diff --git a/dev-0/out.tsv b/dev-0/out.tsv
index d48df39..72709b9 100644
--- a/dev-0/out.tsv
+++ b/dev-0/out.tsv
@@ -1,10519 +1,10519 @@
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diff --git a/run.py b/run.py
index 1c26c89..3dd8194 100644
--- a/run.py
+++ b/run.py
@@ -18,9 +18,32 @@ import sys
 import numpy as np
 from torch.utils.data import DataLoader, TensorDataset
 from bidict import bidict
-import torchtext.vocab as vocab
 import math
+from sklearn.utils import shuffle
 from collections import Counter
+import random
+
+# %%
+os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
+os.environ['TORCH_USE_CUDA_DSA'] = '1'
+
+# %% [markdown]
+# ## Global configuration variables
+
+# %%
+vocab_size = 60_000
+batch_size = 64
+embedding_dim = 64
+hidden_dim = 1024
+learning_rate = 0.001
+epochs = 20
+
+output_size = vocab_size
+
+# %%
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cpu")
+print(device)
 
 # %% [markdown]
 # ## Load train data corpus
@@ -32,21 +55,37 @@ expected_dir = os.path.join('..', 'train', 'expected.tsv')
 df = pd.read_csv(dataset_dir, sep='\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE, dtype=str, chunksize=1000)
 expected_df = pd.read_csv(expected_dir, sep='\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, dtype=str, chunksize=1000)
 
-corpus = []
+
+input_corpus = []
+target_corpus = []
+
+left_tokens = 1
+right_tokens = 1
+
 for j, (df, expected_df) in tqdm(enumerate(zip(df, expected_df)), total=433):
     df = df.replace(r'\\r+|\\n+|\\t+', ' ', regex=True)
     
-    for left_context, word, right_context in zip(df['LeftContext'].to_list(), expected_df['Word'].to_list(), df['LeftContext'].to_list()):
-        corpus.extend(re.split(r"\s+", left_context.strip()) + [str(word).strip()] + re.split(r"\s+", right_context.strip()))
+    for left_context, word, right_context in zip(df['LeftContext'].to_list(), expected_df['Word'].to_list(), df['RightContext'].to_list()):
+        target_corpus.append([str(word).strip()])
+        input_corpus.append(re.split(r"\s+", left_context.strip())[-left_tokens:] + re.split(r"\s+", right_context.strip())[:right_tokens])
 
 # %% [markdown]
 # ## Create dictionaries for mapping words to indices
 
 # %%
-word_to_ix = bidict({})
-counts = Counter(corpus)
+def flatten(matrix):
+    flat_list = []
+    for row in matrix:
+        flat_list += row
+    return flat_list
 
-for word, _ in tqdm(counts.most_common(1_500_000)):
+# %%
+word_to_ix = bidict({})
+words_corpus = flatten(input_corpus) + flatten(target_corpus)
+
+counts = Counter(words_corpus)
+
+for word, _ in tqdm(counts.most_common(vocab_size - 1)):
     if word not in word_to_ix:
         word_to_ix[word] = len(word_to_ix) + 1
 
@@ -54,39 +93,58 @@ for word, _ in tqdm(counts.most_common(1_500_000)):
 # ## Tokenize entire corpus
 
 # %%
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
 def tokenize(w):
     if w in word_to_ix:
         return word_to_ix[w]
     else:
         return 0
 
-tokenized_corpus = []
+tokenized_input_corpus = []
+tokenized_target_corpus = []
 
-for word in tqdm(corpus):
-    tokenized_corpus.append(tokenize(word))
+for words in tqdm(input_corpus):
+    tokenized_input_corpus.append([tokenize(word) for word in words])
+
+for words in tqdm(target_corpus):
+    tokenized_target_corpus.append([tokenize(word) for word in words])
+
+# %%
+tokenized_input_corpus, tokenized_target_corpus = shuffle(tokenized_input_corpus, tokenized_target_corpus)
 
 # %% [markdown]
-# ## Create n-grams
+# ## Create dataset
 
 # %%
-tokenized_training_corpus = []
-ngrams = list(nltk.ngrams(tokenized_corpus, n=7))
-np.random.shuffle(ngrams)
-ngrams = ngrams[:100_000]
-ngrams_tensor = torch.tensor(ngrams, dtype=torch.long, device=device)
+indices = np.nonzero(np.array(tokenized_target_corpus).flatten())
 
-indices = torch.any(ngrams_tensor == 0, dim=1)
-ngrams_tensor = ngrams_tensor[~indices]
+tokenized_input_corpus = np.take(tokenized_input_corpus, indices, axis=0)
+tokenized_target_corpus = np.take(tokenized_target_corpus, indices, axis=0)
 
 # %%
-target_tensor = ngrams_tensor[:, 3].reshape(-1, 1).to(device)
-input_tensor = torch.cat((ngrams_tensor[:, :3], ngrams_tensor[:, 4:]), dim=1).to(device)
+input_corpus_tensor = torch.flatten(torch.tensor(tokenized_input_corpus, dtype=torch.long, device=device), end_dim=-2)
+target_corpus_tensor = torch.flatten(torch.tensor(tokenized_target_corpus, dtype=torch.long, device=device)).reshape(-1, 1)
 
 # %%
-batched_input_tensor = torch.split(input_tensor, 512)
-batched_target_tensor = torch.split(target_tensor, 512)
+print(input_corpus_tensor.size())
+print(target_corpus_tensor.size())
+
+# %%
+random_index = random.randint(0, len(input_corpus_tensor) - 1)
+
+# Get random element from input corpus
+random_input_element = input_corpus_tensor[random_index]
+
+# Get corresponding element from target corpus
+random_target_element = target_corpus_tensor[random_index]
+
+print([word_to_ix.inverse[int(idx)] if int(idx) > 0 else '<UNK>' for idx in random_input_element])
+print([word_to_ix.inverse[int(idx)] if int(idx) > 0 else '<UNK>' for idx in random_target_element])
+
+# %%
+dataset = TensorDataset(input_corpus_tensor[:10_000], target_corpus_tensor[:10_000])
+
+# %%
+dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
 
 # %% [markdown]
 # ## Define the trigram neural network model
@@ -95,28 +153,17 @@ batched_target_tensor = torch.split(target_tensor, 512)
 class TrigramNN(nn.Module):
     def __init__(self, vocab_size, embedding_dim, hidden_dim, output_size):
         super(TrigramNN, self).__init__()
-        self.embedding = nn.Embedding(vocab_size, 50)
-        self.linear1 = nn.Linear(50 * 6, output_size)
-    
+        self.embedding = nn.Embedding(vocab_size, embedding_dim)
+        self.linear1 = nn.Linear(embedding_dim * (left_tokens + right_tokens), hidden_dim)
+        self.linear2 = nn.Linear(hidden_dim, output_size)
+        
     def forward(self, inputs):
         out = self.embedding(inputs)
         out = out.view(inputs.size(0), -1)
-        out = self.linear1(out)
-        out = torch.softmax(out, dim=1)
+        out = torch.softmax(self.linear1(out), dim=1)
+        out = self.linear2(out)
         return out
 
-# %% [markdown]
-# ## Define training parameters
-
-# %%
-batch_size = 512
-vocab_size = len(word_to_ix) + 1
-embedding_dim = 10
-hidden_dim = 64
-output_size = vocab_size
-learning_rate = 0.005
-epochs = 1
-
 # %% [markdown]
 # ## Initialize the model, loss function, and optimizer
 
@@ -131,11 +178,11 @@ optimizer = optim.SGD(model.parameters(), lr=learning_rate)
 # %%
 model.to(device)
 
-batches = list(zip(batched_input_tensor, batched_target_tensor))
-
 for epoch in range(epochs):
     total_loss = 0
-    for batch_inputs, batch_targets in tqdm(batches):
+    for batch_inputs, batch_targets in tqdm(dataloader):
+        batch_inputs, batch_targets = batch_inputs.to(device), batch_targets.to(device)
+        
         model.zero_grad()
         output = model(batch_inputs)
 
@@ -145,7 +192,7 @@ for epoch in range(epochs):
         loss.backward()
         optimizer.step()
 
-    print(f"Epoch {epoch+1}, Loss: {total_loss/len(batches)}")
+    print(f"Epoch {epoch+1}, Loss: {total_loss/len(dataloader)}")
 
 # %% [markdown]
 # ## Write function to convert index to word
@@ -168,14 +215,13 @@ def predict(left_context, right_context):
         output = model(test_context_idxs)
         top_predicted_scores, top_predicted_indices = torch.topk(output, 5)
         predictions = list(zip(top_predicted_scores[0], top_predicted_indices[0]))
-        predictions = [(round(float(score), 2), idx_to_word(idx)) for score, idx in predictions]
+        predictions = [(float(score), idx_to_word(idx)) for score, idx in predictions]
         total_score = np.sum([score for score, _ in predictions])
-        predictions = ' '.join([f"{word}:{round(score/total_score, 2)}" for score, word in predictions]) + ' :0.01'
+        predictions = ' '.join([f"{word}:{score}" for score, word in predictions]) + ' :' + str(1.0 - total_score)
         return predictions
 
 # %%
-test_context = ["came", "fiom", "the", "place", "to", "this"]
-print(predict(test_context[:3], test_context[3:]))
+print(predict(["came", "fiom"], []))
 
 # %% [markdown]
 # # Generate result for dev dataset
@@ -191,8 +237,8 @@ df = df.replace(r'\\r+|\\n+|\\t+', ' ', regex=True)
 final = ""
 
 for i, (_, row) in tqdm(enumerate(df.iterrows()), total=len(df)):
-    left_context = re.split(r"\s+", row['LeftContext'].strip())[-3:]
-    right_context = re.split(r"\s+", row['RightContext'].strip())[:3]
+    left_context = re.split(r"\s+", row['LeftContext'].strip())[-left_tokens:]
+    right_context = re.split(r"\s+", row['RightContext'].strip())[:right_tokens]
 
     final += predict(left_context, right_context) + '\n'
 
diff --git a/src/07_trigram_neural.ipynb b/src/07_trigram_neural.ipynb
index c68c508..2a58a7d 100644
--- a/src/07_trigram_neural.ipynb
+++ b/src/07_trigram_neural.ipynb
@@ -16,24 +16,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 414,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Marcin\\.conda\\envs\\p311-cu121\\Lib\\site-packages\\torchtext\\vocab\\__init__.py:4: UserWarning: \n",
-      "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
-      "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
-      "  warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n",
-      "c:\\Users\\Marcin\\.conda\\envs\\p311-cu121\\Lib\\site-packages\\torchtext\\utils.py:4: UserWarning: \n",
-      "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
-      "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
-      "  warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tqdm import tqdm\n",
     "import re\n",
@@ -48,9 +33,62 @@
     "import numpy as np\n",
     "from torch.utils.data import DataLoader, TensorDataset\n",
     "from bidict import bidict\n",
-    "import torchtext.vocab as vocab\n",
     "import math\n",
-    "from collections import Counter"
+    "from sklearn.utils import shuffle\n",
+    "from collections import Counter\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 415,
+   "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": 416,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vocab_size = 60_000\n",
+    "batch_size = 64\n",
+    "embedding_dim = 64\n",
+    "hidden_dim = 1024\n",
+    "learning_rate = 0.001\n",
+    "epochs = 20\n",
+    "\n",
+    "output_size = vocab_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 417,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n"
+     ]
+    }
+   ],
+   "source": [
+    "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "device = torch.device(\"cpu\")\n",
+    "print(device)"
    ]
   },
   {
@@ -62,14 +100,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 418,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 433/433 [01:10<00:00,  6.12it/s]\n"
+      "  0%|          | 0/433 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 433/433 [01:03<00:00,  6.77it/s]\n"
      ]
     }
    ],
@@ -80,12 +125,19 @@
     "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",
-    "corpus = []\n",
+    "\n",
+    "input_corpus = []\n",
+    "target_corpus = []\n",
+    "\n",
+    "left_tokens = 1\n",
+    "right_tokens = 1\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['LeftContext'].to_list()):\n",
-    "        corpus.extend(re.split(r\"\\s+\", left_context.strip()) + [str(word).strip()] + re.split(r\"\\s+\", right_context.strip()))"
+    "    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])"
    ]
   },
   {
@@ -97,22 +149,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 419,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def flatten(matrix):\n",
+    "    flat_list = []\n",
+    "    for row in matrix:\n",
+    "        flat_list += row\n",
+    "    return flat_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 420,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1500000/1500000 [00:11<00:00, 128039.35it/s]\n"
+      "100%|██████████| 59999/59999 [00:00<00:00, 131034.12it/s]\n"
      ]
     }
    ],
    "source": [
     "word_to_ix = bidict({})\n",
-    "counts = Counter(corpus)\n",
+    "words_corpus = flatten(input_corpus) + flatten(target_corpus)\n",
     "\n",
-    "for word, _ in tqdm(counts.most_common(1_500_000)):\n",
+    "counts = Counter(words_corpus)\n",
+    "\n",
+    "for word, _ in tqdm(counts.most_common(vocab_size - 1)):\n",
     "    if word not in word_to_ix:\n",
     "        word_to_ix[word] = len(word_to_ix) + 1"
    ]
@@ -126,73 +193,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 421,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 139456816/139456816 [01:28<00:00, 1569462.31it/s]\n"
+      "100%|██████████| 432022/432022 [00:01<00:00, 255044.26it/s]\n",
+      "100%|██████████| 432022/432022 [00:01<00:00, 348618.53it/s]\n"
      ]
     }
    ],
    "source": [
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
     "def tokenize(w):\n",
     "    if w in word_to_ix:\n",
     "        return word_to_ix[w]\n",
     "    else:\n",
     "        return 0\n",
     "\n",
-    "tokenized_corpus = []\n",
+    "tokenized_input_corpus = []\n",
+    "tokenized_target_corpus = []\n",
     "\n",
-    "for word in tqdm(corpus):\n",
-    "    tokenized_corpus.append(tokenize(word))"
+    "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": 422,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_input_corpus, tokenized_target_corpus = shuffle(tokenized_input_corpus, tokenized_target_corpus)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Create n-grams"
+    "## Create dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 423,
    "metadata": {},
    "outputs": [],
    "source": [
-    "tokenized_training_corpus = []\n",
-    "ngrams = list(nltk.ngrams(tokenized_corpus, n=7))\n",
-    "np.random.shuffle(ngrams)\n",
-    "ngrams = ngrams[:100_000]\n",
-    "ngrams_tensor = torch.tensor(ngrams, dtype=torch.long, device=device)\n",
+    "indices = np.nonzero(np.array(tokenized_target_corpus).flatten())\n",
     "\n",
-    "indices = torch.any(ngrams_tensor == 0, dim=1)\n",
-    "ngrams_tensor = ngrams_tensor[~indices]"
+    "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": 6,
+   "execution_count": 424,
    "metadata": {},
    "outputs": [],
    "source": [
-    "target_tensor = ngrams_tensor[:, 3].reshape(-1, 1).to(device)\n",
-    "input_tensor = torch.cat((ngrams_tensor[:, :3], ngrams_tensor[:, 4:]), dim=1).to(device)"
+    "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": 7,
+   "execution_count": 425,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([389892, 2])\n",
+      "torch.Size([389892, 1])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(input_corpus_tensor.size())\n",
+    "print(target_corpus_tensor.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 426,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['end', 'the']\n",
+      "['of']\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([word_to_ix.inverse[int(idx)] if int(idx) > 0 else '<UNK>' for idx in random_input_element])\n",
+    "print([word_to_ix.inverse[int(idx)] if int(idx) > 0 else '<UNK>' for idx in random_target_element])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 427,
    "metadata": {},
    "outputs": [],
    "source": [
-    "batched_input_tensor = torch.split(input_tensor, 512)\n",
-    "batched_target_tensor = torch.split(target_tensor, 512)"
+    "dataset = TensorDataset(input_corpus_tensor[:10_000], target_corpus_tensor[:10_000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 428,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)"
    ]
   },
   {
@@ -204,46 +333,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 429,
    "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, 50)\n",
-    "        self.linear1 = nn.Linear(50 * 6, output_size)\n",
-    "    \n",
+    "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
+    "        self.linear1 = nn.Linear(embedding_dim * (left_tokens + right_tokens), hidden_dim)\n",
+    "        self.linear2 = nn.Linear(hidden_dim, output_size)\n",
+    "        \n",
     "    def forward(self, inputs):\n",
     "        out = self.embedding(inputs)\n",
     "        out = out.view(inputs.size(0), -1)\n",
-    "        out = self.linear1(out)\n",
-    "        out = torch.softmax(out, dim=1)\n",
+    "        out = torch.softmax(self.linear1(out), dim=1)\n",
+    "        out = self.linear2(out)\n",
     "        return out"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define training parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 512\n",
-    "vocab_size = len(word_to_ix) + 1\n",
-    "embedding_dim = 10\n",
-    "hidden_dim = 64\n",
-    "output_size = vocab_size\n",
-    "learning_rate = 0.005\n",
-    "epochs = 1"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -253,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 430,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,28 +379,287 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 431,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|          | 0/164 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 164/164 [29:56<00:00, 10.95s/it]"
+      "100%|██████████| 157/157 [00:32<00:00,  4.81it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1, Loss: 14.220980655856248\n"
+      "Epoch 1, Loss: 10.999195001687214\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:32<00:00,  4.86it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2, Loss: 10.997720451112006\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:32<00:00,  4.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3, Loss: 10.99624701214444\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:30<00:00,  5.17it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4, Loss: 10.994744385883306\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:30<00:00,  5.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5, Loss: 10.993266263585182\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:30<00:00,  5.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6, Loss: 10.991843545512788\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:31<00:00,  4.92it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7, Loss: 10.990350304135852\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:28<00:00,  5.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8, Loss: 10.988877800619527\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:32<00:00,  4.81it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9, Loss: 10.987337306806236\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:29<00:00,  5.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10, Loss: 10.985873113012618\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:30<00:00,  5.13it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11, Loss: 10.98438450637137\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:28<00:00,  5.45it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12, Loss: 10.9829175548189\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:30<00:00,  5.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13, Loss: 10.981461263765954\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:30<00:00,  5.08it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14, Loss: 10.97996347269435\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:30<00:00,  5.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15, Loss: 10.978485234983408\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:31<00:00,  4.98it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16, Loss: 10.977057912547117\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:30<00:00,  5.23it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17, Loss: 10.97553843601494\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:29<00:00,  5.34it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18, Loss: 10.974108489455691\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:32<00:00,  4.82it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19, Loss: 10.972679308265638\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [00:30<00:00,  5.23it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20, Loss: 10.971182902147815\n"
      ]
     },
     {
@@ -306,11 +673,11 @@
    "source": [
     "model.to(device)\n",
     "\n",
-    "batches = list(zip(batched_input_tensor, batched_target_tensor))\n",
-    "\n",
     "for epoch in range(epochs):\n",
     "    total_loss = 0\n",
-    "    for batch_inputs, batch_targets in tqdm(batches):\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",
@@ -320,7 +687,7 @@
     "        loss.backward()\n",
     "        optimizer.step()\n",
     "\n",
-    "    print(f\"Epoch {epoch+1}, Loss: {total_loss/len(batches)}\")"
+    "    print(f\"Epoch {epoch+1}, Loss: {total_loss/len(dataloader)}\")"
    ]
   },
   {
@@ -332,7 +699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 432,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 433,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -363,36 +730,27 @@
     "        output = model(test_context_idxs)\n",
     "        top_predicted_scores, top_predicted_indices = torch.topk(output, 5)\n",
     "        predictions = list(zip(top_predicted_scores[0], top_predicted_indices[0]))\n",
-    "        predictions = [(round(float(score), 2), idx_to_word(idx)) for score, idx in predictions]\n",
+    "        predictions = [(float(score), idx_to_word(idx)) for score, idx in predictions]\n",
     "        total_score = np.sum([score for score, _ in predictions])\n",
-    "        predictions = ' '.join([f\"{word}:{round(score/total_score, 2)}\" for score, word in predictions]) + ' :0.01'\n",
+    "        predictions = ' '.join([f\"{word}:{score}\" for score, word in predictions]) + ' :' + str(1.0 - total_score)\n",
     "        return predictions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 434,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "AmiTlceaa.:nan Allentown.:nan thereuntoi:nan Jugo-Slav:nan Sallie,:nan :0.01\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Marcin\\AppData\\Local\\Temp\\ipykernel_36872\\3389363719.py:10: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  predictions = ' '.join([f\"{word}:{round(score/total_score, 2)}\" for score, word in predictions]) + ' :0.01'\n"
+      "the:0.210836723446846 of:0.13834647834300995 and:0.11819174885749817 to:0.09819918870925903 a:0.0662047415971756 :0.36822111904621124\n"
      ]
     }
    ],
    "source": [
-    "test_context = [\"came\", \"fiom\", \"the\", \"place\", \"to\", \"this\"]\n",
-    "print(predict(test_context[:3], test_context[3:]))"
+    "print(predict([\"came\", \"fiom\"], []))"
    ]
   },
   {
@@ -404,7 +762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 435,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -417,15 +775,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 436,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10519/10519 [02:25<00:00, 72.19it/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())[-3:]\n",
-    "    right_context = re.split(r\"\\s+\", row['RightContext'].strip())[:3]\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",