+    "id": "489ac00e"
+   },
    "outputs": [],
    "source": [
-    "def read_data(folder_name, test_data=False):\n",
-    "    \n",
-    "    all_data = lzma.open(f'{folder_name}/in.tsv.xz').read().decode('UTF-8').split('\\n')\n",
-    "    data = [line.split('\\t') for line in all_data][:-1]\n",
-    "    data = [[i[6].replace('\\\\n', ' '), i[7].replace('\\\\n', ' ')] for i in data]\n",
-    "    \n",
-    "    if not test_data:\n",
-    "        words = []\n",
-    "        with open(f'{folder_name}/expected.tsv') as file:\n",
-    "            tsv_file = csv.reader(file, delimiter=\"\\t\")\n",
-    "            for line in tsv_file:\n",
-    "                words.append(line[0])\n",
-    "            \n",
-    "        return data, words\n",
-    "    \n",
-    "    return data\n",
-    "\n",
-    "train_data, train_words = read_data('train')"
+    "import torch\n",
+    "from torch import nn, optim\n",
+    "from torch.utils.data import DataLoader\n",
+    "import numpy as np\n",
+    "from collections import Counter\n",
+    "import re\n",
+    "import lzma\n",
+    "import csv"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "a4a73c19",
-   "metadata": {},
+   "id": "61a07c7b",
+   "metadata": {
+    "id": "61a07c7b"
+   },
    "outputs": [],
    "source": [
-    "def print_example(data, words, idx):\n",
-    "    print(f'{data[idx][0]} _____{words[idx].upper()}_____ {data[idx][1]}')\n",
-    "    \n",
-    "# print_example(train_data, train_words, 13)"
+    "device = 'cuda'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "ce522af5",
-   "metadata": {},
+   "id": "0bdf4949",
+   "metadata": {
+    "id": "0bdf4949"
+   },
    "outputs": [],
    "source": [
-    "def generate_N_grams(text, ngram=1, no_punctuation=True):\n",
-    "    text = re.sub(r'[\\-] ', '', text).lower()\n",
-    "    if no_punctuation:\n",
-    "        text = re.sub(r'[^\\w\\s]', ' ', text)\n",
-    "    words=[word for word in text.split()]\n",
-    "    temp=zip(*[words[i:] for i in range(0,ngram)])\n",
-    "    ans=[' '.join(ngram) for ngram in temp]\n",
-    "    return ans\n",
+    "class Dataset(torch.utils.data.Dataset):\n",
+    "    def __init__(\n",
+    "            self,\n",
+    "            sequence_length,\n",
+    "    ):\n",
+    "        self.sequence_length = sequence_length\n",
+    "        self.words = self.load()\n",
+    "        self.uniq_words = self.get_uniq_words()\n",
     "\n",
-    "N_grams = []\n",
-    "for i in range(len(train_data[:5000])):\n",
-    "    N_grams += generate_N_grams(f'{train_data[i][0]} {train_words[i]} {train_data[i][1]}', 2)\n",
-    "    N_grams += generate_N_grams(f'{train_data[i][0]} {train_words[i]} {train_data[i][1]}', 3)"
+    "        self.index_to_word = {index: word for index, word in enumerate(self.uniq_words)}\n",
+    "        self.word_to_index = {word: index for index, word in enumerate(self.uniq_words)}\n",
+    "\n",
+    "        self.words_indexes = [self.word_to_index[w] for w in self.words]\n",
+    "\n",
+    "    def load(self):\n",
+    "        data = lzma.open(f'train/in.tsv.xz').read().decode('UTF-8').split('\\n')\n",
+    "        data = [line.split('\\t') for line in data][:-1]\n",
+    "        data = [[i[6].replace('\\\\\\\\n', ' '), i[7].replace('\\\\\\\\n', ' ')] for i in data]\n",
+    "        \n",
+    "        words = []\n",
+    "        with open(f'train/expected.tsv') as file:\n",
+    "            tsv_file = csv.reader(file, delimiter=\"\\t\")\n",
+    "            for line in tsv_file:\n",
+    "                words.append(line[0])\n",
+    "                \n",
+    "        text = []\n",
+    "#         for i in range(len(data) - 1):\n",
+    "        for i in range(5000):\n",
+    "            t = data[i][0] + ' ' + words[i] + ' ' + data[i][1] + ' '\n",
+    "            text += [t.replace('\\\\n', ' ')]\n",
+    "            \n",
+    "        text = ' '.join(text).lower()\n",
+    "        text = re.sub('[^a-z ]', '', text)\n",
+    "        text = text.split(' ')\n",
+    "        return text\n",
+    "    \n",
+    "    \n",
+    "    def get_uniq_words(self):\n",
+    "        word_counts = Counter(self.words)\n",
+    "        return sorted(word_counts, key=word_counts.get, reverse=True)\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.words_indexes) - self.sequence_length\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        return (\n",
+    "            torch.tensor(self.words_indexes[index:index+self.sequence_length]),\n",
+    "            torch.tensor(self.words_indexes[index+1:index+self.sequence_length+1]),\n",
+    "        )"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "317ade72",
+   "id": "e5d9b187",
    "metadata": {
-    "scrolled": true
+    "id": "e5d9b187"
    },
    "outputs": [],
    "source": [
-    "def check_prob(N_grams):\n",
-    "    count = {}\n",
-    "    for i in N_grams:\n",
-    "        i = i.rsplit(maxsplit=1)\n",
-    "        if i[0] in count:\n",
-    "            if i[1] in count[i[0]]:\n",
-    "                count[i[0]][i[1]] += 1\n",
-    "            else:\n",
-    "                count[i[0]][i[1]] = 1\n",
-    "        else:\n",
-    "            count[i[0]] = {i[1]: 1}\n",
-    "            \n",
-    "    for word in count:\n",
-    "        s = sum(count[word].values())\n",
-    "        for i in count[word]:\n",
-    "            count[word][i] = count[word][i] / s\n",
-    "            \n",
-    "    return count\n",
-    "\n",
-    "probs = check_prob(N_grams)"
+    "dataset = Dataset(5)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "3a7ec4ec",
-   "metadata": {},
-   "outputs": [],
+   "id": "50c1bffc",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "50c1bffc",
+    "outputId": "9c38096d-99ab-46af-f761-64239e263ca6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([  0, 220,  19, 109, 202]), tensor([220,  19, 109, 202,   5]))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "dev_data, dev_words = read_data('dev-0')"
+    "dataset[200]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "86aeda02",
-   "metadata": {},
-   "outputs": [],
+   "id": "b6adb94e",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "b6adb94e",
+    "outputId": "b08722e7-7a57-4479-da97-fa8a7364a4a7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['the', 'yet', 'i', 'm', 'party']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "def find_word(word_1, word_2):\n",
-    "    tmp_probs = {}\n",
-    "    if word_1 in probs:\n",
-    "        if word_2 in probs:\n",
-    "            for i in probs[word_1]:\n",
-    "                if i in probs[word_2]:\n",
-    "                    tmp_probs[i] = probs[word_1][i] * probs[word_2][i]\n",
-    "                    if tmp_probs[i] == 1:\n",
-    "                        tmp_probs[i] = 0.1\n",
-    "                else:\n",
-    "                    tmp_probs[i] = probs[word_1][i] / 5\n",
-    "        else:\n",
-    "            tmp_probs = probs[word_1]\n",
-    "    else:\n",
-    "        tmp_probs = {}\n",
-    "    \n",
-    "    sorted_list = sorted(tmp_probs.items(), key=lambda x: x[1], reverse=True)[:1]\n",
-    "    tmm = ' '.join([i[0] + ':' + str(i[1]) for i in sorted_list])\n",
-    "    s = 1 - sum(n for _, n in sorted_list)\n",
-    "    if s == 0:\n",
-    "        s = 0.01\n",
-    "    tmm += ' :' + str(s)\n",
-    "    if tmp_probs == {}:\n",
-    "        return ':1'\n",
-    "    return tmm"
+    "[dataset.index_to_word[x] for x in [  0, 231,  19,  98, 189]]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "3b713dc3",
-   "metadata": {},
-   "outputs": [],
+   "id": "d9d3ae44",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "d9d3ae44",
+    "outputId": "8f2fed11-8e5a-4771-fb12-6d716aa84ea0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['yet', 'i', 'm', 'party', 'in']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "def find_words(data):\n",
-    "    found_words = []\n",
-    "    for i in data:\n",
-    "        t = i[0]\n",
-    "        t = re.sub(r'[\\-] ', '', t).lower()\n",
-    "        if True:\n",
-    "            t = re.sub(r'[^\\w\\s]', ' ', t)\n",
-    "        words=[word for word in t.split()]\n",
-    "        found_words.append(find_word(words[-1], ' '.join(words[-2:])))\n",
-    "    return found_words\n",
-    "\n",
-    "dev_found_words = find_words(dev_data)"
+    "[dataset.index_to_word[x] for x in [231,  19,  98, 189,   5]]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "17be7468",
-   "metadata": {},
-   "outputs": [],
+   "id": "57ac91ce",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 299
+    },
+    "id": "57ac91ce",
+    "outputId": "bb9dd417-56d5-41cf-8b64-0ece6b902bc7"
+   },
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "ignored",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-ab2855c8fc94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minput_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m  \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m231\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;36m19\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;36m98\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m189\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0;31m# This function throws if there's a driver initialization error, no GPUs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m         \u001b[0;31m# are found or any other error occurs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m         \u001b[0;31m# Some of the queued calls may reentrantly call _lazy_init();\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m         \u001b[0;31m# we need to just return without initializing in that case.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: No CUDA GPUs are available"
+     ]
+    }
+   ],
    "source": [
-    "def save_data(folder, words):\n",
-    "    f = open(f'{folder}/out.tsv', 'w')\n",
-    "    f.write('\\n'.join(words) + '\\n')\n",
-    "    f.close()\n",
-    "    \n",
-    "save_data('dev-0', dev_found_words)"
+    "input_tensor = torch.tensor([[  0, 231,  19,  98, 189]], dtype=torch.int32).to(device)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "b2e52242",
-   "metadata": {},
+   "execution_count": null,
+   "id": "a4f20449",
+   "metadata": {
+    "id": "a4f20449"
+   },
    "outputs": [],
    "source": [
-    "test_data = read_data('test-A', True)\n",
-    "test_found_words = find_words(test_data)\n",
-    "save_data('test-A', test_found_words)"
+    "class Model(nn.Module):\n",
+    "    def __init__(self, vocab_size):\n",
+    "        super(Model, self).__init__()\n",
+    "        self.lstm_size = 128\n",
+    "        self.embedding_dim = 128\n",
+    "        self.num_layers = 3\n",
+    "\n",
+    "        self.embedding = nn.Embedding(\n",
+    "            num_embeddings=vocab_size,\n",
+    "            embedding_dim=self.embedding_dim,\n",
+    "        )\n",
+    "        self.lstm = nn.LSTM(\n",
+    "            input_size=self.lstm_size,\n",
+    "            hidden_size=self.lstm_size,\n",
+    "            num_layers=self.num_layers,\n",
+    "            dropout=0.2,\n",
+    "        )\n",
+    "        self.fc = nn.Linear(self.lstm_size, vocab_size)\n",
+    "\n",
+    "    def forward(self, x, prev_state = None):\n",
+    "        embed = self.embedding(x)\n",
+    "        output, state = self.lstm(embed, prev_state)\n",
+    "        logits = self.fc(output)\n",
+    "        return logits, state\n",
+    "\n",
+    "    def init_state(self, sequence_length):\n",
+    "        return (torch.zeros(self.num_layers, sequence_length, self.lstm_size).to(device),\n",
+    "                torch.zeros(self.num_layers, sequence_length, self.lstm_size).to(device))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73db4668",
+   "metadata": {
+    "id": "73db4668"
+   },
+   "outputs": [],
+   "source": [
+    "model = Model(len(dataset)).to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0234d67",
+   "metadata": {
+    "id": "b0234d67"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred, state_h = model(input_tensor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f1a4bea",
+   "metadata": {
+    "id": "4f1a4bea"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a7afe95",
+   "metadata": {
+    "id": "8a7afe95"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea0524f5",
+   "metadata": {
+    "id": "ea0524f5"
+   },
+   "outputs": [],
+   "source": [
+    "def train(dataset, model, max_epochs, batch_size):\n",
+    "    model.train()\n",
+    "\n",
+    "    dataloader = DataLoader(dataset, batch_size=batch_size)\n",
+    "    criterion = nn.CrossEntropyLoss()\n",
+    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
+    "\n",
+    "    for epoch in range(max_epochs):\n",
+    "        for batch, (x, y) in enumerate(dataloader):\n",
+    "            optimizer.zero_grad()\n",
+    "            x = x.to(device)\n",
+    "            y = y.to(device)\n",
+    "\n",
+    "            y_pred, state_h = model(x)\n",
+    "            loss = criterion(y_pred.transpose(1, 2), y)\n",
+    "\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            print({ 'epoch': epoch, 'update in batch': batch, '/' : len(dataloader), 'loss': loss.item() })\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a30256c3",
+   "metadata": {
+    "id": "a30256c3",
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model = Model(vocab_size = len(dataset.uniq_words)).to(device)\n",
+    "train(dataset, model, 1, 64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d44e3ed",
+   "metadata": {
+    "id": "6d44e3ed"
+   },
+   "outputs": [],
+   "source": [
+    "def predict(dataset, model, text, next_words=5):\n",
+    "    model.eval()\n",
+    "    words = text.split(' ')\n",
+    "    state_h = model.init_state(len(words))\n",
+    "    res = []\n",
+    "\n",
+    "    x = torch.tensor([[dataset.word_to_index[w] for w in words]]).to(device)\n",
+    "    y_pred, state_h = model(x, state_h)\n",
+    "\n",
+    "    last_word_logits = y_pred[0][-1]\n",
+    "    p = torch.nn.functional.softmax(last_word_logits, dim=0).detach().cpu().numpy()\n",
+    "    tmp = sorted(zip(p, range(len(p))), reverse=True)[:next_words]\n",
+    "    for w in tmp:\n",
+    "        res.append((dataset.index_to_word[w[1]], w[0]))\n",
+    "\n",
+    "    return res\n",
+    "\n",
+    "def predict2(dataset, model, model2, text, text2, next_words=5):\n",
+    "    model.eval()\n",
+    "    model2.eval()\n",
+    "    words = text.split(' ')\n",
+    "    words2 = text2.split(' ')\n",
+    "    words2.reverse()\n",
+    "    state_h = model.init_state(len(words))\n",
+    "    state_h_2 = model2.init_state(len(words))\n",
+    "    res = []\n",
+    "\n",
+    "    x = torch.tensor([[dataset.word_to_index[w] for w in words]]).to(device)\n",
+    "    x2 = torch.tensor([[dataset.word_to_index[w] for w in words2]]).to(device)\n",
+    "    y_pred, state_h = model(x, state_h)\n",
+    "    y_pred_2, state_h_2 = model2(x2, state_h_2)\n",
+    "\n",
+    "    last_word_logits = y_pred[0][-1]\n",
+    "    last_word_logits_2 = y_pred_2[0][-1]\n",
+    "    p = torch.nn.functional.softmax(last_word_logits, dim=0).detach().cpu().numpy()\n",
+    "    p2 = torch.nn.functional.softmax(last_word_logits_2, dim=0).detach().cpu().numpy()\n",
+    "\n",
+    "    p_mean = [(g + h) / 2 for g, h in zip(p, p2)]\n",
+    "\n",
+    "    tmp = sorted(zip(p_mean, range(len(p_mean))), reverse=True)[:next_words]\n",
+    "    for w in tmp:\n",
+    "        res.append((dataset.index_to_word[w[1]], w[0]))\n",
+    "\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a356f317",
+   "metadata": {
+    "id": "a356f317"
+   },
+   "outputs": [],
+   "source": [
+    "predict(dataset, model, 'it is a')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "fab260da",
+   "metadata": {
+    "id": "fab260da"
+   },
+   "outputs": [],
+   "source": [
+    "dev_data = lzma.open(f'dev-0/in.tsv.xz').read().decode('UTF-8').split('\\n')\n",
+    "dev_data = [line.split('\\t') for line in dev_data][:-1]\n",
+    "dev_data1 = [re.sub('[^a-z ]', '', i[6].replace('\\\\n', ' ').lower()).strip() for i in dev_data]\n",
+    "dev_data2 = [re.sub('[^a-z ]', '', i[7].replace('\\\\n', ' ').lower()).strip() for i in dev_data]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "721d9a0c",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 127
+    },
+    "id": "721d9a0c",
+    "outputId": "9aac7214-5089-4b34-fb21-300c3b5eaa39"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      },
+      "text/plain": [
+       "'rin k ui i rsognfd inlriliinnts ir the town of y jinnin the county of lincoln rrspcrtfully rop hhont that the part ol said town whi h they inhabit s remote from tiie viii no and tliat they are so sit jutfd h it they would he much hotter accomodated f their lands were to  c m oil from raid town of mna and allix d and attached to flic town of wis tassel the si ire town of tlio county and where nost of their hmdmss is transacted they would in r lore petition y tir hole rible body that the lividing line of si j towns f wiscnsct mf jim nav his so far alt rod ns to include their farms in mid town of vviscasset and the the now line of li vision between acid towns ninv ho as fdlows viz lh ginning on the pi scut line dividing the towns ol ivia assct and ain at t southeast corner id seorgc acorns laud in said aina and riinninu from'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dev_data[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "22823f96",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "22823f96",
+    "outputId": "38491034-d745-48c8-c311-dc4b230481be"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('to', 0.11381497), ('and', 0.04800251), ('in', 0.042429972), ('the', 0.04154489), ('of', 0.030516978)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(predict(dataset, model, ' '.join(dev_data[9].split()[-1:])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "GGMKTev70Cji",
+   "metadata": {
+    "id": "GGMKTev70Cji"
+   },
+   "outputs": [],
+   "source": [
+    "class ReversedDataset(torch.utils.data.Dataset):\n",
+    "    def __init__(\n",
+    "            self,\n",
+    "            sequence_length,\n",
+    "    ):\n",
+    "        self.sequence_length = sequence_length\n",
+    "        self.words = self.load()\n",
+    "        self.uniq_words = self.get_uniq_words()\n",
+    "\n",
+    "        self.index_to_word = {index: word for index, word in enumerate(self.uniq_words)}\n",
+    "        self.word_to_index = {word: index for index, word in enumerate(self.uniq_words)}\n",
+    "\n",
+    "        self.words_indexes = [self.word_to_index[w] for w in self.words]\n",
+    "\n",
+    "    def load(self):\n",
+    "        data = lzma.open(f'train/in.tsv.xz').read().decode('UTF-8').split('\\n')\n",
+    "        data = [line.split('\\t') for line in data][:-1]\n",
+    "        data = [[i[6].replace('\\\\\\\\n', ' '), i[7].replace('\\\\\\\\n', ' ')] for i in data]\n",
+    "        \n",
+    "        words = []\n",
+    "        with open(f'train/expected.tsv') as file:\n",
+    "            tsv_file = csv.reader(file, delimiter=\"\\t\")\n",
+    "            for line in tsv_file:\n",
+    "                words.append(line[0])\n",
+    "                \n",
+    "        text = []\n",
+    "#         for i in range(len(data) - 1):\n",
+    "        for i in range(5000):\n",
+    "            t = data[i][0] + ' ' + words[i] + ' ' + data[i][1] + ' '\n",
+    "            text += [t.replace('\\\\n', ' ')]\n",
+    "            \n",
+    "        text = ' '.join(text).lower()\n",
+    "        text = re.sub('[^a-z ]', '', text)\n",
+    "        text = text.split(' ')\n",
+    "        text.reverse()\n",
+    "        return text\n",
+    "    \n",
+    "    \n",
+    "    def get_uniq_words(self):\n",
+    "        word_counts = Counter(self.words)\n",
+    "        return sorted(word_counts, key=word_counts.get, reverse=True)\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.words_indexes) - self.sequence_length\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        return (\n",
+    "            torch.tensor(self.words_indexes[index:index+self.sequence_length]),\n",
+    "            torch.tensor(self.words_indexes[index+1:index+self.sequence_length+1]),\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "7WSmYLCq-CRG",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "7WSmYLCq-CRG",
+    "outputId": "4fb65144-b4bd-4611-8022-3446faaaf6c9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
+      "{'epoch': 0, 'update in batch': 20417, '/': 25417, 'loss': 7.6362504959106445}\n",
+      "{'epoch': 0, 'update in batch': 20418, '/': 25417, 'loss': 6.8080902099609375}\n",
+      "{'epoch': 0, 'update in batch': 20419, '/': 25417, 'loss': 6.39241886138916}\n",
+      "{'epoch': 0, 'update in batch': 20420, '/': 25417, 'loss': 7.597629547119141}\n",
+      "{'epoch': 0, 'update in batch': 20421, '/': 25417, 'loss': 7.708348274230957}\n",
+      "{'epoch': 0, 'update in batch': 20422, '/': 25417, 'loss': 7.466558933258057}\n",
+      "{'epoch': 0, 'update in batch': 20423, '/': 25417, 'loss': 6.497800350189209}\n",
+      "{'epoch': 0, 'update in batch': 20424, '/': 25417, 'loss': 7.575223445892334}\n",
+      "{'epoch': 0, 'update in batch': 20425, '/': 25417, 'loss': 6.90737247467041}\n",
+      "{'epoch': 0, 'update in batch': 20426, '/': 25417, 'loss': 6.788297176361084}\n",
+      "{'epoch': 0, 'update in batch': 20427, '/': 25417, 'loss': 7.225445747375488}\n",
+      "{'epoch': 0, 'update in batch': 20428, '/': 25417, 'loss': 6.8070173263549805}\n",
+      "{'epoch': 0, 'update in batch': 20429, '/': 25417, 'loss': 6.6609907150268555}\n",
+      "{'epoch': 0, 'update in batch': 20430, '/': 25417, 'loss': 6.863869667053223}\n",
+      "{'epoch': 0, 'update in batch': 20431, '/': 25417, 'loss': 6.732440948486328}\n",
+      "{'epoch': 0, 'update in batch': 20432, '/': 25417, 'loss': 7.342253684997559}\n",
+      "{'epoch': 0, 'update in batch': 20433, '/': 25417, 'loss': 8.839489936828613}\n",
+      "{'epoch': 0, 'update in batch': 20434, '/': 25417, 'loss': 8.181785583496094}\n",
+      "{'epoch': 0, 'update in batch': 20435, '/': 25417, 'loss': 7.501155853271484}\n",
+      "{'epoch': 0, 'update in batch': 20436, '/': 25417, 'loss': 7.15375280380249}\n",
+      "{'epoch': 0, 'update in batch': 20437, '/': 25417, 'loss': 7.610144138336182}\n",
+      "{'epoch': 0, 'update in batch': 20438, '/': 25417, 'loss': 7.9229230880737305}\n",
+      "{'epoch': 0, 'update in batch': 20439, '/': 25417, 'loss': 7.410401344299316}\n",
+      "{'epoch': 0, 'update in batch': 20440, '/': 25417, 'loss': 7.6451520919799805}\n",
+      "{'epoch': 0, 'update in batch': 20441, '/': 25417, 'loss': 7.612285614013672}\n",
+      "{'epoch': 0, 'update in batch': 20442, '/': 25417, 'loss': 6.862789154052734}\n",
+      "{'epoch': 0, 'update in batch': 20443, '/': 25417, 'loss': 7.761195182800293}\n",
+      "{'epoch': 0, 'update in batch': 20444, '/': 25417, 'loss': 7.075577735900879}\n",
+      "{'epoch': 0, 'update in batch': 20445, '/': 25417, 'loss': 6.839912414550781}\n",
+      "{'epoch': 0, 'update in batch': 20446, '/': 25417, 'loss': 6.835267066955566}\n",
+      "{'epoch': 0, 'update in batch': 20447, '/': 25417, 'loss': 6.691529273986816}\n",
+      "{'epoch': 0, 'update in batch': 20448, '/': 25417, 'loss': 7.013882637023926}\n",
+      "{'epoch': 0, 'update in batch': 20449, '/': 25417, 'loss': 6.065933704376221}\n",
+      "{'epoch': 0, 'update in batch': 20450, '/': 25417, 'loss': 7.55593204498291}\n",
+      "{'epoch': 0, 'update in batch': 20451, '/': 25417, 'loss': 8.576960563659668}\n",
+      "{'epoch': 0, 'update in batch': 20452, '/': 25417, 'loss': 7.220167636871338}\n",
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+      "{'epoch': 0, 'update in batch': 25299, '/': 25417, 'loss': 6.848540306091309}\n",
+      "{'epoch': 0, 'update in batch': 25300, '/': 25417, 'loss': 5.803250789642334}\n",
+      "{'epoch': 0, 'update in batch': 25301, '/': 25417, 'loss': 7.998629093170166}\n",
+      "{'epoch': 0, 'update in batch': 25302, '/': 25417, 'loss': 8.790715217590332}\n",
+      "{'epoch': 0, 'update in batch': 25303, '/': 25417, 'loss': 8.942926406860352}\n",
+      "{'epoch': 0, 'update in batch': 25304, '/': 25417, 'loss': 8.820854187011719}\n",
+      "{'epoch': 0, 'update in batch': 25305, '/': 25417, 'loss': 7.981838226318359}\n",
+      "{'epoch': 0, 'update in batch': 25306, '/': 25417, 'loss': 8.629169464111328}\n",
+      "{'epoch': 0, 'update in batch': 25307, '/': 25417, 'loss': 7.077066898345947}\n",
+      "{'epoch': 0, 'update in batch': 25308, '/': 25417, 'loss': 9.014276504516602}\n",
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+      "{'epoch': 0, 'update in batch': 25310, '/': 25417, 'loss': 8.868875503540039}\n",
+      "{'epoch': 0, 'update in batch': 25311, '/': 25417, 'loss': 8.788274765014648}\n",
+      "{'epoch': 0, 'update in batch': 25312, '/': 25417, 'loss': 8.861018180847168}\n",
+      "{'epoch': 0, 'update in batch': 25313, '/': 25417, 'loss': 8.531967163085938}\n",
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+      "{'epoch': 0, 'update in batch': 25316, '/': 25417, 'loss': 7.537032127380371}\n",
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+      "{'epoch': 0, 'update in batch': 25323, '/': 25417, 'loss': 6.274880886077881}\n",
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+      "{'epoch': 0, 'update in batch': 25329, '/': 25417, 'loss': 7.146348476409912}\n",
+      "{'epoch': 0, 'update in batch': 25330, '/': 25417, 'loss': 6.726944923400879}\n",
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+      "{'epoch': 0, 'update in batch': 25332, '/': 25417, 'loss': 7.103501319885254}\n",
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+      "{'epoch': 0, 'update in batch': 25340, '/': 25417, 'loss': 7.581213474273682}\n",
+      "{'epoch': 0, 'update in batch': 25341, '/': 25417, 'loss': 7.073111534118652}\n",
+      "{'epoch': 0, 'update in batch': 25342, '/': 25417, 'loss': 6.28646183013916}\n",
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+      "{'epoch': 0, 'update in batch': 25348, '/': 25417, 'loss': 7.347395420074463}\n",
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+      "{'epoch': 0, 'update in batch': 25353, '/': 25417, 'loss': 7.012775421142578}\n",
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+      "{'epoch': 0, 'update in batch': 25356, '/': 25417, 'loss': 5.941706657409668}\n",
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+      "{'epoch': 0, 'update in batch': 25359, '/': 25417, 'loss': 5.956967830657959}\n",
+      "{'epoch': 0, 'update in batch': 25360, '/': 25417, 'loss': 6.176455020904541}\n",
+      "{'epoch': 0, 'update in batch': 25361, '/': 25417, 'loss': 7.5411376953125}\n",
+      "{'epoch': 0, 'update in batch': 25362, '/': 25417, 'loss': 7.6988091468811035}\n",
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+      "{'epoch': 0, 'update in batch': 25365, '/': 25417, 'loss': 6.0564117431640625}\n",
+      "{'epoch': 0, 'update in batch': 25366, '/': 25417, 'loss': 7.387345790863037}\n",
+      "{'epoch': 0, 'update in batch': 25367, '/': 25417, 'loss': 6.296487808227539}\n",
+      "{'epoch': 0, 'update in batch': 25368, '/': 25417, 'loss': 6.391259670257568}\n",
+      "{'epoch': 0, 'update in batch': 25369, '/': 25417, 'loss': 7.153417110443115}\n",
+      "{'epoch': 0, 'update in batch': 25370, '/': 25417, 'loss': 6.087955951690674}\n",
+      "{'epoch': 0, 'update in batch': 25371, '/': 25417, 'loss': 7.136627197265625}\n",
+      "{'epoch': 0, 'update in batch': 25372, '/': 25417, 'loss': 6.990172386169434}\n",
+      "{'epoch': 0, 'update in batch': 25373, '/': 25417, 'loss': 6.073897838592529}\n",
+      "{'epoch': 0, 'update in batch': 25374, '/': 25417, 'loss': 6.062530517578125}\n",
+      "{'epoch': 0, 'update in batch': 25375, '/': 25417, 'loss': 6.55550479888916}\n",
+      "{'epoch': 0, 'update in batch': 25376, '/': 25417, 'loss': 6.928401947021484}\n",
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+      "{'epoch': 0, 'update in batch': 25378, '/': 25417, 'loss': 8.140416145324707}\n",
+      "{'epoch': 0, 'update in batch': 25379, '/': 25417, 'loss': 7.775589942932129}\n",
+      "{'epoch': 0, 'update in batch': 25380, '/': 25417, 'loss': 7.7539801597595215}\n",
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+      "{'epoch': 0, 'update in batch': 25386, '/': 25417, 'loss': 6.920530796051025}\n",
+      "{'epoch': 0, 'update in batch': 25387, '/': 25417, 'loss': 6.917654991149902}\n",
+      "{'epoch': 0, 'update in batch': 25388, '/': 25417, 'loss': 6.610927581787109}\n",
+      "{'epoch': 0, 'update in batch': 25389, '/': 25417, 'loss': 7.7330427169799805}\n",
+      "{'epoch': 0, 'update in batch': 25390, '/': 25417, 'loss': 6.415653228759766}\n",
+      "{'epoch': 0, 'update in batch': 25391, '/': 25417, 'loss': 7.531132698059082}\n",
+      "{'epoch': 0, 'update in batch': 25392, '/': 25417, 'loss': 8.30880355834961}\n",
+      "{'epoch': 0, 'update in batch': 25393, '/': 25417, 'loss': 7.46967077255249}\n",
+      "{'epoch': 0, 'update in batch': 25394, '/': 25417, 'loss': 6.488274574279785}\n",
+      "{'epoch': 0, 'update in batch': 25395, '/': 25417, 'loss': 8.471769332885742}\n",
+      "{'epoch': 0, 'update in batch': 25396, '/': 25417, 'loss': 9.313520431518555}\n",
+      "{'epoch': 0, 'update in batch': 25397, '/': 25417, 'loss': 8.828144073486328}\n",
+      "{'epoch': 0, 'update in batch': 25398, '/': 25417, 'loss': 8.142800331115723}\n",
+      "{'epoch': 0, 'update in batch': 25399, '/': 25417, 'loss': 8.256523132324219}\n",
+      "{'epoch': 0, 'update in batch': 25400, '/': 25417, 'loss': 9.177298545837402}\n",
+      "{'epoch': 0, 'update in batch': 25401, '/': 25417, 'loss': 7.534204959869385}\n",
+      "{'epoch': 0, 'update in batch': 25402, '/': 25417, 'loss': 7.903322696685791}\n",
+      "{'epoch': 0, 'update in batch': 25403, '/': 25417, 'loss': 8.201879501342773}\n",
+      "{'epoch': 0, 'update in batch': 25404, '/': 25417, 'loss': 8.566686630249023}\n",
+      "{'epoch': 0, 'update in batch': 25405, '/': 25417, 'loss': 7.56796407699585}\n",
+      "{'epoch': 0, 'update in batch': 25406, '/': 25417, 'loss': 7.309334754943848}\n",
+      "{'epoch': 0, 'update in batch': 25407, '/': 25417, 'loss': 7.279814720153809}\n",
+      "{'epoch': 0, 'update in batch': 25408, '/': 25417, 'loss': 7.750349521636963}\n",
+      "{'epoch': 0, 'update in batch': 25409, '/': 25417, 'loss': 7.323698997497559}\n",
+      "{'epoch': 0, 'update in batch': 25410, '/': 25417, 'loss': 7.569639682769775}\n",
+      "{'epoch': 0, 'update in batch': 25411, '/': 25417, 'loss': 5.856103420257568}\n",
+      "{'epoch': 0, 'update in batch': 25412, '/': 25417, 'loss': 5.683631420135498}\n",
+      "{'epoch': 0, 'update in batch': 25413, '/': 25417, 'loss': 7.018422603607178}\n",
+      "{'epoch': 0, 'update in batch': 25414, '/': 25417, 'loss': 6.817176818847656}\n",
+      "{'epoch': 0, 'update in batch': 25415, '/': 25417, 'loss': 6.651888847351074}\n",
+      "{'epoch': 0, 'update in batch': 25416, '/': 25417, 'loss': 6.18330192565918}\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_2 = ReversedDataset(5)\n",
+    "input_tensor_2 = torch.tensor([[  0, 231,  19,  98, 189]], dtype=torch.int32).to(device)\n",
+    "\n",
+    "model_2 = Model(len(dataset_2)).to(device)\n",
+    "y_pred_2, state_h_2 = model(input_tensor_2)\n",
+    "model_2 = Model(vocab_size = len(dataset_2.uniq_words)).to(device)\n",
+    "train(dataset_2, model_2, 1, 64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "9deb871b",
+   "metadata": {
+    "id": "9deb871b"
+   },
+   "outputs": [],
+   "source": [
+    "n = 2\n",
+    "\n",
+    "f = open(\"dev-0/out.tsv\", \"w\")\n",
+    "\n",
+    "for i in range(len(dev_data1)):\n",
+    "    d1 = dev_data1[i]\n",
+    "    d2 = dev_data2[i]\n",
+    "    try:\n",
+    "        tmp = predict2(dataset, model, model_2, ' '.join(d1.split()[-n:]), ' '.join(d2.split()[:n]))\n",
+    "        f.writelines(' '.join([f'{i[0]}:{i[1]}' for i in tmp]) + ' :0.3\\n')\n",
+    "    except:\n",
+    "        f.writelines(':1\\n')\n",
+    "\n",
+    "f.close()\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "yAphUzARtwVU",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "yAphUzARtwVU",
+    "outputId": "770d0f40-cb01-4438-a762-5404608042cf"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10519"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(dev_data1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "R7TvpogchsUy",
+   "metadata": {
+    "id": "R7TvpogchsUy"
+   },
+   "outputs": [],
+   "source": [
+    "test_data = lzma.open(f'test-A/in.tsv.xz').read().decode('UTF-8').split('\\n')\n",
+    "test_data = [line.split('\\t') for line in test_data][:-1]\n",
+    "test_data1 = [re.sub('[^a-z ]', '', i[6].replace('\\\\n', ' ').lower()).strip() for i in test_data]\n",
+    "test_data2 = [re.sub('[^a-z ]', '', i[7].replace('\\\\n', ' ').lower()).strip() for i in test_data]\n",
+    "\n",
+    "\n",
+    "n = 2\n",
+    "\n",
+    "f = open(\"test-A/out.tsv\", \"w\")\n",
+    "\n",
+    "for i in range(len(test_data1)):\n",
+    "    d1 = test_data1[i]\n",
+    "    d2 = test_data2[i]\n",
+    "    try:\n",
+    "        tmp = predict2(dataset, model, model_2, ' '.join(d1.split()[-n:]), ' '.join(d2.split()[:n]))\n",
+    "        f.writelines(' '.join([f'{i[0]}:{i[1]}' for i in tmp]) + ' :0.3\\n')\n",
+    "    except:\n",
+    "        f.writelines(':1\\n')\n",
+    "\n",
+    "f.close()\n",
+    "        "
    ]
   }
  ],
  "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "run.ipynb",
+   "provenance": []
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
diff --git a/run.py b/run.py
index 91d561c..e95e43c 100644
--- a/run.py
+++ b/run.py
@@ -1,130 +1,389 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# MODEL TRIGRAMOWY - uwzględniamy dwa poprzednie słowa
+# In[2]:
 
 
+import torch
+from torch import nn, optim
+from torch.utils.data import DataLoader
+import numpy as np
+from collections import Counter
+import re
 import lzma
 import csv
-import re
-import math
 
 
-def read_data(folder_name, test_data=False):
-    
-    all_data = lzma.open(f'{folder_name}/in.tsv.xz').read().decode('UTF-8').split('\n')
-    data = [line.split('\t') for line in all_data][:-1]
-    data = [[i[6].replace('\\n', ' '), i[7].replace('\\n', ' ')] for i in data]
-    
-    if not test_data:
+# In[3]:
+
+
+device = 'cuda'
+
+
+# In[4]:
+
+
+class Dataset(torch.utils.data.Dataset):
+    def __init__(
+            self,
+            sequence_length,
+    ):
+        self.sequence_length = sequence_length
+        self.words = self.load()
+        self.uniq_words = self.get_uniq_words()
+
+        self.index_to_word = {index: word for index, word in enumerate(self.uniq_words)}
+        self.word_to_index = {word: index for index, word in enumerate(self.uniq_words)}
+
+        self.words_indexes = [self.word_to_index[w] for w in self.words]
+
+    def load(self):
+        data = lzma.open(f'train/in.tsv.xz').read().decode('UTF-8').split('\n')
+        data = [line.split('\t') for line in data][:-1]
+        data = [[i[6].replace('\\\\n', ' '), i[7].replace('\\\\n', ' ')] for i in data]
+        
         words = []
-        with open(f'{folder_name}/expected.tsv') as file:
+        with open(f'train/expected.tsv') as file:
             tsv_file = csv.reader(file, delimiter="\t")
             for line in tsv_file:
                 words.append(line[0])
+                
+        text = []
+#         for i in range(len(data) - 1):
+        for i in range(5000):
+            t = data[i][0] + ' ' + words[i] + ' ' + data[i][1] + ' '
+            text += [t.replace('\\n', ' ')]
             
-        return data, words
+        text = ' '.join(text).lower()
+        text = re.sub('[^a-z ]', '', text)
+        text = text.split(' ')
+        return text
     
-    return data
-
-train_data, train_words = read_data('train')
-
-
-def print_example(data, words, idx):
-    print(f'{data[idx][0]} _____{words[idx].upper()}_____ {data[idx][1]}')
     
-# print_example(train_data, train_words, 13)
+    def get_uniq_words(self):
+        word_counts = Counter(self.words)
+        return sorted(word_counts, key=word_counts.get, reverse=True)
+
+    def __len__(self):
+        return len(self.words_indexes) - self.sequence_length
+
+    def __getitem__(self, index):
+        return (
+            torch.tensor(self.words_indexes[index:index+self.sequence_length]),
+            torch.tensor(self.words_indexes[index+1:index+self.sequence_length+1]),
+        )
 
 
-def generate_N_grams(text, ngram=1, no_punctuation=True):
-    text = re.sub(r'[\-] ', '', text).lower()
-    if no_punctuation:
-        text = re.sub(r'[^\w\s]', ' ', text)
-    words=[word for word in text.split()]
-    temp=zip(*[words[i:] for i in range(0,ngram)])
-    ans=[' '.join(ngram) for ngram in temp]
-    return ans
-
-N_grams = []
-for i in range(len(train_data[:5000])):
-    N_grams += generate_N_grams(f'{train_data[i][0]} {train_words[i]} {train_data[i][1]}', 2)
-    N_grams += generate_N_grams(f'{train_data[i][0]} {train_words[i]} {train_data[i][1]}', 3)
+# In[5]:
 
 
-def check_prob(N_grams):
-    count = {}
-    for i in N_grams:
-        i = i.rsplit(maxsplit=1)
-        if i[0] in count:
-            if i[1] in count[i[0]]:
-                count[i[0]][i[1]] += 1
-            else:
-                count[i[0]][i[1]] = 1
-        else:
-            count[i[0]] = {i[1]: 1}
+dataset = Dataset(5)
+
+
+# In[6]:
+
+
+dataset[200]
+
+
+# In[7]:
+
+
+[dataset.index_to_word[x] for x in [  0, 231,  19,  98, 189]]
+
+
+# In[8]:
+
+
+[dataset.index_to_word[x] for x in [231,  19,  98, 189,   5]]
+
+
+# In[9]:
+
+
+input_tensor = torch.tensor([[  0, 231,  19,  98, 189]], dtype=torch.int32).to(device)
+
+
+# In[ ]:
+
+
+class Model(nn.Module):
+    def __init__(self, vocab_size):
+        super(Model, self).__init__()
+        self.lstm_size = 128
+        self.embedding_dim = 128
+        self.num_layers = 3
+
+        self.embedding = nn.Embedding(
+            num_embeddings=vocab_size,
+            embedding_dim=self.embedding_dim,
+        )
+        self.lstm = nn.LSTM(
+            input_size=self.lstm_size,
+            hidden_size=self.lstm_size,
+            num_layers=self.num_layers,
+            dropout=0.2,
+        )
+        self.fc = nn.Linear(self.lstm_size, vocab_size)
+
+    def forward(self, x, prev_state = None):
+        embed = self.embedding(x)
+        output, state = self.lstm(embed, prev_state)
+        logits = self.fc(output)
+        return logits, state
+
+    def init_state(self, sequence_length):
+        return (torch.zeros(self.num_layers, sequence_length, self.lstm_size).to(device),
+                torch.zeros(self.num_layers, sequence_length, self.lstm_size).to(device))
+
+
+# In[ ]:
+
+
+model = Model(len(dataset)).to(device)
+
+
+# In[ ]:
+
+
+y_pred, state_h = model(input_tensor)
+
+
+# In[ ]:
+
+
+y_pred
+
+
+# In[ ]:
+
+
+y_pred.shape
+
+
+# In[ ]:
+
+
+def train(dataset, model, max_epochs, batch_size):
+    model.train()
+
+    dataloader = DataLoader(dataset, batch_size=batch_size)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+    for epoch in range(max_epochs):
+        for batch, (x, y) in enumerate(dataloader):
+            optimizer.zero_grad()
+            x = x.to(device)
+            y = y.to(device)
+
+            y_pred, state_h = model(x)
+            loss = criterion(y_pred.transpose(1, 2), y)
+
+            loss.backward()
+            optimizer.step()
+
+            print({ 'epoch': epoch, 'update in batch': batch, '/' : len(dataloader), 'loss': loss.item() })
+
+
+# In[ ]:
+
+
+model = Model(vocab_size = len(dataset.uniq_words)).to(device)
+train(dataset, model, 1, 64)
+
+
+# In[ ]:
+
+
+def predict(dataset, model, text, next_words=5):
+    model.eval()
+    words = text.split(' ')
+    state_h = model.init_state(len(words))
+    res = []
+
+    x = torch.tensor([[dataset.word_to_index[w] for w in words]]).to(device)
+    y_pred, state_h = model(x, state_h)
+
+    last_word_logits = y_pred[0][-1]
+    p = torch.nn.functional.softmax(last_word_logits, dim=0).detach().cpu().numpy()
+    tmp = sorted(zip(p, range(len(p))), reverse=True)[:next_words]
+    for w in tmp:
+        res.append((dataset.index_to_word[w[1]], w[0]))
+
+    return res
+
+def predict2(dataset, model, model2, text, text2, next_words=5):
+    model.eval()
+    model2.eval()
+    words = text.split(' ')
+    words2 = text2.split(' ')
+    words2.reverse()
+    state_h = model.init_state(len(words))
+    state_h_2 = model2.init_state(len(words))
+    res = []
+
+    x = torch.tensor([[dataset.word_to_index[w] for w in words]]).to(device)
+    x2 = torch.tensor([[dataset.word_to_index[w] for w in words2]]).to(device)
+    y_pred, state_h = model(x, state_h)
+    y_pred_2, state_h_2 = model2(x2, state_h_2)
+
+    last_word_logits = y_pred[0][-1]
+    last_word_logits_2 = y_pred_2[0][-1]
+    p = torch.nn.functional.softmax(last_word_logits, dim=0).detach().cpu().numpy()
+    p2 = torch.nn.functional.softmax(last_word_logits_2, dim=0).detach().cpu().numpy()
+
+    p_mean = [(g + h) / 2 for g, h in zip(p, p2)]
+
+    tmp = sorted(zip(p_mean, range(len(p_mean))), reverse=True)[:next_words]
+    for w in tmp:
+        res.append((dataset.index_to_word[w[1]], w[0]))
+
+    return res
+
+
+# In[ ]:
+
+
+predict(dataset, model, 'it is a')
+
+
+# In[69]:
+
+
+dev_data = lzma.open(f'dev-0/in.tsv.xz').read().decode('UTF-8').split('\n')
+dev_data = [line.split('\t') for line in dev_data][:-1]
+dev_data1 = [re.sub('[^a-z ]', '', i[6].replace('\\n', ' ').lower()).strip() for i in dev_data]
+dev_data2 = [re.sub('[^a-z ]', '', i[7].replace('\\n', ' ').lower()).strip() for i in dev_data]
+
+
+# In[23]:
+
+
+dev_data[0]
+
+
+# In[54]:
+
+
+print(predict(dataset, model, ' '.join(dev_data[9].split()[-1:])))
+
+
+# In[66]:
+
+
+class ReversedDataset(torch.utils.data.Dataset):
+    def __init__(
+            self,
+            sequence_length,
+    ):
+        self.sequence_length = sequence_length
+        self.words = self.load()
+        self.uniq_words = self.get_uniq_words()
+
+        self.index_to_word = {index: word for index, word in enumerate(self.uniq_words)}
+        self.word_to_index = {word: index for index, word in enumerate(self.uniq_words)}
+
+        self.words_indexes = [self.word_to_index[w] for w in self.words]
+
+    def load(self):
+        data = lzma.open(f'train/in.tsv.xz').read().decode('UTF-8').split('\n')
+        data = [line.split('\t') for line in data][:-1]
+        data = [[i[6].replace('\\\\n', ' '), i[7].replace('\\\\n', ' ')] for i in data]
+        
+        words = []
+        with open(f'train/expected.tsv') as file:
+            tsv_file = csv.reader(file, delimiter="\t")
+            for line in tsv_file:
+                words.append(line[0])
+                
+        text = []
+#         for i in range(len(data) - 1):
+        for i in range(5000):
+            t = data[i][0] + ' ' + words[i] + ' ' + data[i][1] + ' '
+            text += [t.replace('\\n', ' ')]
             
-    for word in count:
-        s = sum(count[word].values())
-        for i in count[word]:
-            count[word][i] = count[word][i] / s
-            
-    return count
-
-probs = check_prob(N_grams)
-
-
-dev_data, dev_words = read_data('dev-0')
-
-
-def find_word(word_1, word_2):
-    tmp_probs = {}
-    if word_1 in probs:
-        if word_2 in probs:
-            for i in probs[word_1]:
-                if i in probs[word_2]:
-                    tmp_probs[i] = probs[word_1][i] * probs[word_2][i]
-                    if tmp_probs[i] == 1:
-                        tmp_probs[i] = 0.1
-                else:
-                    tmp_probs[i] = probs[word_1][i] / 5
-        else:
-            tmp_probs = probs[word_1]
-    else:
-        tmp_probs = {}
+        text = ' '.join(text).lower()
+        text = re.sub('[^a-z ]', '', text)
+        text = text.split(' ')
+        text.reverse()
+        return text
     
-    sorted_list = sorted(tmp_probs.items(), key=lambda x: x[1], reverse=True)[:1]
-    tmm = ' '.join([i[0] + ':' + str(i[1]) for i in sorted_list])
-    s = 1 - sum(n for _, n in sorted_list)
-    if s == 0:
-        s = 0.01
-    tmm += ' :' + str(s)
-    if tmp_probs == {}:
-        return ':1'
-    return tmm
-
-
-def find_words(data):
-    found_words = []
-    for i in data:
-        t = i[0]
-        t = re.sub(r'[\-] ', '', t).lower()
-        if True:
-            t = re.sub(r'[^\w\s]', ' ', t)
-        words=[word for word in t.split()]
-        found_words.append(find_word(words[-1], ' '.join(words[-2:])))
-    return found_words
-
-dev_found_words = find_words(dev_data)
-
-
-def save_data(folder, words):
-    f = open(f'{folder}/out.tsv', 'w')
-    f.write('\n'.join(words) + '\n')
-    f.close()
     
-save_data('dev-0', dev_found_words)
+    def get_uniq_words(self):
+        word_counts = Counter(self.words)
+        return sorted(word_counts, key=word_counts.get, reverse=True)
+
+    def __len__(self):
+        return len(self.words_indexes) - self.sequence_length
+
+    def __getitem__(self, index):
+        return (
+            torch.tensor(self.words_indexes[index:index+self.sequence_length]),
+            torch.tensor(self.words_indexes[index+1:index+self.sequence_length+1]),
+        )
 
 
-test_data = read_data('test-A', True)
-test_found_words = find_words(test_data)
-save_data('test-A', test_found_words)
+# In[67]:
+
+
+dataset_2 = ReversedDataset(5)
+input_tensor_2 = torch.tensor([[  0, 231,  19,  98, 189]], dtype=torch.int32).to(device)
+
+model_2 = Model(len(dataset_2)).to(device)
+y_pred_2, state_h_2 = model(input_tensor_2)
+model_2 = Model(vocab_size = len(dataset_2.uniq_words)).to(device)
+train(dataset_2, model_2, 1, 64)
+
+
+# In[96]:
+
+
+n = 2
+
+f = open("dev-0/out.tsv", "w")
+
+for i in range(len(dev_data1)):
+    d1 = dev_data1[i]
+    d2 = dev_data2[i]
+    try:
+        tmp = predict2(dataset, model, model_2, ' '.join(d1.split()[-n:]), ' '.join(d2.split()[:n]))
+        f.writelines(' '.join([f'{i[0]}:{i[1]}' for i in tmp]) + ' :0.3\n')
+    except:
+        f.writelines(':1\n')
+
+f.close()
+        
+
+
+# In[95]:
+
+
+len(dev_data1)
+
+
+# In[93]:
+
+
+test_data = lzma.open(f'test-A/in.tsv.xz').read().decode('UTF-8').split('\n')
+test_data = [line.split('\t') for line in test_data][:-1]
+test_data1 = [re.sub('[^a-z ]', '', i[6].replace('\\n', ' ').lower()).strip() for i in test_data]
+test_data2 = [re.sub('[^a-z ]', '', i[7].replace('\\n', ' ').lower()).strip() for i in test_data]
+
+
+n = 2
+
+f = open("test-A/out.tsv", "w")
+
+for i in range(len(test_data1)):
+    d1 = test_data1[i]
+    d2 = test_data2[i]
+    try:
+        tmp = predict2(dataset, model, model_2, ' '.join(d1.split()[-n:]), ' '.join(d2.split()[:n]))
+        f.writelines(' '.join([f'{i[0]}:{i[1]}' for i in tmp]) + ' :0.3\n')
+    except:
+        f.writelines(':1\n')
+
+f.close()
+        
+
diff --git a/test-A/out.tsv b/test-A/out.tsv
deleted file mode 100644
index 9108ec3..0000000
--- a/test-A/out.tsv
+++ /dev/null
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