Language_detector_in_Javascript.ipynb

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    "colab": {
      "name": "Language detector in Javascript.ipynb",
      "provenance": [],
      "mount_file_id": "15yzaJFoRU7a1xeukFS-xtd-fZ49_xMjH",
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      "name": "python3",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/gianlucamalato/machinelearning/blob/master/Language_detector_in_Javascript.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install m2cgen\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwTwY-MiYsNS",
        "outputId": "3019563b-b9c3-4914-9058-29b0d5070ec6"
      },
      "execution_count": null,
      "outputs": [
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          "text": [
            "Collecting m2cgen\n",
            "  Downloading m2cgen-0.9.0-py3-none-any.whl (73 kB)\n",
            "\u001b[?25l\r\u001b[K     |████▌                           | 10 kB 10.6 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 20 kB 14.6 MB/s eta 0:00:01\r\u001b[K     |█████████████▍                  | 30 kB 12.5 MB/s eta 0:00:01\r\u001b[K     |█████████████████▉              | 40 kB 10.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▎         | 51 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 61 kB 5.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 71 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 73 kB 1.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from m2cgen) (1.21.5)\n",
            "Installing collected packages: m2cgen\n",
            "Successfully installed m2cgen-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import m2cgen as m2c\n"
      ],
      "metadata": {
        "id": "R3y2WrF5ZROW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XjN3spAmbXb5"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "from sklearn.ensemble import ExtraTreesClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pk5P1JNsbk1A"
      },
      "source": [
        "os.chdir(\"/content/drive/My Drive/Text Generator\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EhTFcTybzCA"
      },
      "source": [
        "def file2sentences(filename):\n",
        "  txt = \"\"\n",
        "\n",
        "  with open(filename,\"r\",encoding=\"utf-8\") as f:\n",
        "    txt = f.read()\n",
        "\n",
        "  txt = txt.replace(\"?\",\".\")\n",
        "  txt = txt.replace(\"!\",\".\")\n",
        "  txt = txt.replace(\"»\",\"\")\n",
        "  txt = txt.replace(\"«\",\"\")\n",
        "  txt = txt.replace(\":\",\"\")\n",
        "  txt = txt.replace(\";\",\"\")\n",
        "  txt = txt.replace(\"...\",\".\")\n",
        "  txt = txt.replace(\"…\",\".\")\n",
        "  txt = txt.replace(\"\\n\",\".\")\n",
        "  txt = txt.replace(\"  \",\" \")\n",
        "  txt = txt.replace(\"“\",\"\")\n",
        "  txt = txt.replace(\"„\",\"\")\n",
        "  \n",
        "  sentences = txt.split(\".\")\n",
        "\n",
        "  for i in range(len(sentences)):\n",
        "    sentences[i] = sentences[i].strip()\n",
        "\n",
        "  sentences = [x for x in sentences if x != \"\"]\n",
        "\n",
        "  return sentences"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqGPPSmOhosf"
      },
      "source": [
        "http://www.nietzschesource.org/#eKGWB/Za-I\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GwprqyuFbmfC"
      },
      "source": [
        "italian = file2sentences(\"isola del male.txt\")\n",
        "english = file2sentences(\"island of evil.txt\")\n",
        "german = file2sentences(\"also sprach zarathustra.txt\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xW6OBE1mnNbv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3161d3cc-b818-4580-f3d1-f42fd533d5ed"
      },
      "source": [
        "german[100:110]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Einst blickte die Seele verächtlich auf den Leib und damals war diese Verachtung das Höchste — sie wollte ihn mager, grässlich, verhungert',\n",
              " 'So dachte sie ihm und der Erde zu entschlüpfen',\n",
              " 'Oh diese Seele war selber noch mager, grässlich und verhungert und Grausamkeit war die Wollust dieser Seele',\n",
              " 'Aber auch ihr noch, meine Brüder, sprecht mir was kündet euer Leib von eurer Seele',\n",
              " 'Ist eure Seele nicht Armuth und Schmutz und ein erbärmliches Behagen',\n",
              " 'Wahrlich, ein schmutziger Strom ist der Mensch',\n",
              " 'Man muss schon ein Meer sein, um einen schmutzigen Strom aufnehmen zu können, ohne unrein zu werden',\n",
              " 'Seht, ich lehre euch den Übermenschen der ist diess Meer, in ihm kann eure grosse Verachtung untergehn',\n",
              " 'Was ist das Grösste, das ihr erleben könnt',\n",
              " 'Das ist die Stunde der grossen Verachtung']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qta7gRLpcTcu"
      },
      "source": [
        "X = np.array(italian + english + german)\n",
        "y = np.array(['it']*len(italian) + ['en']*len(english) + ['de']*len(german))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jHpku9Y-qWRG",
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        },
        "outputId": "fa879c49-531c-4e3f-88e6-4027a221da04"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4195,)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-PdRomXcdLH"
      },
      "source": [
        "df = pd.DataFrame({'sentence':X,'language':y})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APrPK428KLJ6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "8f4479d4-1a84-4e1d-b67d-1bf61f330136"
      },
      "source": [
        "df.sample(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                               sentence language\n",
              "3499  Vernichter sind es, die stellen Fallen auf für...       de\n",
              "1621  Some electromagnetic storm in the atmosphere, ...       en\n",
              "1923                                                  \"       en\n",
              "1821           My mission, now, is to find those people       en\n",
              "1766                        The observed sound was real       en\n",
              "1007  Ormai ho capito che in questo posto sperduto i...       it\n",
              "1101  Se esce fuori una storia del genere, come mini...       it\n",
              "1831  His abdomen is swollen there are some green st...       en\n",
              "493   Non è tangibile, altrimenti l'avremmo già trovata       it\n",
              "972                                  Mi avvicino ancora       it\n",
              "170                                   Sono già le 19,20       it\n",
              "1018  Riconosco altre costellazioni, tutte come le r...       it\n",
              "3427                                                  *       de\n",
              "909                               Rallento la picchiata       it\n",
              "3568  Er glaubt immer an Das, womit er am stärksten ...       de\n",
              "3726  Vieles fand ich hier böse genannt und dort mit...       de\n",
              "2058  A sound, an ancestral heartbeat, comes from th...       en\n",
              "1509  Disappointed, I put it in my suit pocket and w...       en\n",
              "1616                   It's the first of a line of five       en\n",
              "2187  It comes to mind that only now, a few moments ...       en"
            ],
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              "      <th>3568</th>\n",
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              "      <td>Vieles fand ich hier böse genannt und dort mit...</td>\n",
              "      <td>de</td>\n",
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              "    <tr>\n",
              "      <th>2058</th>\n",
              "      <td>A sound, an ancestral heartbeat, comes from th...</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1509</th>\n",
              "      <td>Disappointed, I put it in my suit pocket and w...</td>\n",
              "      <td>en</td>\n",
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              "    <tr>\n",
              "      <th>1616</th>\n",
              "      <td>It's the first of a line of five</td>\n",
              "      <td>en</td>\n",
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      "source": [
        "df['language'].value_counts().plot(kind = 'bar')"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3enSHyeCdNhX"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ToCMjd9P5cQO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27931361-4b61-4e64-cb6d-6852bf11c187"
      },
      "source": [
        "cnt = CountVectorizer(analyzer = 'char',ngram_range=(2,2),binary=True)\n",
        "\n",
        "cnt.fit(X_train)\n",
        "\n",
        "len(cnt.get_feature_names_out())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "842"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "_RRL9QG4YgAr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEQz7h9e6CsO"
      },
      "source": [
        "model.fit(cnt.transform(X_train),y_train)\n",
        "y_pred = model.predict(cnt.transform(X_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxfV7tGK6Hl7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f130f31-6926-4ddf-beb8-881bea932365"
      },
      "source": [
        "confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[513,   2,   0],\n",
              "       [ 11, 412,  14],\n",
              "       [  3,  15, 415]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVcOGZ5G6J8G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0eaa9753-659e-478a-d657-c6ef7f39d7cf"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          de       0.97      1.00      0.98       515\n",
            "          en       0.96      0.94      0.95       437\n",
            "          it       0.97      0.96      0.96       433\n",
            "\n",
            "    accuracy                           0.97      1385\n",
            "   macro avg       0.97      0.97      0.97      1385\n",
            "weighted avg       0.97      0.97      0.97      1385\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.classes_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bDRPkYHWf0Tl",
        "outputId": "45b9d49f-ac2d-4430-a461-a65593328acb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['de', 'en', 'it'], dtype='<U2')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CW2R0mo6OKo"
      },
      "source": [
        "test = [\n",
        "(\"Ci vediamo domani\", 'Italian sentence'),\n",
        "(\"See you tomorrow\", 'English sentence'),\n",
        "(\"I would like a dish of spaghetti or even a good italian pizza\", 'English sentence with Italian words'),\n",
        "(\"Espresso or cappuccino? I can't choose\" , 'English sentence with many Italian words'),\n",
        "(\"Espresso o cappuccino?\" , 'Italian sentence'),\n",
        "('Ci vediamo nel weekend', 'Italian sentence with English word'),\n",
        "('weekend', 'English word'),\n",
        "('remote work', 'English expression'),\n",
        "('Weltanschauung', 'German word'),\n",
        "('Auf Wiedersehen!', 'German expression'),\n",
        "('Mare', 'Italian word'),\n",
        "('Buongiorno', 'Italian word'),\n",
        "('Facciamo una call conference in remote meeting su skype', 'Italian sentence with many English words'),\n",
        "('hyperparameters', 'English word not present in original corpus'),\n",
        "('machine learning', 'English expression not present in original corpus '),\n",
        "('Bitte geben Sie mir Spaghetti und ein Stück Pizza', 'German sentence with Italian words'),\n",
        "('Ich traf Annalisa in Colosseo und sie sagte mir, sie hätte gerne Spaghetti und ein Stück Pizza', 'German sentence with many Italian words'),\n",
        "('Vedremo con calma cosa possiamo fare','Italian sentence'),\n",
        "('Harrier','English word often present in both Italian and English corpus')\n",
        "        ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "qX9ETHEMZWUN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AU0JE9I6OKv"
      },
      "source": [
        "pred = model.predict(cnt.transform([x[0] for x in test]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQJH5xtx8tFm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60b3ca1b-98d4-4260-df1a-0416c8f214c2"
      },
      "source": [
        "for i in range(len(test)):\n",
        "  print(\"Sentence: {}\".format(test[i][0]))\n",
        "  print(\"Comment: {}\".format(test[i][1]))\n",
        "  print(\"Predicted language: {}\".format(pred[i]))\n",
        "  print(\"-----------------------\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence: Ci vediamo domani\n",
            "Comment: Italian sentence\n",
            "Predicted language: it\n",
            "-----------------------\n",
            "Sentence: See you tomorrow\n",
            "Comment: English sentence\n",
            "Predicted language: en\n",
            "-----------------------\n",
            "Sentence: I would like a dish of spaghetti or even a good italian pizza\n",
            "Comment: English sentence with Italian words\n",
            "Predicted language: en\n",
            "-----------------------\n",
            "Sentence: Espresso or cappuccino? I can't choose\n",
            "Comment: English sentence with many Italian words\n",
            "Predicted language: en\n",
            "-----------------------\n",
            "Sentence: Espresso o cappuccino?\n",
            "Comment: Italian sentence\n",
            "Predicted language: it\n",
            "-----------------------\n",
            "Sentence: Ci vediamo nel weekend\n",
            "Comment: Italian sentence with English word\n",
            "Predicted language: en\n",
            "-----------------------\n",
            "Sentence: weekend\n",
            "Comment: English word\n",
            "Predicted language: de\n",
            "-----------------------\n",
            "Sentence: remote work\n",
            "Comment: English expression\n",
            "Predicted language: en\n",
            "-----------------------\n",
            "Sentence: Weltanschauung\n",
            "Comment: German word\n",
            "Predicted language: de\n",
            "-----------------------\n",
            "Sentence: Auf Wiedersehen!\n",
            "Comment: German expression\n",
            "Predicted language: de\n",
            "-----------------------\n",
            "Sentence: Mare\n",
            "Comment: Italian word\n",
            "Predicted language: en\n",
            "-----------------------\n",
            "Sentence: Buongiorno\n",
            "Comment: Italian word\n",
            "Predicted language: en\n",
            "-----------------------\n",
            "Sentence: Facciamo una call conference in remote meeting su skype\n",
            "Comment: Italian sentence with many English words\n",
            "Predicted language: it\n",
            "-----------------------\n",
            "Sentence: hyperparameters\n",
            "Comment: English word not present in original corpus\n",
            "Predicted language: en\n",
            "-----------------------\n",
            "Sentence: machine learning\n",
            "Comment: English expression not present in original corpus \n",
            "Predicted language: en\n",
            "-----------------------\n",
            "Sentence: Bitte geben Sie mir Spaghetti und ein Stück Pizza\n",
            "Comment: German sentence with Italian words\n",
            "Predicted language: de\n",
            "-----------------------\n",
            "Sentence: Ich traf Annalisa in Colosseo und sie sagte mir, sie hätte gerne Spaghetti und ein Stück Pizza\n",
            "Comment: German sentence with many Italian words\n",
            "Predicted language: it\n",
            "-----------------------\n",
            "Sentence: Vedremo con calma cosa possiamo fare\n",
            "Comment: Italian sentence\n",
            "Predicted language: it\n",
            "-----------------------\n",
            "Sentence: Harrier\n",
            "Comment: English word often present in both Italian and English corpus\n",
            "Predicted language: de\n",
            "-----------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.setrecursionlimit(10000)"
      ],
      "metadata": {
        "id": "RUMROA64dIFA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(m2c.export_to_javascript(model))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HI14eS-Mc9Iy",
        "outputId": "a5da1686-6bb9-498e-d935-818d69822c7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "}\n",
            "\n"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnt.get_feature_names_out()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDg5dt2qdp2q",
        "outputId": "6f8744f8-96bf-4590-f5fd-ceb6f0eeb078"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "       'sà', 'sä', 'sé', 'sì', 'sö', 'sü', 't ', 't\"', \"t'\", 't,', 't-',\n",
              "       'ta', 'tb', 'tc', 'td', 'te', 'tf', 'tg', 'th', 'ti', 'tl', 'tm',\n",
              "       'tn', 'to', 'tr', 'ts', 'tt', 'tu', 'tv', 'tw', 'tx', 'ty', 'tz',\n",
              "       'tà', 'tä', 'tö', 'tü', 't’', 't”', 'u ', 'u,', 'u-', 'ua', 'ub',\n",
              "       'uc', 'ud', 'ue', 'uf', 'ug', 'uh', 'ui', 'uk', 'ul', 'um', 'un',\n",
              "       'uo', 'up', 'ur', 'us', 'ut', 'uu', 'uv', 'uw', 'ux', 'uy', 'uz',\n",
              "       'uä', 'uò', 'u”', \"v'\", 'v,', 'v-', 'va', 've', 'vi', 'vo', 'vr',\n",
              "       'vu', 'vv', 'vy', 'vö', 'w ', 'w,', 'wa', 'wd', 'we', 'wh', 'wi',\n",
              "       'wk', 'wl', 'wn', 'wo', 'wr', 'ws', 'wu', 'ww', 'wä', 'wö', 'wü',\n",
              "       'x ', 'x,', 'xa', 'xc', 'xe', 'xi', 'xp', 'xt', 'xu', 'y ', 'y\"',\n",
              "       \"y'\", 'y(', 'y,', 'y-', 'ya', 'yb', 'ye', 'yi', 'ym', 'yo', 'yp',\n",
              "       'yr', 'ys', 'yt', 'yz', 'z ', \"z'\", 'z,', 'za', 'ze', 'zg', 'zh',\n",
              "       'zi', 'zl', 'zn', 'zo', 'zt', 'zu', 'zw', 'zy', 'zz', 'zä', 'zö',\n",
              "       'zü', 'à ', 'à,', 'äb', 'äc', 'äd', 'äe', 'äf', 'äg', 'äh', 'äl',\n",
              "       'äm', 'än', 'är', 'äs', 'ät', 'äu', 'è ', 'é ', 'é,', 'ì ', 'ì,',\n",
              "       'ò ', 'ò,', 'öb', 'öc', 'öd', 'öf', 'ög', 'öh', 'öl', 'ön', 'öp',\n",
              "       'ör', 'ös', 'öt', 'öw', 'ù ', 'ù,', 'üb', 'üc', 'üd', 'üg', 'üh',\n",
              "       'ül', 'üm', 'ün', 'üp', 'ür', 'üs', 'üt', '\\u200b ', '– ', '— ',\n",
              "       '’a', '’o', '’s', '’t', '’u', '” ', '”,'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "xhsDSY9avFPp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}