diff --git "a/MidTerm.ipynb" "b/MidTerm.ipynb"
deleted file mode 100644--- "a/MidTerm.ipynb"
+++ /dev/null
@@ -1,652 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 162
-        },
-        "id": "GqzNORgwcGBQ",
-        "outputId": "6eada4e8-c560-454f-bdb9-6197db092c91"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "     <input type=\"file\" id=\"files-fa5a7bad-862c-4fcb-95e3-d41d72fc140e\" name=\"files[]\" multiple disabled\n",
-              "        style=\"border:none\" />\n",
-              "     <output id=\"result-fa5a7bad-862c-4fcb-95e3-d41d72fc140e\">\n",
-              "      Upload widget is only available when the cell has been executed in the\n",
-              "      current browser session. Please rerun this cell to enable.\n",
-              "      </output>\n",
-              "      <script>// Copyright 2017 Google LLC\n",
-              "//\n",
-              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-              "// you may not use this file except in compliance with the License.\n",
-              "// You may obtain a copy of the License at\n",
-              "//\n",
-              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
-              "//\n",
-              "// Unless required by applicable law or agreed to in writing, software\n",
-              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-              "// See the License for the specific language governing permissions and\n",
-              "// limitations under the License.\n",
-              "\n",
-              "/**\n",
-              " * @fileoverview Helpers for google.colab Python module.\n",
-              " */\n",
-              "(function(scope) {\n",
-              "function span(text, styleAttributes = {}) {\n",
-              "  const element = document.createElement('span');\n",
-              "  element.textContent = text;\n",
-              "  for (const key of Object.keys(styleAttributes)) {\n",
-              "    element.style[key] = styleAttributes[key];\n",
-              "  }\n",
-              "  return element;\n",
-              "}\n",
-              "\n",
-              "// Max number of bytes which will be uploaded at a time.\n",
-              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
-              "\n",
-              "function _uploadFiles(inputId, outputId) {\n",
-              "  const steps = uploadFilesStep(inputId, outputId);\n",
-              "  const outputElement = document.getElementById(outputId);\n",
-              "  // Cache steps on the outputElement to make it available for the next call\n",
-              "  // to uploadFilesContinue from Python.\n",
-              "  outputElement.steps = steps;\n",
-              "\n",
-              "  return _uploadFilesContinue(outputId);\n",
-              "}\n",
-              "\n",
-              "// This is roughly an async generator (not supported in the browser yet),\n",
-              "// where there are multiple asynchronous steps and the Python side is going\n",
-              "// to poll for completion of each step.\n",
-              "// This uses a Promise to block the python side on completion of each step,\n",
-              "// then passes the result of the previous step as the input to the next step.\n",
-              "function _uploadFilesContinue(outputId) {\n",
-              "  const outputElement = document.getElementById(outputId);\n",
-              "  const steps = outputElement.steps;\n",
-              "\n",
-              "  const next = steps.next(outputElement.lastPromiseValue);\n",
-              "  return Promise.resolve(next.value.promise).then((value) => {\n",
-              "    // Cache the last promise value to make it available to the next\n",
-              "    // step of the generator.\n",
-              "    outputElement.lastPromiseValue = value;\n",
-              "    return next.value.response;\n",
-              "  });\n",
-              "}\n",
-              "\n",
-              "/**\n",
-              " * Generator function which is called between each async step of the upload\n",
-              " * process.\n",
-              " * @param {string} inputId Element ID of the input file picker element.\n",
-              " * @param {string} outputId Element ID of the output display.\n",
-              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
-              " */\n",
-              "function* uploadFilesStep(inputId, outputId) {\n",
-              "  const inputElement = document.getElementById(inputId);\n",
-              "  inputElement.disabled = false;\n",
-              "\n",
-              "  const outputElement = document.getElementById(outputId);\n",
-              "  outputElement.innerHTML = '';\n",
-              "\n",
-              "  const pickedPromise = new Promise((resolve) => {\n",
-              "    inputElement.addEventListener('change', (e) => {\n",
-              "      resolve(e.target.files);\n",
-              "    });\n",
-              "  });\n",
-              "\n",
-              "  const cancel = document.createElement('button');\n",
-              "  inputElement.parentElement.appendChild(cancel);\n",
-              "  cancel.textContent = 'Cancel upload';\n",
-              "  const cancelPromise = new Promise((resolve) => {\n",
-              "    cancel.onclick = () => {\n",
-              "      resolve(null);\n",
-              "    };\n",
-              "  });\n",
-              "\n",
-              "  // Wait for the user to pick the files.\n",
-              "  const files = yield {\n",
-              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
-              "    response: {\n",
-              "      action: 'starting',\n",
-              "    }\n",
-              "  };\n",
-              "\n",
-              "  cancel.remove();\n",
-              "\n",
-              "  // Disable the input element since further picks are not allowed.\n",
-              "  inputElement.disabled = true;\n",
-              "\n",
-              "  if (!files) {\n",
-              "    return {\n",
-              "      response: {\n",
-              "        action: 'complete',\n",
-              "      }\n",
-              "    };\n",
-              "  }\n",
-              "\n",
-              "  for (const file of files) {\n",
-              "    const li = document.createElement('li');\n",
-              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
-              "    li.append(span(\n",
-              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
-              "        `last modified: ${\n",
-              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
-              "                                    'n/a'} - `));\n",
-              "    const percent = span('0% done');\n",
-              "    li.appendChild(percent);\n",
-              "\n",
-              "    outputElement.appendChild(li);\n",
-              "\n",
-              "    const fileDataPromise = new Promise((resolve) => {\n",
-              "      const reader = new FileReader();\n",
-              "      reader.onload = (e) => {\n",
-              "        resolve(e.target.result);\n",
-              "      };\n",
-              "      reader.readAsArrayBuffer(file);\n",
-              "    });\n",
-              "    // Wait for the data to be ready.\n",
-              "    let fileData = yield {\n",
-              "      promise: fileDataPromise,\n",
-              "      response: {\n",
-              "        action: 'continue',\n",
-              "      }\n",
-              "    };\n",
-              "\n",
-              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
-              "    let position = 0;\n",
-              "    do {\n",
-              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
-              "      const chunk = new Uint8Array(fileData, position, length);\n",
-              "      position += length;\n",
-              "\n",
-              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
-              "      yield {\n",
-              "        response: {\n",
-              "          action: 'append',\n",
-              "          file: file.name,\n",
-              "          data: base64,\n",
-              "        },\n",
-              "      };\n",
-              "\n",
-              "      let percentDone = fileData.byteLength === 0 ?\n",
-              "          100 :\n",
-              "          Math.round((position / fileData.byteLength) * 100);\n",
-              "      percent.textContent = `${percentDone}% done`;\n",
-              "\n",
-              "    } while (position < fileData.byteLength);\n",
-              "  }\n",
-              "\n",
-              "  // All done.\n",
-              "  yield {\n",
-              "    response: {\n",
-              "      action: 'complete',\n",
-              "    }\n",
-              "  };\n",
-              "}\n",
-              "\n",
-              "scope.google = scope.google || {};\n",
-              "scope.google.colab = scope.google.colab || {};\n",
-              "scope.google.colab._files = {\n",
-              "  _uploadFiles,\n",
-              "  _uploadFilesContinue,\n",
-              "};\n",
-              "})(self);\n",
-              "</script> "
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Saving cleaned_english_data.txt to cleaned_english_data.txt\n",
-            "Saving cleaned_hebrew_data.txt to cleaned_hebrew_data.txt\n",
-            "English Text Data: ['Instruction: Give three tips for staying healthy.', 'Input: ', 'Output: 1. Eat a balanced and nutritious diet: Make sure your meals are inclusive of a variety of fruits and vegetables, lean protein, whole grains, and healthy fats. This helps to provide your body with the essential nutrients to function at its best and can help prevent chronic diseases.', '', '2. Engage in regular physical activity: Exercise is crucial for maintaining strong bones, muscles, and cardiovascular health. Aim for at least 150 minutes of moderate aerobic exercise or 75 minutes of vigorous exercise each week.']\n",
-            "Assigned Language Text Data: ['Instruction: I request you to convert the given sentence into Hebrew.', 'Input: Give three tips for staying healthy.', 'Output: תן שלושה טיפים לשמירה על הבריאות.', '', '--------------------------------------------------']\n"
-          ]
-        }
-      ],
-      "source": [
-        "\n",
-        "from google.colab import files\n",
-        "def load_dataset(file_path):\n",
-        "    with open(file_path, 'r', encoding='utf-8') as file:\n",
-        "        return file.read().splitlines()\n",
-        "\n",
-        "uploaded = files.upload()\n",
-        "\n",
-        "if 'cleaned_english_data.txt' in uploaded and 'cleaned_hebrew_data.txt' in uploaded:\n",
-        "    english_text = load_dataset('cleaned_english_data.txt')\n",
-        "    assigned_lang_text = load_dataset('cleaned_hebrew_data.txt')\n",
-        "\n",
-        "    print(f\"English Text Data: {english_text[:5]}\")  # Show the first 5 lines\n",
-        "    print(f\"Assigned Language Text Data: {assigned_lang_text[:5]}\")\n",
-        "else:\n",
-        "    print(\"Error: Required files not uploaded.\")\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import tensorflow as tf\n",
-        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
-        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import Embedding, LSTM, Dense\n",
-        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "def load_dataset(file_path):\n",
-        "    with open(file_path, 'r', encoding='utf-8') as file:\n",
-        "        return file.read().splitlines()\n",
-        "\n",
-        "english_text = load_dataset('/content/cleaned_english_data.txt')\n",
-        "assigned_lang_text = load_dataset('/content/cleaned_hebrew_data.txt')\n",
-        "\n",
-        "corpus = english_text + assigned_lang_text  #datasets combining\n",
-        "\n",
-        "# Tokenization\n",
-        "tokenizer = Tokenizer()\n",
-        "tokenizer.fit_on_texts(corpus)\n",
-        "total_words = len(tokenizer.word_index) + 1\n",
-        "\n",
-        "# Converting sentences into sequences of tokens\n",
-        "input_sequences = []\n",
-        "for line in corpus:\n",
-        "    token_list = tokenizer.texts_to_sequences([line])[0]\n",
-        "    for i in range(1, len(token_list)):\n",
-        "        n_gram_sequence = token_list[:i+1]\n",
-        "        input_sequences.append(n_gram_sequence)\n",
-        "\n",
-        "max_sequence_len = max([len(x) for x in input_sequences])\n",
-        "input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre')\n",
-        "\n",
-        "X, y = input_sequences[:, :-1], input_sequences[:, -1]\n",
-        "y = tf.keras.utils.to_categorical(y, num_classes=total_words)\n",
-        "\n",
-        "print(f'Total words in corpus: {total_words}')\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HLhmmB1z7xE4",
-        "outputId": "2193d440-8923-421b-9111-a56b48f970a5"
-      },
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Total words in corpus: 5461\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Model Defining using LSTM\n",
-        "model = Sequential()\n",
-        "model.add(Embedding(input_dim=total_words, output_dim=100, input_length=max_sequence_len-1))\n",
-        "model.add(LSTM(128, return_sequences=False))\n",
-        "model.add(Dense(total_words, activation='softmax'))\n",
-        "model.build(input_shape=(None, max_sequence_len-1))\n",
-        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
-        "model.summary()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 225
-        },
-        "id": "lNugx3_B8Okl",
-        "outputId": "43948526-1cb6-4be1-fbb1-d29e14ebeccd"
-      },
-      "execution_count": 11,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
-              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
-              "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m288\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │         \u001b[38;5;34m546,100\u001b[0m │\n",
-              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-              "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │         \u001b[38;5;34m117,248\u001b[0m │\n",
-              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5461\u001b[0m)                │         \u001b[38;5;34m704,469\u001b[0m │\n",
-              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
-              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
-              "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">288</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">546,100</span> │\n",
-              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-              "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">117,248</span> │\n",
-              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
-              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5461</span>)                │         <span style=\"color: #00af00; text-decoration-color: #00af00\">704,469</span> │\n",
-              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,367,817\u001b[0m (5.22 MB)\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,367,817</span> (5.22 MB)\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,367,817\u001b[0m (5.22 MB)\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,367,817</span> (5.22 MB)\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
-        "\n",
-        "checkpoint = ModelCheckpoint('best_model.keras', monitor='val_loss', save_best_only=True, verbose=1) # Defining the checkpoint\n",
-        "\n",
-        "def calculate_perplexity(loss):  #calculating the perplexity\n",
-        "    return np.exp(loss)\n",
-        "\n",
-        "# Training the model\n",
-        "history = model.fit(X, y, epochs=20, batch_size=64, validation_split=0.2, callbacks=[checkpoint])\n",
-        "\n",
-        "# Calculating perplexity for both training and validation losses\n",
-        "training_perplexity = [calculate_perplexity(l) for l in history.history['loss']]\n",
-        "validation_perplexity = [calculate_perplexity(vl) for vl in history.history['val_loss']]\n",
-        "\n",
-        "# Adding perplexity values to the history dictionary\n",
-        "history.history['training_perplexity'] = training_perplexity\n",
-        "history.history['validation_perplexity'] = validation_perplexity\n",
-        "\n",
-        "# Saving the history (including perplexity) to a CSV file\n",
-        "history_df = pd.DataFrame(history.history)\n",
-        "history_df.to_csv('training_history_with_perplexity.csv', index=False)\n",
-        "\n",
-        "# Plotting the training loss and validation loss\n",
-        "plt.plot(history.history['loss'], label='Training Loss')\n",
-        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
-        "plt.legend()\n",
-        "plt.title('Training and Validation Loss')\n",
-        "plt.show()\n",
-        "\n",
-        "# Plotting the training perplexity and validation perplexity\n",
-        "plt.plot(history.history['training_perplexity'], label='Training Perplexity')\n",
-        "plt.plot(history.history['validation_perplexity'], label='Validation Perplexity')\n",
-        "plt.legend()\n",
-        "plt.title('Training and Validation Perplexity')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "Zr9Lbipu8SgV",
-        "outputId": "646eb1a2-7509-4f15-f321-c75407ce618e"
-      },
-      "execution_count": 12,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch 1/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669ms/step - accuracy: 0.0554 - loss: 7.7486\n",
-            "Epoch 1: val_loss improved from inf to 7.92880, saving model to best_model.keras\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 725ms/step - accuracy: 0.0554 - loss: 7.7476 - val_accuracy: 0.0482 - val_loss: 7.9288\n",
-            "Epoch 2/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 656ms/step - accuracy: 0.0614 - loss: 6.8756\n",
-            "Epoch 2: val_loss improved from 7.92880 to 7.88635, saving model to best_model.keras\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 711ms/step - accuracy: 0.0614 - loss: 6.8756 - val_accuracy: 0.0491 - val_loss: 7.8864\n",
-            "Epoch 3/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665ms/step - accuracy: 0.0732 - loss: 6.6309\n",
-            "Epoch 3: val_loss did not improve from 7.88635\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m264s\u001b[0m 721ms/step - accuracy: 0.0733 - loss: 6.6309 - val_accuracy: 0.0710 - val_loss: 7.8969\n",
-            "Epoch 4/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 655ms/step - accuracy: 0.0884 - loss: 6.4134\n",
-            "Epoch 4: val_loss did not improve from 7.88635\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m264s\u001b[0m 726ms/step - accuracy: 0.0884 - loss: 6.4134 - val_accuracy: 0.0766 - val_loss: 7.9415\n",
-            "Epoch 5/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 661ms/step - accuracy: 0.0998 - loss: 6.1432\n",
-            "Epoch 5: val_loss did not improve from 7.88635\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 716ms/step - accuracy: 0.0998 - loss: 6.1432 - val_accuracy: 0.0939 - val_loss: 8.0160\n",
-            "Epoch 6/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 653ms/step - accuracy: 0.1154 - loss: 5.8254\n",
-            "Epoch 6: val_loss did not improve from 7.88635\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 714ms/step - accuracy: 0.1154 - loss: 5.8255 - val_accuracy: 0.1077 - val_loss: 8.0629\n",
-            "Epoch 7/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664ms/step - accuracy: 0.1242 - loss: 5.5808\n",
-            "Epoch 7: val_loss did not improve from 7.88635\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m264s\u001b[0m 721ms/step - accuracy: 0.1242 - loss: 5.5809 - val_accuracy: 0.1090 - val_loss: 8.1210\n",
-            "Epoch 8/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 666ms/step - accuracy: 0.1374 - loss: 5.3624\n",
-            "Epoch 8: val_loss did not improve from 7.88635\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 721ms/step - accuracy: 0.1374 - loss: 5.3625 - val_accuracy: 0.1277 - val_loss: 7.9136\n",
-            "Epoch 9/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 661ms/step - accuracy: 0.1512 - loss: 5.1204\n",
-            "Epoch 9: val_loss improved from 7.88635 to 7.64110, saving model to best_model.keras\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 717ms/step - accuracy: 0.1512 - loss: 5.1205 - val_accuracy: 0.1333 - val_loss: 7.6411\n",
-            "Epoch 10/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 650ms/step - accuracy: 0.1700 - loss: 4.9033\n",
-            "Epoch 10: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 708ms/step - accuracy: 0.1700 - loss: 4.9034 - val_accuracy: 0.1404 - val_loss: 7.9004\n",
-            "Epoch 11/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 656ms/step - accuracy: 0.1843 - loss: 4.6931\n",
-            "Epoch 11: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m263s\u001b[0m 710ms/step - accuracy: 0.1843 - loss: 4.6932 - val_accuracy: 0.1484 - val_loss: 7.9742\n",
-            "Epoch 12/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665ms/step - accuracy: 0.2012 - loss: 4.4692\n",
-            "Epoch 12: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m265s\u001b[0m 720ms/step - accuracy: 0.2012 - loss: 4.4694 - val_accuracy: 0.1584 - val_loss: 8.1840\n",
-            "Epoch 13/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669ms/step - accuracy: 0.2214 - loss: 4.2359\n",
-            "Epoch 13: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m209s\u001b[0m 725ms/step - accuracy: 0.2214 - loss: 4.2361 - val_accuracy: 0.1679 - val_loss: 8.3711\n",
-            "Epoch 14/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664ms/step - accuracy: 0.2519 - loss: 4.0399\n",
-            "Epoch 14: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m266s\u001b[0m 736ms/step - accuracy: 0.2519 - loss: 4.0400 - val_accuracy: 0.1757 - val_loss: 8.4434\n",
-            "Epoch 15/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665ms/step - accuracy: 0.2770 - loss: 3.8515\n",
-            "Epoch 15: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m257s\u001b[0m 719ms/step - accuracy: 0.2769 - loss: 3.8516 - val_accuracy: 0.1841 - val_loss: 8.5408\n",
-            "Epoch 16/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 656ms/step - accuracy: 0.3077 - loss: 3.6306\n",
-            "Epoch 16: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 717ms/step - accuracy: 0.3077 - loss: 3.6308 - val_accuracy: 0.1887 - val_loss: 8.5005\n",
-            "Epoch 17/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658ms/step - accuracy: 0.3513 - loss: 3.4129\n",
-            "Epoch 17: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m211s\u001b[0m 729ms/step - accuracy: 0.3512 - loss: 3.4131 - val_accuracy: 0.1956 - val_loss: 8.5829\n",
-            "Epoch 18/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662ms/step - accuracy: 0.3825 - loss: 3.2154\n",
-            "Epoch 18: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m260s\u001b[0m 724ms/step - accuracy: 0.3824 - loss: 3.2156 - val_accuracy: 0.2049 - val_loss: 8.5252\n",
-            "Epoch 19/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 805ms/step - accuracy: 0.4251 - loss: 3.0267\n",
-            "Epoch 19: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m327s\u001b[0m 948ms/step - accuracy: 0.4250 - loss: 3.0269 - val_accuracy: 0.2224 - val_loss: 8.4326\n",
-            "Epoch 20/20\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665ms/step - accuracy: 0.4558 - loss: 2.8457\n",
-            "Epoch 20: val_loss did not improve from 7.64110\n",
-            "\u001b[1m289/289\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m256s\u001b[0m 720ms/step - accuracy: 0.4557 - loss: 2.8458 - val_accuracy: 0.2352 - val_loss: 8.5149\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(\"Final Training Perplexity: \", training_perplexity[-1])\n",
-        "print(\"Final Validation Perplexity: \", validation_perplexity[-1])"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "lE6jggPRUaR_",
-        "outputId": "07aa9372-b02e-406a-8b82-e59429f1ba15"
-      },
-      "execution_count": 17,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Final Training Perplexity:  18.076039528948318\n",
-            "Final Validation Perplexity:  4988.557729823132\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def generate_text(seed_text, next_words, max_sequence_len):\n",
-        "    for _ in range(next_words):\n",
-        "        token_list = tokenizer.texts_to_sequences([seed_text])[0]\n",
-        "        token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')\n",
-        "        predicted = model.predict(token_list, verbose=0)\n",
-        "        predicted_word_index = tf.argmax(predicted, axis=1).numpy()[0]\n",
-        "        predicted_word = tokenizer.index_word[predicted_word_index]\n",
-        "        seed_text += \" \" + predicted_word\n",
-        "    return seed_text\n",
-        "\n",
-        "# Generating text in English\n",
-        "print(generate_text(\"Once upon a time\", 10, max_sequence_len))\n",
-        "\n",
-        "# Generate text in Hebrew\n",
-        "print(generate_text(\"היה היה פעם\", 10, max_sequence_len))\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "dtgL5UtKQILW",
-        "outputId": "70374e44-a1bb-4ae7-d6c0-f0759b50ce88"
-      },
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Once upon a time is a camping trip technique used to make a difficult\n",
-            "היה היה פעם delivery cbm and regular injuries or epochs alternated and cardiovascular\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.save('final_model.keras')\n"
-      ],
-      "metadata": {
-        "id": "PyYVGwc6Qqs8"
-      },
-      "execution_count": 14,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file