diff --git a/RNN.ipynb b/RNN.ipynb
deleted file mode 100644
index 07afcbb..0000000
--- a/RNN.ipynb
+++ /dev/null
@@ -1,531 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "c80ac05e-c22e-4f7f-a48d-ca85173f0f86",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import classification_report\n",
-    "from tqdm.notebook import tqdm\n",
-    "import numpy as np\n",
-    "from collections import defaultdict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c8a9db2f-9e34-4246-8a71-5b7c504cfcb5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_path = 'en-ner-conll-2003-master/en-ner-conll-2003/train/train.tsv/train.tsv'\n",
-    "dev_in_path = 'en-ner-conll-2003-master/en-ner-conll-2003/dev-0/in.tsv'\n",
-    "test_in_path = 'en-ner-conll-2003-master/en-ner-conll-2003/test-A/in.tsv'\n",
-    "dev_out_path = 'en-ner-conll-2003-master/en-ner-conll-2003/dev-0/out.tsv'\n",
-    "test_out_path = 'en-ner-conll-2003-master/en-ner-conll-2003/test-A/out.tsv'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "06e936cf-3022-4d9e-a6d8-03c736bd7586",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "f1c3b706-0e38-4b3d-814e-79449ba696f6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def load_data(file_path):\n",
-    "    sentences, labels = [], []\n",
-    "    with open(file_path, 'r') as file:\n",
-    "        sentence, label = [], []\n",
-    "        for line in file:\n",
-    "            if line.startswith('-DOCSTART-') or line == \"\\n\":\n",
-    "                if sentence:\n",
-    "                    sentences.append(sentence)\n",
-    "                    labels.append(label)\n",
-    "                    sentence, label = [], []\n",
-    "            else:\n",
-    "                splits = line.split()\n",
-    "                sentence.append(splits[0])\n",
-    "                label.append(splits[-1])\n",
-    "        if sentence:\n",
-    "            sentences.append(sentence)\n",
-    "            labels.append(label)\n",
-    "    return sentences, labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e9aa2f41-8625-44d9-815e-f8d010d7ea6b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def build_vocab(sentences):\n",
-    "    vocab = defaultdict(lambda: len(vocab))\n",
-    "    vocab[\"<PAD>\"]\n",
-    "    for sentence in sentences:\n",
-    "        for word in sentence:\n",
-    "            vocab[word]\n",
-    "    return vocab"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "011e8daa-8bd0-4fba-8785-cf25b31c3343",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def encode_sentences(sentences, vocab):\n",
-    "    return [[vocab[word] for word in sentence] for sentence in sentences]\n",
-    "\n",
-    "def pad_sequences(sequences, max_len):\n",
-    "    padded_sequences = np.zeros((len(sequences), max_len), dtype=int)\n",
-    "    for i, seq in enumerate(sequences):\n",
-    "        padded_sequences[i, :len(seq)] = seq\n",
-    "    return padded_sequences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e1c4c06b-fb61-48aa-b6bd-53b8b6b0b7b1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_dataloader(inputs, labels, batch_size):\n",
-    "    inputs_tensor = torch.tensor(inputs, dtype=torch.long)\n",
-    "    labels_tensor = torch.tensor(labels, dtype=torch.long)\n",
-    "    dataset = TensorDataset(inputs_tensor, labels_tensor)\n",
-    "    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
-    "    return dataloader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c1012f38-c925-4b3a-97b9-ec2ff3a66bae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class NER_RNN(nn.Module):\n",
-    "    def __init__(self, vocab_size, tagset_size, embedding_dim=128, hidden_dim=256):\n",
-    "        super(NER_RNN, self).__init__()\n",
-    "        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)\n",
-    "        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)\n",
-    "        self.fc = nn.Linear(hidden_dim, tagset_size)\n",
-    "    \n",
-    "    def forward(self, x):\n",
-    "        x = self.embedding(x)\n",
-    "        x, _ = self.lstm(x)\n",
-    "        x = self.fc(x)\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f2f09d01-11c7-4cc0-91f7-af10c992be9a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def correct_iob_labels(predictions): \n",
-    "    corrected = []\n",
-    "    for pred in predictions:\n",
-    "        corrected_sentence = []\n",
-    "        prev_label = 'O'\n",
-    "        for label in pred:\n",
-    "            if label.startswith('I-') and (prev_label == 'O' or prev_label[2:] != label[2:]):             \n",
-    "                corrected_sentence.append('B-' + label[2:])\n",
-    "            else:\n",
-    "                corrected_sentence.append(label)\n",
-    "            prev_label = corrected_sentence[-1]\n",
-    "        corrected.append(corrected_sentence)\n",
-    "    return corrected"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "50971cde-d176-4fce-af14-1a7530c1c453",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "train_sentences, train_labels = load_data(train_path)\n",
-    "\n",
-    "all_labels = [label for sentence_labels in train_labels for label in sentence_labels]\n",
-    "unique_labels = list(set(all_labels))\n",
-    "label_mapping = {label: i for i, label in enumerate(unique_labels)}\n",
-    "\n",
-    "inverse_label_mapping = {i: label for label, i in label_mapping.items()}\n",
-    "\n",
-    "def predict_and_save(model, input_tokens_ids, output_file):\n",
-    "    model.eval() \n",
-    "    predictions = []\n",
-    "    for tokens in tqdm(input_tokens_ids):\n",
-    "        tokens = tokens.unsqueeze(0)\n",
-    "        with torch.no_grad():\n",
-    "            predicted_tags = model(tokens)\n",
-    "            predicted_tags = torch.argmax(predicted_tags.squeeze(0), 1).tolist() \n",
-    "        predicted_labels = [inverse_label_mapping[tag] for tag in predicted_tags]\n",
-    "        predictions.append(predicted_labels[1:-1]) \n",
-    "\n",
-    "    with open(output_file, 'w') as f:\n",
-    "        for sentence in correct_iob_labels(predictions):\n",
-    "            f.write(\" \".join(sentence) + \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2542fb92-9c15-4abe-8b4d-d002df57fe99",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_sentences, train_labels = load_data(train_path)\n",
-    "dev_sentences, dev_labels = load_data(dev_in_path)\n",
-    "test_sentences, test_labels = load_data(test_in_path)\n",
-    "word_vocab = build_vocab(train_sentences)\n",
-    "tag_vocab = build_vocab(train_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "ff64590b-08cd-42ac-8e32-363f16038c0b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_inputs = encode_sentences(train_sentences, word_vocab)\n",
-    "dev_inputs = encode_sentences(dev_sentences, word_vocab)\n",
-    "test_inputs = encode_sentences(test_sentences, word_vocab)\n",
-    "\n",
-    "train_labels_encoded = encode_sentences(train_labels, tag_vocab)\n",
-    "dev_labels_encoded = encode_sentences(dev_labels, tag_vocab)\n",
-    "test_labels_encoded = encode_sentences(test_labels, tag_vocab)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "4c591b46-6cda-4f72-9574-e284faa58312",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_len = max(max(len(seq) for seq in train_inputs), max(len(seq) for seq in dev_inputs), max(len(seq) for seq in test_inputs))\n",
-    "\n",
-    "train_inputs_padded = pad_sequences(train_inputs, max_len)\n",
-    "dev_inputs_padded = pad_sequences(dev_inputs, max_len)\n",
-    "test_inputs_padded = pad_sequences(test_inputs, max_len)\n",
-    "\n",
-    "train_labels_padded = pad_sequences(train_labels_encoded, max_len)\n",
-    "dev_labels_padded = pad_sequences(dev_labels_encoded, max_len)\n",
-    "test_labels_padded = pad_sequences(test_labels_encoded, max_len)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "228cf2c6-6fa0-47fd-97cc-3a0495b5059e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 32\n",
-    "\n",
-    "train_dataloader = create_dataloader(train_inputs_padded, train_labels_padded, batch_size)\n",
-    "dev_dataloader = create_dataloader(dev_inputs_padded, dev_labels_padded, batch_size)\n",
-    "test_dataloader = create_dataloader(test_inputs_padded, test_labels_padded, batch_size)\n",
-    "\n",
-    "vocab_size = len(word_vocab)\n",
-    "tagset_size = len(tag_vocab)\n",
-    "\n",
-    "model = NER_RNN(vocab_size, tagset_size).to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "eafe2cf8-547d-4245-b32a-de24792f33ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "criterion = nn.CrossEntropyLoss(ignore_index=0)\n",
-    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "a178c476-b566-42ec-b2ca-fb59ed85f6ee",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f7c956b746a454d82306b90bb3d3abf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5, Loss: 0.6613934636116028\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f9c3700d6d6343bc882e082504745ec3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 2/5, Loss: 0.4284511208534241\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "539a905707ef48b3a33406616d163695",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 3/5, Loss: 0.26125839352607727\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0d9f7098cd9348d8964a083f91b5684b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 4/5, Loss: 0.14203576743602753\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eb7df70c5e1e441e9afb8032301a3808",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5/5, Loss: 0.06412970274686813\n"
-     ]
-    }
-   ],
-   "source": [
-    "num_epochs = 5\n",
-    "\n",
-    "for epoch in range(num_epochs):\n",
-    "    model.train()\n",
-    "    total_loss = 0\n",
-    "    for inputs, labels in tqdm(train_dataloader):\n",
-    "        inputs, labels = inputs.to(device), labels.to(device)\n",
-    "        optimizer.zero_grad()\n",
-    "        outputs = model(inputs)\n",
-    "        outputs = outputs.view(-1, tagset_size)\n",
-    "        labels = labels.view(-1)\n",
-    "        \n",
-    "        loss = criterion(outputs, labels)\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "        \n",
-    "        total_loss += loss.item()\n",
-    "    print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {total_loss/len(train_dataloader)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "bdd4c839-9df1-4f85-a1f4-2f5ca6436f89",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate(dataloader):\n",
-    "    model.eval()\n",
-    "    all_preds = []\n",
-    "    all_labels = []\n",
-    "    with torch.no_grad():\n",
-    "        for inputs, labels in dataloader:\n",
-    "            inputs, labels = inputs.to(device), labels.to(device)\n",
-    "            outputs = model(inputs)\n",
-    "            _, preds = torch.max(outputs, dim=2)\n",
-    "            \n",
-    "            all_preds.extend(preds.cpu().numpy())\n",
-    "            all_labels.extend(labels.cpu().numpy())\n",
-    "    \n",
-    "    return all_preds, all_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "be8eee4f-4f28-433c-9e23-a3730ac11780",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dev_preds, dev_labels_true = evaluate(dev_dataloader)\n",
-    "test_preds, test_labels_true = evaluate(test_dataloader)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "961dcea7-d873-4aac-82c6-435953c449c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calculate_accuracy(predictions, labels):\n",
-    "    correct = 0\n",
-    "    total = 0\n",
-    "    for preds, true_labels in zip(predictions, labels):\n",
-    "        for pred, true_label in zip(preds, true_labels):\n",
-    "            if pred == true_label:\n",
-    "                correct += 1\n",
-    "            total += 1\n",
-    "    accuracy = correct / total\n",
-    "    return accuracy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "dc8791ef-ddfa-45d3-8b81-9e1eec38ad1a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy on Dev Set: 0.14708994708994708\n",
-      "Accuracy on Test Set: 0.17566137566137566\n"
-     ]
-    }
-   ],
-   "source": [
-    "dev_accuracy = calculate_accuracy(dev_preds, dev_labels_true)\n",
-    "test_accuracy = calculate_accuracy(test_preds, test_labels_true)\n",
-    "\n",
-    "print(\"Accuracy on Dev Set:\", dev_accuracy)\n",
-    "print(\"Accuracy on Test Set:\", test_accuracy)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "4965d2be-eb8e-437d-b53b-3a038ea77e91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def save_predictions(predictions, file_path):\n",
-    "    with open(file_path, 'w') as file:\n",
-    "        for preds in predictions:\n",
-    "            for pred in preds:\n",
-    "                file.write(f\"{pred}\\n\")\n",
-    "            file.write(\"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "9fefb237-ee84-430a-9e72-9f606c2bc7d3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "save_predictions(dev_preds, dev_out_path)\n",
-    "save_predictions(test_preds, test_out_path)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}