en_tr_translate.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJVwSEy5LIT4",
        "outputId": "5202ab20-11ea-4b03-ca5e-c2fb630d5969"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive/')\n",
        "\n",
        "path = './drive/MyDrive/Data/'\n",
        "\n",
        "data_path = path + 'tur.txt'\n",
        "glove_path = path + 'glove.6B.100d.txt'\n",
        "tr_vec_path = path + 'cc.tr.300.vec/cc.tr.300.vec'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oFX5a7hO8pj",
        "outputId": "f41fb127-1acd-4fe1-8914-d3156822b6f8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting translate\n",
            "  Downloading translate-3.6.1-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from translate) (8.1.7)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from translate) (4.9.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from translate) (2.31.0)\n",
            "Collecting libretranslatepy==2.1.1 (from translate)\n",
            "  Downloading libretranslatepy-2.1.1-py3-none-any.whl (3.2 kB)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (2023.7.22)\n",
            "Installing collected packages: libretranslatepy, translate\n",
            "Successfully installed libretranslatepy-2.1.1 translate-3.6.1\n"
          ]
        }
      ],
      "source": [
        "!pip install translate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IjebRr5cN3Sr",
        "outputId": "2be6780a-9370-4e21-a9ce-10be402d5dad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting rouge-score\n",
            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from rouge-score) (1.4.0)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from rouge-score) (3.8.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rouge-score) (1.23.5)\n",
            "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from rouge-score) (1.16.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score) (1.3.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score) (2023.6.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score) (4.66.1)\n",
            "Building wheels for collected packages: rouge-score\n",
            "  Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24932 sha256=ad6558078113acc649ba4daf736fd2f993ceaaa5e4c93a450c6b89904686e8c7\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "Successfully built rouge-score\n",
            "Installing collected packages: rouge-score\n",
            "Successfully installed rouge-score-0.1.2\n"
          ]
        }
      ],
      "source": [
        "!pip install rouge-score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0AP1ZHVoN-6r"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "import io\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import GRU\n",
        "from tensorflow.keras.layers import Embedding\n",
        "\n",
        "from tensorflow import nn\n",
        "from tensorflow import reduce_mean\n",
        "from tensorflow import int32\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "\n",
        "from translate import Translator\n",
        "from rouge_score import rouge_scorer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "I84DbwmqO3rr"
      },
      "outputs": [],
      "source": [
        "contractions = {\n",
        "    r\"'m\": \" am\",\n",
        "    r\"isn't\": \"isnot\",\n",
        "    r\"Isn't\": \"Isnot\",\n",
        "    r\"'re\": \" are\",\n",
        "    r\"aren't\": \"arenot\",\n",
        "    r\"Aren't\": \"Arenot\",\n",
        "    r\"'ve\": \" have\",\n",
        "    r\"haven't\": \"havenot\",\n",
        "    r\"hasn't\": \"hasnot\",\n",
        "    r\"Haven't\": \"Havenot\",\n",
        "    r\"Hasn't\": \"Hasnot\",\n",
        "    r\"don't\": \"donot\",\n",
        "    r\"doesn't\": \"doesnot\",\n",
        "    r\"didn't\": \"didnot\",\n",
        "    r\"Don't\": \"Donot\",\n",
        "    r\"Doesn't\": \"Doesnot\",\n",
        "    r\"Didn't\": \"Didnot\",\n",
        "    r\"can't\": \"cannot\",\n",
        "    r\"Can't\": \"Cannot\",\n",
        "    r\"won't\": \"willnot\",\n",
        "    r\"Won't\": \"Willnot\",\n",
        "    r\"'ll\": \" will\",\n",
        "    r\"shouldn't\": \"shouldnot\",\n",
        "    r\"Shouldn't\": \"Should not\",\n",
        "    r\"wouldn't\": \"wouldnot\",\n",
        "    r\"Wouldn't\": \"Wouldnot\",\n",
        "}\n",
        "\n",
        "def remove_contractions(text):\n",
        "  for pattern, replacement in contractions.items():\n",
        "    text = re.sub(pattern, replacement, text, flags=re.IGNORECASE)\n",
        "  return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "wnVxLH6BCfgU"
      },
      "outputs": [],
      "source": [
        "starter_mark = 'startstart '\n",
        "end_mark = ' endend'\n",
        "\n",
        "tr_data = []\n",
        "en_data = []\n",
        "\n",
        "with open(data_path, 'r', encoding='utf-8') as f:\n",
        "  lines = f.readlines()\n",
        "\n",
        "for line in lines:\n",
        "  line, _ = line.strip().split('\\t')\n",
        "  line = remove_contractions(line)\n",
        "  line = re.sub(r'[^a-zA-Z]', ' ', line)\n",
        "  line = ' '.join(word.lower() for word in line.split())\n",
        "  en_data.append(line)\n",
        "\n",
        "for line in lines:\n",
        "  _, line = line.strip().split('\\t')\n",
        "  line = re.compile(r'[^a-zA-ZğüşıöçĞÜŞİÖÇ]').sub(' ', line)\n",
        "  line = ' '.join(word.lower() for word in line.split())\n",
        "  line = starter_mark + line + end_mark\n",
        "  tr_data.append(line)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "wVrNxSW5EZ7t"
      },
      "outputs": [],
      "source": [
        "def create_tokenizer(texts, padding, reverse=False):\n",
        "    tokenizer = Tokenizer(num_words=None)\n",
        "    tokenizer.fit_on_texts(texts)\n",
        "\n",
        "    index_to_word = dict(zip(tokenizer.word_index.values(), tokenizer.word_index.keys()))\n",
        "\n",
        "    tokens = tokenizer.texts_to_sequences(texts)\n",
        "\n",
        "    if reverse:\n",
        "        tokens = [list(reversed(x)) for x in tokens]\n",
        "        truncating = 'pre'\n",
        "    else:\n",
        "        truncating = 'post'\n",
        "\n",
        "    num_tokens = [len(x) for x in tokens]\n",
        "    max_tokens = np.mean(num_tokens) + 2 * np.std(num_tokens)\n",
        "    max_tokens = int(max_tokens)\n",
        "\n",
        "    tokens_padded = pad_sequences(\n",
        "        tokens,\n",
        "        maxlen=max_tokens,\n",
        "        padding=padding,\n",
        "        truncating=truncating\n",
        "      )\n",
        "\n",
        "    return tokenizer, index_to_word, tokens_padded, max_tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "0LEA0E0dE7nL"
      },
      "outputs": [],
      "source": [
        "def token_to_word(token, index_to_word):\n",
        "    word = ' ' if token == 0 else index_to_word[token]\n",
        "    return word"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "XQFNBFsJKRTc"
      },
      "outputs": [],
      "source": [
        "def tokens_to_string(tokens, index_to_word):\n",
        "    words = [index_to_word[token] for token in tokens if token != 0]\n",
        "    text = ' '.join(words)\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "o6-XemUJKr5P"
      },
      "outputs": [],
      "source": [
        "def text_to_tokens(text, tokenizer, padding, reverse=False, max_tokens=None):\n",
        "    tokens = tokenizer.texts_to_sequences([text])\n",
        "    tokens = np.array(tokens)\n",
        "\n",
        "    if reverse:\n",
        "        tokens = np.flip(tokens, axis=1)\n",
        "        truncating = 'pre'\n",
        "    else:\n",
        "        truncating = 'post'\n",
        "\n",
        "    if max_tokens is not None:\n",
        "        tokens = pad_sequences(\n",
        "            tokens,\n",
        "            maxlen=max_tokens,\n",
        "            padding=padding,\n",
        "            truncating=truncating\n",
        "          )\n",
        "\n",
        "    return tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "gez4AelyLdJm"
      },
      "outputs": [],
      "source": [
        "tokenizer_en, index_to_word_en, tokens_padded_en, max_tokens_en = create_tokenizer(\n",
        "    texts=en_data,\n",
        "    padding='pre',\n",
        "    reverse=True,\n",
        ")\n",
        "\n",
        "tokenizer_tr, index_to_word_tr, tokens_padded_tr, max_tokens_tr = create_tokenizer(\n",
        "    texts=tr_data,\n",
        "    padding='post',\n",
        "    reverse=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "3gFEmjMgMTEs"
      },
      "outputs": [],
      "source": [
        "token_start = tokenizer_tr.word_index[starter_mark.strip()]\n",
        "token_end = tokenizer_tr.word_index[end_mark.strip()]\n",
        "\n",
        "encoder_input_data = tokens_padded_en\n",
        "\n",
        "decoder_input_data = tokens_padded_tr[:, :-1]\n",
        "decoder_output_data = tokens_padded_tr[:, 1:]\n",
        "\n",
        "num_encoder_words = len(tokenizer_en.word_index)\n",
        "num_decoder_words = len(tokenizer_tr.word_index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "iF-ggGn6MEHJ"
      },
      "outputs": [],
      "source": [
        "fasttext_tr_vec = {}\n",
        "\n",
        "fin = io.open(tr_vec_path, 'r', encoding='utf-8').readlines()\n",
        "\n",
        "for line in fin:\n",
        "  values = line.split()\n",
        "  word = values[0]\n",
        "  vec = np.asarray(values[1:], dtype='float32')\n",
        "  fasttext_tr_vec[word] = vec\n",
        "\n",
        "fasttext_embedding_matrix = np.random.uniform(-1, 1, (num_decoder_words, 300))\n",
        "\n",
        "for word, index in tokenizer_tr.word_index.items():\n",
        "  if index < num_decoder_words:\n",
        "    ft_embedding_vector = fasttext_tr_vec.get(word)\n",
        "    if ft_embedding_vector is not None:\n",
        "      fasttext_embedding_matrix[index] = ft_embedding_vector\n",
        "\n",
        "word2vec = {}\n",
        "\n",
        "with open(glove_path, encoding='utf-8') as f:\n",
        "  for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    vec = np.asarray(values[1:], dtype='float32')\n",
        "    word2vec[word] = vec\n",
        "\n",
        "glove_embedding_matrix = np.random.uniform(-1, 1, (num_encoder_words, 100))\n",
        "\n",
        "for word, index in tokenizer_en.word_index.items():\n",
        "  if index < num_encoder_words:\n",
        "    embedding_vector = word2vec.get(word)\n",
        "    if embedding_vector is not None:\n",
        "      glove_embedding_matrix[index] = embedding_vector"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "V8nxlRqzNhs0"
      },
      "outputs": [],
      "source": [
        "num_units = 512\n",
        "\n",
        "encoder_input = Input(shape=(None,), name='encoder_input')\n",
        "\n",
        "encoder_embedding = Embedding(\n",
        "    input_dim=num_encoder_words,\n",
        "    output_dim=100,\n",
        "    weights=[glove_embedding_matrix],\n",
        "    trainable=True,\n",
        "    name='encoder_embedding')(encoder_input)\n",
        "\n",
        "encoder_gru1 = GRU(num_units, return_sequences=True)(encoder_embedding)\n",
        "encoder_gru2 = GRU(num_units, return_sequences=True)(encoder_gru1)\n",
        "encoder_gru3 = GRU(num_units, return_sequences=True)(encoder_gru2)\n",
        "encoder_gru4 = GRU(num_units, return_sequences=True)(encoder_gru3)\n",
        "encoder_gru5 = GRU(num_units, return_sequences=True)(encoder_gru4)\n",
        "encoder_final_state = GRU(num_units, return_sequences=False)(encoder_gru5)\n",
        "\n",
        "encoder_output = encoder_final_state"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ePgHZtxAV7f0"
      },
      "outputs": [],
      "source": [
        "decoder_initial_state = Input(shape=(num_units,), name='decoder_initial_state')\n",
        "decoder_input = Input(shape=(None,), name='decoder_input')\n",
        "\n",
        "decoder_embedding = Embedding(\n",
        "    input_dim=num_decoder_words,\n",
        "    output_dim=300,\n",
        "    weights=[fasttext_embedding_matrix],\n",
        "    trainable=True,\n",
        "    name='decoder_embedding'\n",
        "  )(decoder_input)\n",
        "\n",
        "decoder_layer1 = GRU(num_units, return_sequences=True)\n",
        "decoder_layer2 = GRU(num_units, return_sequences=True)\n",
        "decoder_layer3 = GRU(num_units, return_sequences=True)\n",
        "decoder_layer4 = GRU(num_units, return_sequences=True)\n",
        "decoder_layer5 = GRU(num_units, return_sequences=True)\n",
        "decoder_final_layer = GRU(num_units, return_sequences=True)\n",
        "\n",
        "\n",
        "decoder_dense = Dense(\n",
        "    num_decoder_words,\n",
        "    activation='linear',\n",
        "    name='decoder_output'\n",
        ")\n",
        "\n",
        "def connect_decoder(initial_state):\n",
        "    con_dec = decoder_layer1(decoder_embedding, initial_state=initial_state)\n",
        "    con_dec = decoder_layer2(con_dec, initial_state=initial_state)\n",
        "    con_dec = decoder_layer3(con_dec, initial_state=initial_state)\n",
        "    con_dec = decoder_layer4(con_dec, initial_state=initial_state)\n",
        "    con_dec = decoder_layer5(con_dec, initial_state=initial_state)\n",
        "    con_dec = decoder_final_layer(con_dec, initial_state=initial_state)\n",
        "\n",
        "\n",
        "    decoder_output = decoder_dense(con_dec)\n",
        "\n",
        "    return decoder_output\n",
        "\n",
        "model_train = Model(inputs=[encoder_input, decoder_input], outputs=[connect_decoder(initial_state=encoder_output)])\n",
        "model_encoder = Model(inputs=[encoder_input], outputs=[encoder_output])\n",
        "decoder_output = connect_decoder(initial_state=decoder_initial_state)\n",
        "\n",
        "model_decoder = Model(inputs=[decoder_input, decoder_initial_state], outputs=[decoder_output])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxaGp7DeYhd1",
        "outputId": "757d2a0f-6f9a-4022-f4f1-a2e402fb101a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " encoder_input (InputLayer)  [(None, None)]               0         []                            \n",
            "                                                                                                  \n",
            " encoder_embedding (Embeddi  (None, None, 100)            2014300   ['encoder_input[0][0]']       \n",
            " ng)                                                                                              \n",
            "                                                                                                  \n",
            " gru_12 (GRU)                (None, None, 512)            943104    ['encoder_embedding[0][0]']   \n",
            "                                                                                                  \n",
            " gru_13 (GRU)                (None, None, 512)            1575936   ['gru_12[0][0]']              \n",
            "                                                                                                  \n",
            " gru_14 (GRU)                (None, None, 512)            1575936   ['gru_13[0][0]']              \n",
            "                                                                                                  \n",
            " gru_15 (GRU)                (None, None, 512)            1575936   ['gru_14[0][0]']              \n",
            "                                                                                                  \n",
            " decoder_input (InputLayer)  [(None, None)]               0         []                            \n",
            "                                                                                                  \n",
            " gru_16 (GRU)                (None, None, 512)            1575936   ['gru_15[0][0]']              \n",
            "                                                                                                  \n",
            " decoder_embedding (Embeddi  (None, None, 300)            2752680   ['decoder_input[0][0]']       \n",
            " ng)                                                      0                                       \n",
            "                                                                                                  \n",
            " gru_17 (GRU)                (None, 512)                  1575936   ['gru_16[0][0]']              \n",
            "                                                                                                  \n",
            " gru_18 (GRU)                (None, None, 512)            1250304   ['decoder_embedding[0][0]',   \n",
            "                                                                     'gru_17[0][0]']              \n",
            "                                                                                                  \n",
            " gru_19 (GRU)                (None, None, 512)            1575936   ['gru_18[0][0]',              \n",
            "                                                                     'gru_17[0][0]']              \n",
            "                                                                                                  \n",
            " gru_20 (GRU)                (None, None, 512)            1575936   ['gru_19[0][0]',              \n",
            "                                                                     'gru_17[0][0]']              \n",
            "                                                                                                  \n",
            " gru_21 (GRU)                (None, None, 512)            1575936   ['gru_20[0][0]',              \n",
            "                                                                     'gru_17[0][0]']              \n",
            "                                                                                                  \n",
            " gru_22 (GRU)                (None, None, 512)            1575936   ['gru_21[0][0]',              \n",
            "                                                                     'gru_17[0][0]']              \n",
            "                                                                                                  \n",
            " gru_23 (GRU)                (None, None, 512)            1575936   ['gru_22[0][0]',              \n",
            "                                                                     'gru_17[0][0]']              \n",
            "                                                                                                  \n",
            " decoder_output (Dense)      (None, None, 91756)          4707082   ['gru_23[0][0]']              \n",
            "                                                          8                                       \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 94564696 (360.74 MB)\n",
            "Trainable params: 94564696 (360.74 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def sparse_cross_entropy(y_true, y_pred):\n",
        "    loss = nn.sparse_softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred)\n",
        "    loss_mean = reduce_mean(loss)\n",
        "    return loss_mean\n",
        "\n",
        "decoder_target = Input(shape=(None,), dtype=int32)\n",
        "\n",
        "model_train.compile(optimizer=optimizers.RMSprop(learning_rate=1e-3), loss=sparse_cross_entropy)\n",
        "\n",
        "model_train.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrn204gHZAlk",
        "outputId": "3753d77e-95ab-4be4-cebb-1cb9ef7c5600"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/44\n",
            "1848/1848 [==============================] - 249s 125ms/step - loss: 4.0813\n",
            "Epoch 2/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 3.0923\n",
            "Epoch 3/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 2.4200\n",
            "Epoch 4/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 1.9690\n",
            "Epoch 5/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 1.6636\n",
            "Epoch 6/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 1.4528\n",
            "Epoch 7/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 1.2961\n",
            "Epoch 8/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 1.1728\n",
            "Epoch 9/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 1.0711\n",
            "Epoch 10/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.9848\n",
            "Epoch 11/44\n",
            "1848/1848 [==============================] - 225s 122ms/step - loss: 0.9097\n",
            "Epoch 12/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.8424\n",
            "Epoch 13/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.7819\n",
            "Epoch 14/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.7268\n",
            "Epoch 15/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.6762\n",
            "Epoch 16/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.6295\n",
            "Epoch 17/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.5861\n",
            "Epoch 18/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.5463\n",
            "Epoch 19/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.5095\n",
            "Epoch 20/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.4749\n",
            "Epoch 21/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.4429\n",
            "Epoch 22/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.4135\n",
            "Epoch 23/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.3866\n",
            "Epoch 24/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.3616\n",
            "Epoch 25/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.3384\n",
            "Epoch 26/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.3166\n",
            "Epoch 27/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.2967\n",
            "Epoch 28/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.2782\n",
            "Epoch 29/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.2620\n",
            "Epoch 30/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.2474\n",
            "Epoch 31/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.2327\n",
            "Epoch 32/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.2207\n",
            "Epoch 33/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.2092\n",
            "Epoch 34/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.1996\n",
            "Epoch 35/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.1911\n",
            "Epoch 36/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.1827\n",
            "Epoch 37/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.1766\n",
            "Epoch 38/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.1719\n",
            "Epoch 39/44\n",
            "1848/1848 [==============================] - 224s 121ms/step - loss: 0.1683\n",
            "Epoch 40/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.1650\n",
            "Epoch 41/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.1637\n",
            "Epoch 42/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.1627\n",
            "Epoch 43/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.1619\n",
            "Epoch 44/44\n",
            "1848/1848 [==============================] - 223s 121ms/step - loss: 0.1631\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7f68cd889420>"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_data = {\n",
        "    'encoder_input': encoder_input_data,\n",
        "    'decoder_input': decoder_input_data\n",
        "}\n",
        "\n",
        "y_data = {'decoder_output': decoder_output_data}\n",
        "\n",
        "model_train.fit(\n",
        "    x=X_data, y=y_data,\n",
        "    batch_size=256,\n",
        "    epochs=44\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "7QmFL7VV0B18"
      },
      "outputs": [],
      "source": [
        "def translate(input_text):\n",
        "    input_tokens = text_to_tokens(\n",
        "        text=input_text,\n",
        "        reverse=True,\n",
        "        padding='pre',\n",
        "        tokenizer=tokenizer_en\n",
        "    )\n",
        "\n",
        "    initial_state = model_encoder.predict(input_tokens)\n",
        "\n",
        "    max_tokens = max_tokens_tr\n",
        "\n",
        "    decoder_input_data = np.zeros(shape=(1, max_tokens), dtype=np.int)\n",
        "\n",
        "    token_int = token_start\n",
        "    output_text = ''\n",
        "    count_tokens = 0\n",
        "\n",
        "    while token_int != token_end and count_tokens < max_tokens:\n",
        "        decoder_input_data[0, count_tokens] = token_int\n",
        "        x_data = {'decoder_initial_state': initial_state, 'decoder_input': decoder_input_data}\n",
        "\n",
        "        decoder_output = model_decoder.predict(x_data)\n",
        "\n",
        "        token_onehot = decoder_output[0, count_tokens, :]\n",
        "        token_int = np.argmax(token_onehot)\n",
        "\n",
        "        sampled_word = token_to_word(token_int, index_to_word_tr)\n",
        "\n",
        "        output_text += ' ' + sampled_word\n",
        "        count_tokens += 1\n",
        "\n",
        "    return output_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLMmVAdB0D1h",
        "outputId": "e107cdf3-1c04-42ac-85f1-f6fed57d10b1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-30-aa7f1b3791ad>:13: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  decoder_input_data = np.zeros(shape=(1, max_tokens), dtype=np.int)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "en_texts = [\n",
        "    'How are you today?', 'Which road leads to the airport?', \"Where did tom go\", \"Drinking water is important\", \"I like to do sports\",\n",
        "    'How was your weekend?','They take the bus to work','His dog barks loudly','We always eat dinner together',\"I like to read books.\",\n",
        "    \"She is a good student.\",\"The sun rises in the east.\", \"My favorite color is blue.\",\"Do you want to go for a walk?\",\n",
        "    \"I enjoy listening to music.\",\"He plays the guitar very well.\",\"They went to the park yesterday.\", \"It is a beautiful day outside.\",\n",
        "    \"Can you pass the salt, please?\",\"I need to buy some groceries.\",\"The cat is sitting on the windowsill.\",\"We are going on vacation next week.\",\n",
        "    \"I love to eat pizza and pasta.\", \"The movie starts at 7:00 PM.\", \"She always helps her friends.\",\"He has a big family.\",\n",
        "    \"I want to learn how to cook.\", \"The dog is chasing its tail.\", \"I am going to the store to buy some milk.\", \"The sky is clear today.\",\n",
        "    \"I have a red bicycle.\", \"She enjoys playing the piano.\", \"They visited the museum yesterday.\", \"My favorite fruit is strawberries.\",\n",
        "    \"We are planning a picnic.\", \"He can speak three languages.\", \"The book is on the table.\", \"Do you like to swim in the sea?\",\n",
        "    \"I need to call my friend.\", \"The birds are singing in the trees.\", \"She is going to a concert tonight.\", \"I want to learn how to dance.\",\n",
        "    \"The car is parked in the garage.\", \"They have a cute puppy.\", \"I am cooking dinner right now.\", \"The computer is on the desk.\",\n",
        "    \"He plays soccer on weekends.\", \"I like to watch movies at home.\", \"She has a green umbrella.\"\n",
        "]\n",
        "\n",
        "tranlated_texts_list = []\n",
        "\n",
        "for text in en_texts:\n",
        "    text = remove_contractions(text)\n",
        "    text = re.sub(r'[^a-zA-Z]', ' ', text)\n",
        "    text = ' '.join(word.lower() for word in text.split())\n",
        "    tranlated_texts_list.append(translate(input_text=text))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "b6C71CHK0GSv"
      },
      "outputs": [],
      "source": [
        "translator = Translator(to_lang=\"tr\")\n",
        "\n",
        "scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)\n",
        "\n",
        "text_rouge_df = pd.DataFrame(columns=['text', 'real_translation', 'model_translation', 'rouge_socore'])\n",
        "\n",
        "for en_text, trans_text in zip(en_texts, tranlated_texts_list):\n",
        "\n",
        "  real_translate = translator.translate(en_text)\n",
        "\n",
        "  translated_text_by_model = ' '.join(trans_text.split()[:-1])\n",
        "\n",
        "  scores = scorer.score(real_translate, translated_text_by_model)\n",
        "\n",
        "  text_rouge_df.loc[len(text_rouge_df)] = [en_text, real_translate, translated_text_by_model, scores['rougeL'][1]]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "g4ZZCs3u3l3C",
        "outputId": "087b7adb-428f-4fa5-8127-33b5fe658071"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-52962f13-c1d5-4b3f-95ca-28c154c5e3c3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>real_translation</th>\n",
              "      <th>model_translation</th>\n",
              "      <th>rouge_socore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>How are you today?</td>\n",
              "      <td>Bugün nasılsın?</td>\n",
              "      <td>nasıl bir günün değilsin</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Which road leads to the airport?</td>\n",
              "      <td>Hangi yol havaalanına gider?</td>\n",
              "      <td>yol hangi yol yolu ile yol alanına gider</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Where did tom go</td>\n",
              "      <td>Tom nereye gitti?</td>\n",
              "      <td>nereye tom gitti</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Drinking water is important</td>\n",
              "      <td>İçme suyu önemlidir</td>\n",
              "      <td>su içmek su içmek önemlidir</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I like to do sports</td>\n",
              "      <td>Spor yapmayı severim</td>\n",
              "      <td>spor yapmayı severim keşke</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>How was your weekend?</td>\n",
              "      <td>Hafta sonun nasıl geçti?</td>\n",
              "      <td>hafta sonun nasıl bir hafta geçti</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>They take the bus to work</td>\n",
              "      <td>İşe otobüsle giderler</td>\n",
              "      <td>otobüse otobüsle giden otobüse giderler</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>His dog barks loudly</td>\n",
              "      <td>Köpeği yüksek sesle havlıyor</td>\n",
              "      <td>köpeği yüksek sesle müzik kullanarak havladı</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>We always eat dinner together</td>\n",
              "      <td>Akşam yemeğini hep birlikte yeriz</td>\n",
              "      <td>birlikte her zaman akşam yemeği yemek yiyoruz</td>\n",
              "      <td>0.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>I like to read books.</td>\n",
              "      <td>Kitap okumayı seviyorum.</td>\n",
              "      <td>kitaplar okumayı kitaplar severim</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>She is a good student.</td>\n",
              "      <td>İyi bir öğrencidir.</td>\n",
              "      <td>o iyi bir öğrenci</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>The sun rises in the east.</td>\n",
              "      <td>Güneş her zaman doğudan doğar.</td>\n",
              "      <td>güneş doğudan doğar</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>My favorite color is blue.</td>\n",
              "      <td>En sevdiğim renk mavidir.</td>\n",
              "      <td>benim en sevdiğim mavi renk onun renk</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Do you want to go for a walk?</td>\n",
              "      <td>Yürüyüşe çıkmak ister misin?</td>\n",
              "      <td>bir yürüyüşe gitmek ister misin</td>\n",
              "      <td>0.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>I enjoy listening to music.</td>\n",
              "      <td>Müzik dinlemeyi severim.</td>\n",
              "      <td>müzik dinlemeye çalışırken benim zevk alıyorum</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>He plays the guitar very well.</td>\n",
              "      <td>O, çok iyi şekilde gitar çalar.</td>\n",
              "      <td>o çok iyi gitar çalar çalıyor</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>They went to the park yesterday.</td>\n",
              "      <td>Dün parka gittim.</td>\n",
              "      <td>dün parka gittiler</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>It is a beautiful day outside.</td>\n",
              "      <td>Dışarısı güzel bir gün.</td>\n",
              "      <td>güzel bir gün için güzel bir gece</td>\n",
              "      <td>0.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Can you pass the salt, please?</td>\n",
              "      <td>Bana tuzu uzatır mısın, lütfen?</td>\n",
              "      <td>eti uzatır mısın lütfen tavuk e</td>\n",
              "      <td>0.777778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>I need to buy some groceries.</td>\n",
              "      <td>Biraz market alışverişi yapmam gerekiyor.</td>\n",
              "      <td>bu tür bir bira satın almam gerekiyor</td>\n",
              "      <td>0.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>The cat is sitting on the windowsill.</td>\n",
              "      <td>Kedi pencere pervazında oturuyor.</td>\n",
              "      <td>kedi kanepede oturuyor</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>We are going on vacation next week.</td>\n",
              "      <td>Haftaya tatile çıkıyoruz.</td>\n",
              "      <td>gelecek hafta pikniğe gidiyoruz</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>I love to eat pizza and pasta.</td>\n",
              "      <td>Pizza ve makarna yemeyi seviyorum.</td>\n",
              "      <td>ben pizza ve yemek yemeyi severim ve onu seviy...</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>The movie starts at 7:00 PM.</td>\n",
              "      <td>Film 19:00 'da başlıyor.</td>\n",
              "      <td>film san francisco ile sona erdi</td>\n",
              "      <td>0.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>She always helps her friends.</td>\n",
              "      <td>O her zaman arkadaşlarına yardım eder.</td>\n",
              "      <td>her zaman arkadaşlarına yardım eder</td>\n",
              "      <td>0.888889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>He has a big family.</td>\n",
              "      <td>Onun büyük bir ailesi vardır.</td>\n",
              "      <td>onun büyük bir ailesi var</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>I want to learn how to cook.</td>\n",
              "      <td>Ben kayak yapmayı öğrenmek istiyorum.</td>\n",
              "      <td>yemek yemeği birini nasıl öğrenmek istiyorum</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>The dog is chasing its tail.</td>\n",
              "      <td>Köpek kuyruğunu kovalıyor.</td>\n",
              "      <td>köpek kuşları görmezden duruyor</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>I am going to the store to buy some milk.</td>\n",
              "      <td>Süt almak için dükkana gidiyorum.</td>\n",
              "      <td>biraz süt almak için mağazaya gidiyorum</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>The sky is clear today.</td>\n",
              "      <td>Bugün gökyüzü açık.</td>\n",
              "      <td>bugün gökyüzü açık ve temiz</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>I have a red bicycle.</td>\n",
              "      <td>Bir bisikletim var.</td>\n",
              "      <td>kırmızı bir kırmızı arabam var</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>She enjoys playing the piano.</td>\n",
              "      <td>Piyano çalmaktan hoşlanır.</td>\n",
              "      <td>o piyano çalmaktan çok hoşlanır</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>They visited the museum yesterday.</td>\n",
              "      <td>Dün müzeyi ziyaret ettiler.</td>\n",
              "      <td>onlar müzeyi dün ziyaret ettiler</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>My favorite fruit is strawberries.</td>\n",
              "      <td>En sevdiğim meyve çilektir.</td>\n",
              "      <td>benim favori sevdiğim balık en sevdiğim çorap</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>We are planning a picnic.</td>\n",
              "      <td>Piknik planlıyoruz.</td>\n",
              "      <td>piknik yapmak istiyoruz</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>He can speak three languages.</td>\n",
              "      <td>O, üç dilde konuşabilir.</td>\n",
              "      <td>o üç dili konuşabilir</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>The book is on the table.</td>\n",
              "      <td>Kitap masanın üstündedir.</td>\n",
              "      <td>kitap masanın üstünde</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Do you like to swim in the sea?</td>\n",
              "      <td>Denizde yüzmeyi sever misin?</td>\n",
              "      <td>gölde yüzmeyi sever misin</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>I need to call my friend.</td>\n",
              "      <td>Arkadaşımı aramam lazım.</td>\n",
              "      <td>arkadaşımı aramam gerekiyor</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>The birds are singing in the trees.</td>\n",
              "      <td>Kuşlar ağaçlarda ötüyorlar.</td>\n",
              "      <td>kuşlar ağaçlarda kuşlar ötüyor</td>\n",
              "      <td>0.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>She is going to a concert tonight.</td>\n",
              "      <td>Bu gece konsere gidiyor.</td>\n",
              "      <td>o bu gece bir konsere gidiyor</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>I want to learn how to dance.</td>\n",
              "      <td>Dans etmeyi öğrenmek istiyorum.</td>\n",
              "      <td>ben dans etmeyi öğrenmek istiyorum</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>The car is parked in the garage.</td>\n",
              "      <td>Araba garajda ​​.</td>\n",
              "      <td>araba arabası park caddesi yanında</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>They have a cute puppy.</td>\n",
              "      <td>Sevimli bir yavru köpekleri var.</td>\n",
              "      <td>sevimli bir hayvan yavrusu var</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>I am cooking dinner right now.</td>\n",
              "      <td>Yemek yapıyorum şu an.</td>\n",
              "      <td>şimdi akşam yemeği yemek yiyorum</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>The computer is on the desk.</td>\n",
              "      <td>Bilgisayar masanın üstündedir.</td>\n",
              "      <td>bilgisayar makinesi masanın üzerinde</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>He plays soccer on weekends.</td>\n",
              "      <td>Hafta sonları futbol oynuyor.</td>\n",
              "      <td>o hafta sonlarında futbol oynuyor</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>I like to watch movies at home.</td>\n",
              "      <td>Evde film izlemeyi severim.</td>\n",
              "      <td>evde filmler izlemeyi severim</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>She has a green umbrella.</td>\n",
              "      <td>Yeşil bir şemsiyesi var.</td>\n",
              "      <td>onun yeşil bir kedi var</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </div>\n"
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            "text/plain": [
              "                                         text  \\\n",
              "0                          How are you today?   \n",
              "1            Which road leads to the airport?   \n",
              "2                            Where did tom go   \n",
              "3                 Drinking water is important   \n",
              "4                         I like to do sports   \n",
              "5                       How was your weekend?   \n",
              "6                   They take the bus to work   \n",
              "7                        His dog barks loudly   \n",
              "8               We always eat dinner together   \n",
              "9                       I like to read books.   \n",
              "10                     She is a good student.   \n",
              "11                 The sun rises in the east.   \n",
              "12                 My favorite color is blue.   \n",
              "13              Do you want to go for a walk?   \n",
              "14                I enjoy listening to music.   \n",
              "15             He plays the guitar very well.   \n",
              "16           They went to the park yesterday.   \n",
              "17             It is a beautiful day outside.   \n",
              "18             Can you pass the salt, please?   \n",
              "19              I need to buy some groceries.   \n",
              "20      The cat is sitting on the windowsill.   \n",
              "21        We are going on vacation next week.   \n",
              "22             I love to eat pizza and pasta.   \n",
              "23               The movie starts at 7:00 PM.   \n",
              "24              She always helps her friends.   \n",
              "25                       He has a big family.   \n",
              "26               I want to learn how to cook.   \n",
              "27               The dog is chasing its tail.   \n",
              "28  I am going to the store to buy some milk.   \n",
              "29                    The sky is clear today.   \n",
              "30                      I have a red bicycle.   \n",
              "31              She enjoys playing the piano.   \n",
              "32         They visited the museum yesterday.   \n",
              "33         My favorite fruit is strawberries.   \n",
              "34                  We are planning a picnic.   \n",
              "35              He can speak three languages.   \n",
              "36                  The book is on the table.   \n",
              "37            Do you like to swim in the sea?   \n",
              "38                  I need to call my friend.   \n",
              "39        The birds are singing in the trees.   \n",
              "40         She is going to a concert tonight.   \n",
              "41              I want to learn how to dance.   \n",
              "42           The car is parked in the garage.   \n",
              "43                    They have a cute puppy.   \n",
              "44             I am cooking dinner right now.   \n",
              "45               The computer is on the desk.   \n",
              "46               He plays soccer on weekends.   \n",
              "47            I like to watch movies at home.   \n",
              "48                  She has a green umbrella.   \n",
              "\n",
              "                             real_translation  \\\n",
              "0                             Bugün nasılsın?   \n",
              "1                Hangi yol havaalanına gider?   \n",
              "2                           Tom nereye gitti?   \n",
              "3                         İçme suyu önemlidir   \n",
              "4                        Spor yapmayı severim   \n",
              "5                    Hafta sonun nasıl geçti?   \n",
              "6                       İşe otobüsle giderler   \n",
              "7                Köpeği yüksek sesle havlıyor   \n",
              "8           Akşam yemeğini hep birlikte yeriz   \n",
              "9                    Kitap okumayı seviyorum.   \n",
              "10                        İyi bir öğrencidir.   \n",
              "11             Güneş her zaman doğudan doğar.   \n",
              "12                  En sevdiğim renk mavidir.   \n",
              "13               Yürüyüşe çıkmak ister misin?   \n",
              "14                   Müzik dinlemeyi severim.   \n",
              "15            O, çok iyi şekilde gitar çalar.   \n",
              "16                          Dün parka gittim.   \n",
              "17                    Dışarısı güzel bir gün.   \n",
              "18            Bana tuzu uzatır mısın, lütfen?   \n",
              "19  Biraz market alışverişi yapmam gerekiyor.   \n",
              "20          Kedi pencere pervazında oturuyor.   \n",
              "21                  Haftaya tatile çıkıyoruz.   \n",
              "22         Pizza ve makarna yemeyi seviyorum.   \n",
              "23                   Film 19:00 'da başlıyor.   \n",
              "24     O her zaman arkadaşlarına yardım eder.   \n",
              "25              Onun büyük bir ailesi vardır.   \n",
              "26      Ben kayak yapmayı öğrenmek istiyorum.   \n",
              "27                 Köpek kuyruğunu kovalıyor.   \n",
              "28          Süt almak için dükkana gidiyorum.   \n",
              "29                        Bugün gökyüzü açık.   \n",
              "30                        Bir bisikletim var.   \n",
              "31                 Piyano çalmaktan hoşlanır.   \n",
              "32                Dün müzeyi ziyaret ettiler.   \n",
              "33                En sevdiğim meyve çilektir.   \n",
              "34                        Piknik planlıyoruz.   \n",
              "35                   O, üç dilde konuşabilir.   \n",
              "36                  Kitap masanın üstündedir.   \n",
              "37               Denizde yüzmeyi sever misin?   \n",
              "38                   Arkadaşımı aramam lazım.   \n",
              "39                Kuşlar ağaçlarda ötüyorlar.   \n",
              "40                   Bu gece konsere gidiyor.   \n",
              "41            Dans etmeyi öğrenmek istiyorum.   \n",
              "42                          Araba garajda ​​.   \n",
              "43           Sevimli bir yavru köpekleri var.   \n",
              "44                     Yemek yapıyorum şu an.   \n",
              "45             Bilgisayar masanın üstündedir.   \n",
              "46              Hafta sonları futbol oynuyor.   \n",
              "47                Evde film izlemeyi severim.   \n",
              "48                   Yeşil bir şemsiyesi var.   \n",
              "\n",
              "                                    model_translation  rouge_socore  \n",
              "0                            nasıl bir günün değilsin      0.400000  \n",
              "1            yol hangi yol yolu ile yol alanına gider      0.800000  \n",
              "2                                    nereye tom gitti      0.666667  \n",
              "3                         su içmek su içmek önemlidir      0.500000  \n",
              "4                          spor yapmayı severim keşke      1.000000  \n",
              "5                   hafta sonun nasıl bir hafta geçti      1.000000  \n",
              "6             otobüse otobüsle giden otobüse giderler      0.600000  \n",
              "7        köpeği yüksek sesle müzik kullanarak havladı      0.750000  \n",
              "8       birlikte her zaman akşam yemeği yemek yiyoruz      0.428571  \n",
              "9                   kitaplar okumayı kitaplar severim      0.333333  \n",
              "10                                  o iyi bir öğrenci      0.250000  \n",
              "11                                güneş doğudan doğar      0.750000  \n",
              "12              benim en sevdiğim mavi renk onun renk      0.800000  \n",
              "13                    bir yürüyüşe gitmek ister misin      0.857143  \n",
              "14     müzik dinlemeye çalışırken benim zevk alıyorum      0.500000  \n",
              "15                      o çok iyi gitar çalar çalıyor      0.833333  \n",
              "16                                 dün parka gittiler      0.750000  \n",
              "17                  güzel bir gün için güzel bir gece      0.625000  \n",
              "18                    eti uzatır mısın lütfen tavuk e      0.777778  \n",
              "19              bu tür bir bira satın almam gerekiyor      0.142857  \n",
              "20                             kedi kanepede oturuyor      0.400000  \n",
              "21                    gelecek hafta pikniğe gidiyoruz      0.000000  \n",
              "22  ben pizza ve yemek yemeyi severim ve onu seviy...      0.800000  \n",
              "23                   film san francisco ile sona erdi      0.142857  \n",
              "24                her zaman arkadaşlarına yardım eder      0.888889  \n",
              "25                          onun büyük bir ailesi var      0.750000  \n",
              "26       yemek yemeği birini nasıl öğrenmek istiyorum      0.400000  \n",
              "27                    köpek kuşları görmezden duruyor      0.333333  \n",
              "28            biraz süt almak için mağazaya gidiyorum      0.750000  \n",
              "29                        bugün gökyüzü açık ve temiz      1.000000  \n",
              "30                     kırmızı bir kırmızı arabam var      0.666667  \n",
              "31                    o piyano çalmaktan çok hoşlanır      1.000000  \n",
              "32                   onlar müzeyi dün ziyaret ettiler      0.666667  \n",
              "33      benim favori sevdiğim balık en sevdiğim çorap      0.600000  \n",
              "34                            piknik yapmak istiyoruz      0.333333  \n",
              "35                              o üç dili konuşabilir      0.750000  \n",
              "36                              kitap masanın üstünde      0.800000  \n",
              "37                          gölde yüzmeyi sever misin      0.800000  \n",
              "38                        arkadaşımı aramam gerekiyor      0.600000  \n",
              "39                     kuşlar ağaçlarda kuşlar ötüyor      0.857143  \n",
              "40                      o bu gece bir konsere gidiyor      1.000000  \n",
              "41                 ben dans etmeyi öğrenmek istiyorum      1.000000  \n",
              "42                 araba arabası park caddesi yanında      0.500000  \n",
              "43                     sevimli bir hayvan yavrusu var      0.500000  \n",
              "44                   şimdi akşam yemeği yemek yiyorum      0.200000  \n",
              "45               bilgisayar makinesi masanın üzerinde      0.600000  \n",
              "46                  o hafta sonlarında futbol oynuyor      1.000000  \n",
              "47                      evde filmler izlemeyi severim      0.750000  \n",
              "48                            onun yeşil bir kedi var      0.800000  "
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "text_rouge_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xjp66iW20LVi",
        "outputId": "e09932c0-660e-4262-be80-82b9762a79ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mean 0.6459912536443149\n",
            "std 0.25526077506545636\n",
            "max 1.0\n",
            "min 0.6459912536443149\n"
          ]
        }
      ],
      "source": [
        "print('mean', np.mean(text_rouge_df['rouge_socore']))\n",
        "print('std', np.std(text_rouge_df['rouge_socore']))\n",
        "print('max', np.max(text_rouge_df['rouge_socore']))\n",
        "print('min', np.mean(text_rouge_df['rouge_socore']))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "V100",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.11.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}