diff --git a/dev-0/out.tsv b/dev-0/out.tsv
index 72709b9..a1a3544 100644
--- a/dev-0/out.tsv
+++ b/dev-0/out.tsv
@@ -1,10519 +1,10519 @@
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diff --git a/src/08_word2vec.ipynb b/src/08_word2vec.ipynb
new file mode 100644
index 0000000..2aa9954
--- /dev/null
+++ b/src/08_word2vec.ipynb
@@ -0,0 +1,643 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# <b>Trigram</b> neural network model for gap fill task"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import re\n",
+    "import nltk\n",
+    "import os\n",
+    "import csv\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "from torch.utils.data import DataLoader, TensorDataset\n",
+    "from bidict import bidict\n",
+    "import math\n",
+    "from sklearn.utils import shuffle\n",
+    "from collections import Counter\n",
+    "import random\n",
+    "from torchtext.vocab import build_vocab_from_iterator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['CUDA_LAUNCH_BLOCKING'] = '1'\n",
+    "os.environ['TORCH_USE_CUDA_DSA'] = '1'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Global configuration variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vocab_size = 40_000\n",
+    "batch_size = 256\n",
+    "embedding_dim = 128\n",
+    "hidden_dim = 1024\n",
+    "learning_rate = 0.001\n",
+    "epochs = 5\n",
+    "\n",
+    "output_size = vocab_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "# device = torch.device(\"cpu\")\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build vocabulary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_word_lines_from_dataset():\n",
+    "    dataset_dir = os.path.join('..', 'train', 'in.tsv.xz')\n",
+    "    expected_dir = os.path.join('..', 'train', 'expected.tsv')\n",
+    "\n",
+    "    df = pd.read_csv(dataset_dir, sep='\\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE, dtype=str, chunksize=1000)\n",
+    "    expected_df = pd.read_csv(expected_dir, sep='\\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, dtype=str, chunksize=1000)\n",
+    "\n",
+    "    for j, (df, expected_df) in tqdm(enumerate(zip(df, expected_df)), total=433):\n",
+    "        df = df.replace(r'\\\\r+|\\\\n+|\\\\t+', ' ', regex=True)\n",
+    "        \n",
+    "        for left_context, word, right_context in zip(df['LeftContext'].to_list(), expected_df['Word'].to_list(), df['RightContext'].to_list()):\n",
+    "            yield re.split(r\"\\s+\", left_context.strip()) + [str(word).strip()] + re.split(r\"\\s+\", right_context.strip())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/433 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 433/433 [01:34<00:00,  4.60it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab = build_vocab_from_iterator(\n",
+    "    get_word_lines_from_dataset(),\n",
+    "    max_tokens = vocab_size,\n",
+    "    specials = ['<unk>'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vocab.set_default_index(vocab['<unk>'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['<unk>', 'the', 'of', 'me.']"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vocab.lookup_tokens([0, 1, 2, 1245])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 433/433 [01:06<00:00,  6.50it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_dir = os.path.join('..', 'train', 'in.tsv.xz')\n",
+    "expected_dir = os.path.join('..', 'train', 'expected.tsv')\n",
+    "\n",
+    "df = pd.read_csv(dataset_dir, sep='\\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE, dtype=str, chunksize=1000)\n",
+    "expected_df = pd.read_csv(expected_dir, sep='\\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, dtype=str, chunksize=1000)\n",
+    "\n",
+    "\n",
+    "input_corpus = []\n",
+    "target_corpus = []\n",
+    "\n",
+    "left_tokens = 2\n",
+    "right_tokens = 2\n",
+    "\n",
+    "for j, (df, expected_df) in tqdm(enumerate(zip(df, expected_df)), total=433):\n",
+    "    df = df.replace(r'\\\\r+|\\\\n+|\\\\t+', ' ', regex=True)\n",
+    "    \n",
+    "    for left_context, word, right_context in zip(df['LeftContext'].to_list(), expected_df['Word'].to_list(), df['RightContext'].to_list()):\n",
+    "        target_corpus.append([str(word).strip()])\n",
+    "        input_corpus.append(re.split(r\"\\s+\", left_context.strip())[-left_tokens:] + re.split(r\"\\s+\", right_context.strip())[:right_tokens])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tokenize entire corpus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 432022/432022 [00:02<00:00, 168428.47it/s]\n",
+      "100%|██████████| 432022/432022 [00:01<00:00, 332294.03it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def tokenize(w):\n",
+    "    return vocab[w]\n",
+    "    \n",
+    "def detokenize(t):\n",
+    "    return vocab.lookup_tokens([t])[0]\n",
+    "\n",
+    "tokenized_input_corpus = []\n",
+    "tokenized_target_corpus = []\n",
+    "\n",
+    "for words in tqdm(input_corpus):\n",
+    "    tokenized_input_corpus.append([tokenize(word) for word in words])\n",
+    "\n",
+    "for words in tqdm(target_corpus):\n",
+    "    tokenized_target_corpus.append([tokenize(word) for word in words])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_input_corpus, tokenized_target_corpus = shuffle(tokenized_input_corpus, tokenized_target_corpus)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "indices = np.nonzero(np.array(tokenized_target_corpus).flatten())\n",
+    "\n",
+    "tokenized_input_corpus = np.take(tokenized_input_corpus, indices, axis=0)\n",
+    "tokenized_target_corpus = np.take(tokenized_target_corpus, indices, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_corpus_tensor = torch.flatten(torch.tensor(tokenized_input_corpus, dtype=torch.long, device=device), end_dim=-2)\n",
+    "target_corpus_tensor = torch.flatten(torch.tensor(tokenized_target_corpus, dtype=torch.long, device=device)).reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([378666, 4])\n",
+      "torch.Size([378666, 1])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(input_corpus_tensor.size())\n",
+    "print(target_corpus_tensor.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['silver', 'republicans', 'silver', 'den']\n",
+      "['and']\n"
+     ]
+    }
+   ],
+   "source": [
+    "random_index = random.randint(0, len(input_corpus_tensor) - 1)\n",
+    "\n",
+    "# Get random element from input corpus\n",
+    "random_input_element = input_corpus_tensor[random_index]\n",
+    "\n",
+    "# Get corresponding element from target corpus\n",
+    "random_target_element = target_corpus_tensor[random_index]\n",
+    "\n",
+    "print([detokenize(int(idx)) for idx in random_input_element])\n",
+    "print([detokenize(int(idx)) for idx in random_target_element])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = TensorDataset(input_corpus_tensor, target_corpus_tensor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define the trigram neural network model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TrigramNN(nn.Module):\n",
+    "    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_size):\n",
+    "        super(TrigramNN, self).__init__()\n",
+    "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
+    "        self.linear = nn.Linear(embedding_dim * (left_tokens + right_tokens), output_size)\n",
+    "        \n",
+    "    def forward(self, inputs):\n",
+    "        out = self.embedding(inputs)\n",
+    "        out = out.view(inputs.size(0), -1)\n",
+    "        out = self.linear(out)\n",
+    "        # out = torch.softmax(out, dim=1)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize the model, loss function, and optimizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = TrigramNN(vocab_size, embedding_dim, hidden_dim, output_size)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/1480 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1480/1480 [00:32<00:00, 44.97it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, Loss: 7.505966403999844\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1480/1480 [00:32<00:00, 45.57it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2, Loss: 5.1014555966531905\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1480/1480 [00:32<00:00, 45.42it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3, Loss: 3.835972652886365\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1480/1480 [00:32<00:00, 45.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4, Loss: 3.1567180975063427\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1480/1480 [00:32<00:00, 44.90it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5, Loss: 2.749172909517546\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.to(device)\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    total_loss = 0\n",
+    "    for batch_inputs, batch_targets in tqdm(dataloader):\n",
+    "        batch_inputs, batch_targets = batch_inputs.to(device), batch_targets.to(device)\n",
+    "        \n",
+    "        model.zero_grad()\n",
+    "        output = model(batch_inputs)\n",
+    "\n",
+    "        loss = criterion(output, batch_targets.view(-1))\n",
+    "        total_loss += loss.item()\n",
+    "\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "    print(f\"Epoch {epoch+1}, Loss: {total_loss/len(dataloader)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## test the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(left_context, right_context):\n",
+    "    with torch.no_grad():\n",
+    "        context = left_context + right_context\n",
+    "        test_context_idxs = torch.tensor([[tokenize(x) for x in context]], device=device)\n",
+    "        output = model(test_context_idxs)\n",
+    "        top_predicted_scores, top_predicted_indices = torch.topk(output, vocab_size)\n",
+    "\n",
+    "        top_predicted_scores = np.array(top_predicted_scores[0].cpu())\n",
+    "        top_predicted_indices = top_predicted_indices[0]\n",
+    "\n",
+    "        top_predicted_scores = top_predicted_scores[top_predicted_scores > 0]\n",
+    "        top_predicted_indices = top_predicted_indices[:len(top_predicted_scores)]\n",
+    "\n",
+    "        total_score = np.sum([score for score in top_predicted_scores[:20]])\n",
+    "\n",
+    "        predictions = list(zip(top_predicted_scores, top_predicted_indices))\n",
+    "        predictions = [(round(float(score), 2), detokenize(idx)) for score, idx in predictions[:10]]\n",
+    "        \n",
+    "        words = [word for _, word in predictions]\n",
+    "        scores = [round(score/total_score, 2) for score, _ in predictions]\n",
+    "\n",
+    "        remaining_score = round(1.0 - np.sum(scores), 2)\n",
+    "\n",
+    "        predictions = ' '.join([f\"{word}:{score}\" for score, word in zip(scores, words)]) + ' :' + str(remaining_score)\n",
+    "\n",
+    "        return predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a:0.07 the:0.06 The:0.06 A:0.06 my:0.05 ,:0.05 to:0.05 and:0.05 -:0.05 an:0.05 :0.45\n",
+      "of:0.07 on:0.06 and:0.06 be:0.06 for:0.05 in:0.05 school,:0.05 ol:0.05 it:0.05 the:0.05 :0.45\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(predict([\"came\", \"fiom\"], [\"26th\", \"place\"]))\n",
+    "print(predict([\"will\", \"buy\"], [\"telephone\", \"and\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate result for dev dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_dir = os.path.join('..', 'dev-0', 'in.tsv.xz')\n",
+    "output_dir = os.path.join('..', 'dev-0', 'out.tsv')\n",
+    "\n",
+    "df = pd.read_csv(dataset_dir, sep='\\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE)\n",
+    "df = df.replace(r'\\\\r+|\\\\n+|\\\\t+', ' ', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10519/10519 [00:51<00:00, 203.44it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "final = \"\"\n",
+    "\n",
+    "for i, (_, row) in tqdm(enumerate(df.iterrows()), total=len(df)):\n",
+    "    left_context = re.split(r\"\\s+\", row['LeftContext'].strip())[-left_tokens:]\n",
+    "    right_context = re.split(r\"\\s+\", row['RightContext'].strip())[:right_tokens]\n",
+    "\n",
+    "    final += predict(left_context, right_context) + '\\n'\n",
+    "\n",
+    "with open(output_dir, 'w', encoding=\"UTF-8\") as f:\n",
+    "    f.write(final)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "p311-cu121",
+   "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": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}