foodorder.ipynb

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              "\"I'd like a medium pepperoni pizza with extra cheese, please.\""
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      "cell_type": "markdown",
      "source": [
        "#restaurant menu order"
      ],
      "metadata": {
        "id": "-XQyL9TYogBD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the text file into a dataframe\n",
        "df = pd.read_csv('/content/100orders.txt', header=None, names=['text'])\n",
        "\n",
        "# Remove duplicate rows\n",
        "df = df.drop_duplicates()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "yCFtdpmyofWl"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[~df['text'].str.startswith('QR')]"
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        "id": "fhuhDr2JpyM7"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[~df['text'].str.startswith('no image')]"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[~df['text'].str.startswith('Very good')]"
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        "id": "pTNN-KqdrOGU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[~df['text'].str.startswith('Amazing')]"
      ],
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        "id": "TEiP16N_rFGY"
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      "execution_count": null,
      "outputs": []
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              "    index                                               text\n",
              "0       0  I'd like a medium pepperoni pizza with extra c...\n",
              "1       1  Can I get a bowl of your veggie soup and a sid...\n",
              "2       2  I'd like a grilled chicken sandwich with avoca...\n",
              "3       3  Can I get a large coffee with cream and two su...\n",
              "4       4  I'd like a tuna salad on whole grain bread wit...\n",
              "5       5  Can I get a bowl of your minestrone soup and a...\n",
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              "7       7  Can I get a Caesar salad with grilled chicken ...\n",
              "8       8  I'd like a medium pepperoni and mushroom pizza...\n",
              "9       9  Can I get a bowl of your chicken noodle soup a...\n",
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              "11     11  Can I get a medium latte with soy milk and two...\n",
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              "13     13  Can I get a bowl of your tomato basil soup and...\n",
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              "15     15  Can I get a spinach and feta omelette with who...\n",
              "16     16  I'd like a buffalo chicken wrap with lettuce, ...\n",
              "17     17  Can I get a bowl of your chili and a side of c...\n",
              "18     18  I'd like a medium meat lover's pizza with extr...\n",
              "19     19  Can I get a chicken Caesar salad with extra dr...\n",
              "20     20  I'd like a turkey and swiss cheese sandwich on...\n",
              "21     21  Can I get a bowl of your beef barley soup and ...\n",
              "22     22  I'd like a plain bagel with peanut butter and ...\n",
              "23     23  Can I get a medium iced latte with almond milk...\n",
              "24     24  I'd like a tuna melt on rye bread with cheddar...\n",
              "25     25  Can I get a bowl of your split pea soup and a ...\n",
              "26     26  I'd like a medium cheese pizza with extra sauc...\n",
              "27     27  Can I get a chicken parmesan sandwich with mar...\n",
              "28     28  I'd like a bacon, lettuce, and tomato sandwich...\n",
              "29     29  Can I get a bowl of your chicken and wild rice...\n",
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              "31     31  Can I get a Caesar salad with grilled shrimp a...\n",
              "32     32  I'd like a turkey club sandwich on whole grain...\n",
              "33     33  Can I get a bowl of your lentil soup and a sid...\n",
              "34     34  I'd like a medium sausage and onion pizza with...\n",
              "35     35  Can I get a bowl of your chicken tortilla soup...\n",
              "36     36  I'd like a roast beef and cheddar sandwich on ...\n",
              "37     37  Can I get a bowl of your black bean soup and a...\n",
              "38     38  I'd like a medium veggie pizza with extra mush...\n",
              "39     39  Can I get a bowl of your beef and vegetable st..."
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              "                                text\n",
              "551                  Spanish Twister\n",
              "325         Vegetables Club Sandwich\n",
              "697                     Mango Pieces\n",
              "903                    Spanish Latte\n",
              "94                   Wrap Sandwiches\n",
              "243             Family Club Sandwich\n",
              "1136           Fresh Milk With Honey\n",
              "216                Fresh Beef Burger\n",
              "482           Hot Dog Sandwich Combo\n",
              "84             Italian Wrap Sandwich\n",
              "1063                    Kashmiri Tea\n",
              "334            Hot Dog Club Sandwich\n",
              "632                     Calzone Wrap\n",
              "225             Dynamite Beef Burger\n",
              "187         Hot Dog Paratha Sandwich\n",
              "142   Chicken Tandoori Wrap Sandwich\n",
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              "666                         Desserts\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>551</th>\n",
              "      <td>Spanish Twister</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>325</th>\n",
              "      <td>Vegetables Club Sandwich</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>697</th>\n",
              "      <td>Mango Pieces</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>903</th>\n",
              "      <td>Spanish Latte</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Wrap Sandwiches</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>Family Club Sandwich</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1136</th>\n",
              "      <td>Fresh Milk With Honey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>Fresh Beef Burger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>482</th>\n",
              "      <td>Hot Dog Sandwich Combo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>Italian Wrap Sandwich</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1063</th>\n",
              "      <td>Kashmiri Tea</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>334</th>\n",
              "      <td>Hot Dog Club Sandwich</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>Calzone Wrap</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>Dynamite Beef Burger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>Hot Dog Paratha Sandwich</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>Chicken Tandoori Wrap Sandwich</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>Chicken Nuggets Burger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>666</th>\n",
              "      <td>Desserts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1070</th>\n",
              "      <td>Lipton Tea With Milk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1047</th>\n",
              "      <td>Dynamic Mix</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Write the resulting dataframe back to a text file\n",
        "df.to_csv('100menu.txt', index=False, header=False)"
      ],
      "metadata": {
        "id": "Oo9B2WmLosYs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g58wqKNG-YNi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the text file into a dataframe\n",
        "dffood = pd.read_csv('/content/foods.txt', header=None, names=['text'])\n",
        "dfdrink = pd.read_csv('/content/drinks.txt', header=None, names=['text'])\n",
        "dfextras = pd.read_csv('/content/extras.txt', header=None, names=['text'])\n",
        "dfsize = pd.read_csv('/content/size.txt', header=None, names=['text'])\n",
        "dfnumber = pd.read_csv('/content/numbers.txt', header=None, names=['text'])"
      ],
      "metadata": {
        "id": "W78AdR_U-YJS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dffood.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6S3itTcf-YF2",
        "outputId": "c5e6fcff-1e0f-4b9f-f625-d9dccbd53d73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 179 entries, 0 to 178\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   text    179 non-null    object\n",
            "dtypes: object(1)\n",
            "memory usage: 1.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "food= dffood.sample().text.values[0]\n",
        "food"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "efXwnpEZ-Xfs",
        "outputId": "d430f432-705a-4b87-d0a8-41776fb41e02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Mix Burger Sandwich'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drink= dfdrink.sample().text.values[0]\n",
        "drink"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "aN5LYYdZBAm5",
        "outputId": "79a6905f-a3f0-4f1b-d587-3146873a9ff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Pineapple Juice'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "extras= dfextras.sample().text.values[0]\n",
        "extras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Bi2Y2andA_fh",
        "outputId": "6ef11a30-81bc-428c-e889-83da9bb06f93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Desserts'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "size= dfsize.sample().text.values[0]\n",
        "size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "BEDwZQ5zA_Ir",
        "outputId": "3b232879-38be-416f-8c8a-3d21a4c91579"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'small'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "number= dfnumber.sample().text.values[0]\n",
        "number"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "lbRLR81WA-7o",
        "outputId": "60188a94-1d93-4300-f46b-ccac40254f89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'eleven'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "# Let's say you have a list of items\n",
        "entrance = ['I want to order', 'send me', 'I would like', 'can we get']\n",
        "adress = ['my home','my work place','my home two']\n",
        "payy = ['cash', 'with credit card', 'with debit','with card']\n",
        "df = pd.DataFrame()\n",
        "df['text']= []"
      ],
      "metadata": {
        "id": "FBCxnVvCIMqU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame()\n",
        "sentences=[]"
      ],
      "metadata": {
        "id": "yTmnrT7OU7Ci"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def foodordergenerator():\n",
        "    # To pick an item randomly, you can use the choice() function\n",
        "    entr = random.choice(entrance)\n",
        "    adres= random.choice(adress)\n",
        "    pay= random.choice(payy)\n",
        "\n",
        "    number= dfnumber.sample().text.values[0]\n",
        "    size= dfsize.sample().text.values[0]\n",
        "    extras= dfextras.sample().text.values[0]\n",
        "    drink= dfdrink.sample().text.values[0]\n",
        "    food= dffood.sample().text.values[0]\n",
        "    try: \n",
        "        # Combine the values from the rows into a single string\n",
        "         sentence = ' '.join([entr, number, food, 'with', extras, 'and', number, size, drink, 'send it to', adres, 'I will pay with', pay])\n",
        "    except TypeError:\n",
        "         sentence =''\n",
        "    return sentence"
      ],
      "metadata": {
        "id": "fmdWWMD3BVoD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "foodordergenerator()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "nI04pRamXXL2",
        "outputId": "c8a2b50f-f4a7-40b6-d386-cca15beb7b07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'send me two Served with french fries. with Mango Pieces and two small Mango Bottle send it to my work place I will pay with cash'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for _ in range (1000):\n",
        "      a = foodordergenerator()\n",
        "      sentences.append(a)"
      ],
      "metadata": {
        "id": "WoQM0UdsXY0E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loop through the sentences\n",
        "for sentence in sentences:\n",
        "    # Add the sentence as a row to the DataFrame, and assign a value to the 'label' column\n",
        "    df = df.append({'text': sentence},ignore_index=True)"
      ],
      "metadata": {
        "id": "ZHFSfOe8W8Mn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4jQVp7B8K9YA",
        "outputId": "d1f30c44-42b9-448d-eb96-607d2c18e4da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   text\n",
              "0     send me two Dynamite Chicken French Fries with...\n",
              "1     I want to order three Onion Rings Plate with M...\n",
              "2     I would like nine Net Potato Plate with Strawb...\n",
              "3     I want to order six Calzone Combo with Fruit S...\n",
              "4     I want to order ten Egg Wrap Sandwich with Ext...\n",
              "...                                                 ...\n",
              "1607  I would like one Pineapple Chicken Burger with...\n",
              "1608  I would like four Fish Fillet Sandwich with Mi...\n",
              "1609  I want to order twelve Shish Tawook Sandwich C...\n",
              "1610  can we get fifteen Spicy Club Sandwich with Ma...\n",
              "1611  send me twelve Falafel Paratha Sandwich with T...\n",
              "\n",
              "[1612 rows x 1 columns]"
            ],
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c344f295-57b0-43c5-9243-a81d1f85155a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c344f295-57b0-43c5-9243-a81d1f85155a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "aWC4IOB-BVWv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u7RRI9x4GSpK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FYyiyVKpDcnB",
        "outputId": "1ddd9e84-e672-41f8-f7fa-30319363e353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1612 entries, 0 to 1611\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   text    1612 non-null   object\n",
            "dtypes: object(1)\n",
            "memory usage: 25.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('foodgen.txt', index=False, header=False)"
      ],
      "metadata": {
        "id": "U5x4Acgxc6Xq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# chatgbt bot"
      ],
      "metadata": {
        "id": "ecwGCV_tokic"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install selenium"
      ],
      "metadata": {
        "id": "NjnFZRIgdWYR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install webdrivermanager"
      ],
      "metadata": {
        "id": "JsdEBOXAeMe8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from selenium import webdriver\n",
        "\n",
        "PATH = r\"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe\"\n",
        "driver = webdriver.Chrome(executable_path=PATH)\n",
        "\n",
        "#driver = webdriver.Chrome()\n",
        "\n",
        "driver.get('https://youtube.com')\n",
        "\n"
      ],
      "metadata": {
        "id": "ag0ziirrHvya"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# first import the module\n",
        "import webbrowser\n",
        "import time\n",
        "# import web\n",
        "from selenium.webdriver.common.by import By\n",
        "from selenium.webdriver.chrome.service import Service\n",
        "from selenium import webdriver\n",
        "from selenium.webdriver.common.keys import Keys\n",
        "\n",
        "# then make a url variable\n",
        "#url = \"https://chat.openai.com/chat/\"\n",
        "url = \"https://google.com/\"\n",
        "# getting path\n",
        "chrome_path = r\"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe\"\n",
        "\n",
        "# First registers the new browser\n",
        "webbrowser.register('chrome', None,\n",
        "\t\t\t\t\twebbrowser.BackgroundBrowser(chrome_path))\n",
        "\n",
        "# after registering we can open it by getting its code.\n",
        "webbrowser.get('chrome').open(url)\n",
        "'''\n",
        "driver = webdriver.Chrome(chrome_path)\n",
        "\n",
        "time.sleep(3)\n",
        "# input\n",
        "\n",
        "text_input = driver.find_element(by='xpath',\n",
        "                                 value='//*[@id=\"__next\"]/div/div/main/div[2]/form/div/div[2]/textarea')\n",
        "text_input.send_keys(\"Write your text here\")\n",
        "\n",
        "submit_button = driver.find_element(by='xpath', value='//*[@id=\"__next\"]/div/div/main/div[2]/form/div/div[2]/button/svg')\n",
        "submit_button.click()\n",
        "\n",
        "time.sleep(1)\n",
        "driver.quit()\n",
        "'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "aPherP_-1iMV",
        "outputId": "e13d95b2-85ac-424d-f435-b7f89ae484d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\ndriver = webdriver.Chrome(chrome_path)\\n\\ntime.sleep(3)\\n# input\\n\\ntext_input = driver.find_element(by=\\'xpath\\',\\n                                 value=\\'//*[@id=\"__next\"]/div/div/main/div[2]/form/div/div[2]/textarea\\')\\ntext_input.send_keys(\"Write your text here\")\\n\\nsubmit_button = driver.find_element(by=\\'xpath\\', value=\\'//*[@id=\"__next\"]/div/div/main/div[2]/form/div/div[2]/button/svg\\')\\nsubmit_button.click()\\n\\ntime.sleep(1)\\ndriver.quit()\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "def send_to_chatgpt(sentence):\n",
        "    # Set the URL for the chatgpt endpoint\n",
        "    url = \"https://chat.openai.com/chat\"\n",
        "\n",
        "    input= //*[@id=\"__next\"]/div/div/main/div[2]/form/div/div[2]/textarea\n",
        "    \n",
        "    # Set the payload for the request\n",
        "    payload = {'text': sentence}\n",
        "    \n",
        "    # Send the request\n",
        "    response = requests.post(url, json=payload)\n",
        "    \n",
        "    # Print the response from chatgpt\n",
        "    print(response.text)\n"
      ],
      "metadata": {
        "id": "kK92_wHcqEeo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "# Open the text file\n",
        "with open('/content/foodorderamazon.txt', 'r') as file:\n",
        "    # Read the lines of the file into a list\n",
        "    lines = file.readlines()\n",
        "\n",
        "# Extract a single sentence from the list\n",
        "sentence = lines[0]\n",
        "\n",
        "while True:\n",
        "    # Send the sentence to chatgpt\n",
        "    send_to_chatgpt(sentence)\n",
        "    \n",
        "    # Sleep for one minute\n",
        "    time.sleep(60)\n"
      ],
      "metadata": {
        "id": "5a0LwopDqA7b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vocqLKoCqAtX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.read_json('/content/dev.json', lines=True)"
      ],
      "metadata": {
        "id": "f3mC2RFjsxif"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "id": "K4NQ8shusxY9",
        "outputId": "e49872a9-9e5c-45f3-b72d-e0afd3c00622"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  SRC  \\\n",
              "0   i would like a vegan burger with lettuce tomat...   \n",
              "1                       i would also like an iced tea   \n",
              "2   i'll have a cheeseburger with lettuce large fr...   \n",
              "3   i'd like a double cheeseburger with onions pic...   \n",
              "4   for my drink i'd like an extra large sugar fre...   \n",
              "5   hi can i have the double cheeseburger with ket...   \n",
              "6                  can i also have a medium diet coke   \n",
              "7   can i have a chicken sandwich with lettuce and...   \n",
              "8   hi could i please have a cheeseburger with che...   \n",
              "9   i would also like a small garlic fry and a med...   \n",
              "10  today i'd like to try your double cheese burge...   \n",
              "11  to drink i'd like a root beer and for my side ...   \n",
              "12  we also need a small fry a side of apple slice...   \n",
              "13  i would like two cheeseburgers with lettuce pi...   \n",
              "14  i would also like a large sugar free lemonade ...   \n",
              "15  hi i want a vegan burger with extra onions let...   \n",
              "16  i'd also like a side of baby carrots and a sug...   \n",
              "17  can i get a double cheese burger with onions c...   \n",
              "18                  a large side of fries with a coke   \n",
              "19  and two plain chicken sandwiches with iced tea...   \n",
              "\n",
              "                                                  EXR  \n",
              "0   (MAIN_DISH_ORDER (NUMBER 1 ) (MAIN_DISH_TYPE v...  \n",
              "1   (DRINK_ORDER (NUMBER 1 ) (DRINK_TYPE iced_tea ) )  \n",
              "2   (MAIN_DISH_ORDER (NUMBER 1 ) (MAIN_DISH_TYPE c...  \n",
              "3   (MAIN_DISH_ORDER (NUMBER 1 ) (MAIN_DISH_TYPE d...  \n",
              "4   (DRINK_ORDER (NUMBER 1 ) (SIZE extra_large ) (...  \n",
              "5   (MAIN_DISH_ORDER (NUMBER 1 ) (MAIN_DISH_TYPE d...  \n",
              "6   (DRINK_ORDER (NUMBER 1 ) (SIZE medium ) (DRINK...  \n",
              "7   (MAIN_DISH_ORDER (NUMBER 1 ) (MAIN_DISH_TYPE c...  \n",
              "8   (MAIN_DISH_ORDER (NUMBER 1 ) (MAIN_DISH_TYPE c...  \n",
              "9   (SIDE_ORDER (NUMBER 1 ) (SIZE small ) (SIDE_TY...  \n",
              "10  (MAIN_DISH_ORDER (NUMBER 1 ) (MAIN_DISH_TYPE d...  \n",
              "11  (DRINK_ORDER (NUMBER 1 ) (DRINK_TYPE root_beer...  \n",
              "12  (SIDE_ORDER (NUMBER 1 ) (SIZE small ) (SIDE_TY...  \n",
              "13  (MAIN_DISH_ORDER (NUMBER 2 ) (MAIN_DISH_TYPE c...  \n",
              "14  (DRINK_ORDER (NUMBER 1 ) (SIZE large ) (DRINK_...  \n",
              "15  (MAIN_DISH_ORDER (NUMBER 1 ) (MAIN_DISH_TYPE v...  \n",
              "16  (SIDE_ORDER (NUMBER 1 ) (SIDE_TYPE baby_carrot...  \n",
              "17  (MAIN_DISH_ORDER (NUMBER 1 ) (MAIN_DISH_TYPE d...  \n",
              "18  (SIDE_ORDER (NUMBER 1 ) (SIZE large ) (SIDE_TY...  \n",
              "19  (MAIN_DISH_ORDER (NUMBER 2 ) (MAIN_DISH_TYPE c...  "
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>i would like a vegan burger with lettuce tomat...</td>\n",
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              "      <td>can i get a double cheese burger with onions c...</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d05f367-112c-4321-ae0c-a322d1232944')\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3d05f367-112c-4321-ae0c-a322d1232944');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sw63I-L_La-S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for row in df3['SRC']:\n",
        "    with open('foodorderamazon.txt', 'a') as f:\n",
        "        f.write(row + '\\n')"
      ],
      "metadata": {
        "id": "XRWRqB56Lawt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1AuJGhGO16m-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.read_json('/content/train.json', lines=True)"
      ],
      "metadata": {
        "id": "LMxBfuR-17NY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.sample(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "outputId": "4e5f61a0-c072-485c-9c93-c81bc529f7cc",
        "id": "r9yqHj2g17NZ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    SRC  \\\n",
              "7225  can you please order for me two salads with ca...   \n",
              "2195    i feel like getting just one tofu bowl not rice   \n",
              "7215    i need to order four tacos with sofritas please   \n",
              "3220  i need to put in an order for just one carnita...   \n",
              "4272  order for me two steak salads with cheese and ...   \n",
              "2499  put an order for me for a barbacoa bowl with g...   \n",
              "5393  i would prefer just one carnitas burrito with ...   \n",
              "7060  could i have fifteen side of queso and guacamo...   \n",
              "4045  can you please get for me three chicken burrit...   \n",
              "1356  i'll be going for one carnitas burrito with li...   \n",
              "2360  i would like to order a pork burrito without a...   \n",
              "7762       three salads with veggie and spicy red salsa   \n",
              "446   get for me four steak burritos with black bean...   \n",
              "7757             four bowls with pork and pico de gallo   \n",
              "9059  i'm going to have thirteen pork quesadillas an...   \n",
              "3133  i need to order fifteen chicken burrito bowls ...   \n",
              "8206  i'm going to go for just one carnitas burrito ...   \n",
              "9263                          i want four side of chips   \n",
              "287   i'm gonna try four burritos with steak brown r...   \n",
              "8003  place an order for me for eleven quesadillas w...   \n",
              "\n",
              "                                               TOPALIAS  \n",
              "7225  (SALAD_ORDER (NUMBER two ) (MAIN_FILLING carni...  \n",
              "2195  (BURRITO_BOWL_ORDER (NUMBER just one ) (MAIN_F...  \n",
              "7215  (TACO_ORDER (NUMBER four ) (MAIN_FILLING sofri...  \n",
              "3220  (BURRITO_ORDER (NUMBER just one ) (MAIN_FILLIN...  \n",
              "4272  (SALAD_ORDER (NUMBER two ) (MAIN_FILLING steak...  \n",
              "2499  (BURRITO_BOWL_ORDER (NUMBER a ) (MAIN_FILLING ...  \n",
              "5393  (BURRITO_ORDER (NUMBER just one ) (MAIN_FILLIN...  \n",
              "7060  (SIDE_ORDER (NUMBER fifteen ) (SIDE_TYPE queso...  \n",
              "4045  (BURRITO_BOWL_ORDER (NUMBER three ) (MAIN_FILL...  \n",
              "1356  (BURRITO_ORDER (NUMBER one ) (MAIN_FILLING car...  \n",
              "2360  (BURRITO_ORDER (NUMBER a ) (MAIN_FILLING pork ...  \n",
              "7762  (SALAD_ORDER (NUMBER three ) (MAIN_FILLING veg...  \n",
              "446   (BURRITO_ORDER (NUMBER four ) (MAIN_FILLING st...  \n",
              "7757  (BURRITO_BOWL_ORDER (NUMBER four ) (MAIN_FILLI...  \n",
              "9059  (QUESADILLA_ORDER (NUMBER thirteen ) (MAIN_FIL...  \n",
              "3133  (BURRITO_BOWL_ORDER (NUMBER fifteen ) (MAIN_FI...  \n",
              "8206  (BURRITO_BOWL_ORDER (NUMBER just one ) (MAIN_F...  \n",
              "9263    (SIDE_ORDER (NUMBER four ) (SIDE_TYPE chips ) )  \n",
              "287   (BURRITO_ORDER (NUMBER four ) (MAIN_FILLING st...  \n",
              "8003  (QUESADILLA_ORDER (NUMBER eleven ) (TOPPING ve...  "
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              "      <th>SRC</th>\n",
              "      <th>TOPALIAS</th>\n",
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              "      <th>7225</th>\n",
              "      <td>can you please order for me two salads with ca...</td>\n",
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              "      <th>7215</th>\n",
              "      <td>i need to order four tacos with sofritas please</td>\n",
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              "      <td>i need to put in an order for just one carnita...</td>\n",
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              "      <td>order for me two steak salads with cheese and ...</td>\n",
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              "      <td>i would prefer just one carnitas burrito with ...</td>\n",
              "      <td>(BURRITO_ORDER (NUMBER just one ) (MAIN_FILLIN...</td>\n",
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              "      <th>7060</th>\n",
              "      <td>could i have fifteen side of queso and guacamo...</td>\n",
              "      <td>(SIDE_ORDER (NUMBER fifteen ) (SIDE_TYPE queso...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4045</th>\n",
              "      <td>can you please get for me three chicken burrit...</td>\n",
              "      <td>(BURRITO_BOWL_ORDER (NUMBER three ) (MAIN_FILL...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1356</th>\n",
              "      <td>i'll be going for one carnitas burrito with li...</td>\n",
              "      <td>(BURRITO_ORDER (NUMBER one ) (MAIN_FILLING car...</td>\n",
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              "    <tr>\n",
              "      <th>2360</th>\n",
              "      <td>i would like to order a pork burrito without a...</td>\n",
              "      <td>(BURRITO_ORDER (NUMBER a ) (MAIN_FILLING pork ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7762</th>\n",
              "      <td>three salads with veggie and spicy red salsa</td>\n",
              "      <td>(SALAD_ORDER (NUMBER three ) (MAIN_FILLING veg...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>446</th>\n",
              "      <td>get for me four steak burritos with black bean...</td>\n",
              "      <td>(BURRITO_ORDER (NUMBER four ) (MAIN_FILLING st...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7757</th>\n",
              "      <td>four bowls with pork and pico de gallo</td>\n",
              "      <td>(BURRITO_BOWL_ORDER (NUMBER four ) (MAIN_FILLI...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9059</th>\n",
              "      <td>i'm going to have thirteen pork quesadillas an...</td>\n",
              "      <td>(QUESADILLA_ORDER (NUMBER thirteen ) (MAIN_FIL...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3133</th>\n",
              "      <td>i need to order fifteen chicken burrito bowls ...</td>\n",
              "      <td>(BURRITO_BOWL_ORDER (NUMBER fifteen ) (MAIN_FI...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8206</th>\n",
              "      <td>i'm going to go for just one carnitas burrito ...</td>\n",
              "      <td>(BURRITO_BOWL_ORDER (NUMBER just one ) (MAIN_F...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9263</th>\n",
              "      <td>i want four side of chips</td>\n",
              "      <td>(SIDE_ORDER (NUMBER four ) (SIDE_TYPE chips ) )</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>i'm gonna try four burritos with steak brown r...</td>\n",
              "      <td>(BURRITO_ORDER (NUMBER four ) (MAIN_FILLING st...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8003</th>\n",
              "      <td>place an order for me for eleven quesadillas w...</td>\n",
              "      <td>(QUESADILLA_ORDER (NUMBER eleven ) (TOPPING ve...</td>\n",
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oNmmYxpK16gk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#food order "
      ],
      "metadata": {
        "id": "XH3J2WkEoa2A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "-uaJmvEEjVPp"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_rows', None)\n",
        "pd.set_option('display.max_columns', None)"
      ],
      "metadata": {
        "id": "f_DZaft1bYDV"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "i2XKtOmFKROZ"
      },
      "outputs": [],
      "source": [
        "\n",
        "# read the CoNLL file into a list of lines\n",
        "with open('/content/foodorder.txt', 'r') as f:\n",
        "    lines = f.readlines()[1:]\n",
        "\n",
        "# initialize an empty list to store the rows\n",
        "rows = []\n",
        "\n",
        "# initialize the sentence ID counter\n",
        "sentence_id = 0\n",
        "\n",
        "# iterate over the lines and split each line into fields\n",
        "for line in lines:\n",
        "    fields = line.strip().split()\n",
        "    if fields:  # line is not empty\n",
        "        rows.append([sentence_id] + fields)\n",
        "    else:  # line is empty\n",
        "        sentence_id += 1\n",
        "\n",
        "# create a Pandas dataframe from the rows\n",
        "df0 = pd.DataFrame(rows, columns=['sentence_id', 'words', 'col2', 'col3','labels','col5', 'col4'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "aZj3cm_5L536"
      },
      "outputs": [],
      "source": [
        "df=df0[['sentence_id','words','labels']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "DEL-_I-SJPqB",
        "outputId": "3b1a5c2a-66a9-4edd-e1a7-18631dfd7c4d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sentence_id    words              labels\n",
              "17267          700   mantis              B-food\n",
              "17268          700      and                   O\n",
              "17269          700      six    B-drink_quantity\n",
              "17270          700    ayran             B-drink\n",
              "17271          700       to                   O\n",
              "17272          700       my                   O\n",
              "17273          700   workie  B-delivery_address\n",
              "17274          701        I                   O\n",
              "17275          701     like                   O\n",
              "17276          701       to                   O\n",
              "17277          701    order                   O\n",
              "17278          701    three     B-food_quantity\n",
              "17279          701    large         B-food_size\n",
              "17280          701  falafel              B-food\n",
              "17281          701      and                   O\n",
              "17282          701     four    B-drink_quantity\n",
              "17283          701    cokes             B-drink\n",
              "17284          701       to                   O\n",
              "17285          701       my                   O\n",
              "17286          701     home  B-delivery_address"
            ],
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              "      <th>17274</th>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.tail(20)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "on6uygVZU8Cf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5MM8GU_JPfq",
        "outputId": "91709e6e-84e8-443c-aead-80d28c59ca84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17287 entries, 0 to 17286\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   sentence_id  17287 non-null  int64 \n",
            " 1   words        17287 non-null  object\n",
            " 2   labels       17285 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 405.3+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "OHV2fASRdnxt"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5vA0hhS5RVI",
        "outputId": "5ad9645a-19da-4e90-caeb-1e1ecf0de1c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df['labels'].isna().values.any()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['labels'].value_counts()"
      ],
      "metadata": {
        "id": "vDzXZvWw9lGG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3c35c94-00a2-45c5-ab23-1ee512fb69bf"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "O                     8918\n",
              "I-food_ingredients    2512\n",
              "B-food_ingredients    1308\n",
              "B-food                 798\n",
              "B-food_quantity        734\n",
              "I-food                 665\n",
              "B-delivery_address     416\n",
              "B-payment              380\n",
              "B-drink                307\n",
              "B-drink_size           299\n",
              "I-drink                286\n",
              "B-drink_quantity       259\n",
              "I-delivery_address     136\n",
              "B-food_size            130\n",
              "I-payment              121\n",
              "_                        4\n",
              "I-drink_size             3\n",
              "I-food_size              2\n",
              "I                        2\n",
              "-X-                      2\n",
              "I-ing                    2\n",
              "B                        1\n",
              "Name: labels, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['labels']=='_') | (df['labels']=='B') | (df['labels']=='I') | (df['labels']=='-X-') | (df['labels']=='I-ing')]"
      ],
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        "outputId": "f16e0355-1ced-4f44-bb11-ca5a0a8f5c79"
      },
      "execution_count": 10,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "       sentence_id    words labels\n",
              "3568           152        '      _\n",
              "3827           164     mild      B\n",
              "4136           180    beans      I\n",
              "5371           243    salsa    -X-\n",
              "5372           243       on      _\n",
              "5389           244     pico    -X-\n",
              "5390           244       on      _\n",
              "8003           342        '      _\n",
              "9911           396  veggies  I-ing\n",
              "10552          420     bowl      I\n",
              "10822          432  lettuce  I-ing"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
      "cell_type": "code",
      "source": [
        "index = df.loc[(df['labels'] == '_') | (df['labels'] == 'B') | (df['labels'] == 'I') | (df['labels'] == '-X-') | (df['labels'] == 'I-ing')].index"
      ],
      "metadata": {
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      "outputs": []
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          "data": {
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              "Int64Index([3568, 3827, 4136, 5371, 5372, 5389, 5390, 8003, 9911, 10552,\n",
              "            10822],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
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    {
      "cell_type": "code",
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        "df = df.drop(index)"
      ],
      "metadata": {
        "id": "71P7rQLJe9i4"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['labels'].value_counts()"
      ],
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        "colab": {
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        },
        "id": "tJFO5K9DhaB7",
        "outputId": "0450df05-5094-4de4-80c3-04c42a984393"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "O                     8918\n",
              "I-food_ingredients    2512\n",
              "B-food_ingredients    1308\n",
              "B-food                 798\n",
              "B-food_quantity        734\n",
              "I-food                 665\n",
              "B-delivery_address     416\n",
              "B-payment              380\n",
              "B-drink                307\n",
              "B-drink_size           299\n",
              "I-drink                286\n",
              "B-drink_quantity       259\n",
              "I-delivery_address     136\n",
              "B-food_size            130\n",
              "I-payment              121\n",
              "I-drink_size             3\n",
              "I-food_size              2\n",
              "Name: labels, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['labels']=='B-ingredients'].words.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUh2Xwznb69H",
        "outputId": "e8d9c977-a5df-4938-d869-ea753b0e8323"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: words, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['labels']=='B-DRINK')]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "7o9sLndZFwkG",
        "outputId": "55b8fdf7-5ea7-41ce-fe40-80457c97f67b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [sentence_id, words, labels]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3e01419-377e-459c-bd00-2790b33c5705\">\n",
              "    <div 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>sentence_id</th>\n",
              "      <th>words</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3e01419-377e-459c-bd00-2790b33c5705')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f3e01419-377e-459c-bd00-2790b33c5705 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f3e01419-377e-459c-bd00-2790b33c5705');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['labels']=='B-drink'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlGY2lnjGN3n",
        "outputId": "88105b09-1fad-491a-de5f-64479b13a44a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "17198    False\n",
              "17199     True\n",
              "17200    False\n",
              "17201    False\n",
              "17202    False\n",
              "Name: labels, Length: 17190, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a boolean mask indicating which rows have null values\n",
        "mask = df.isnull().any(axis=1)\n",
        "\n",
        "# Use the boolean mask to index the dataframe and select the rows with null values\n",
        "null_rows = df[mask]\n",
        "\n",
        "# Print the index labels of the null rows\n",
        "print(null_rows.index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oT1NUVSa9lAP",
        "outputId": "b750036c-4de1-4882-b417-cec0af784870"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Int64Index([], dtype='int64')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head (20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "z6CKFYbg9k2q",
        "outputId": "e7b55114-beec-466b-d5bd-7299e771a42e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    sentence_id     words              labels\n",
              "0             0         I                   O\n",
              "1             0     would                   O\n",
              "2             0      like                   O\n",
              "3             0        to                   O\n",
              "4             0     order                   O\n",
              "5             0     three     B-food_quantity\n",
              "6             0     large         B-food_size\n",
              "7             0    veggie              B-food\n",
              "8             0      subs              I-food\n",
              "9             0      with                   O\n",
              "10            0     extra  B-food_ingredients\n",
              "11            0   avocado  I-food_ingredients\n",
              "12            0       and                   O\n",
              "13            0         a                   O\n",
              "14            0     large        B-drink_size\n",
              "15            0  six-pack    B-drink_quantity\n",
              "16            0        of                   O\n",
              "17            0     water             B-drink\n",
              "18            0         I                   O\n",
              "19            0      will                   O"
            ],
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              "      <th>14</th>\n",
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              "      <td>B-drink_size</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>six-pack</td>\n",
              "      <td>B-drink_quantity</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c5c975dc-22ea-4d26-8900-d6be1b3d9272 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c5c975dc-22ea-4d26-8900-d6be1b3d9272');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find which cells in the 'col' column are null\n",
        "null_cells = df['labels'].isnull()\n",
        "\n",
        "# Use the 'loc' indexer to select the rows that have null cells in the 'col' column\n",
        "null_rows = df.loc[null_cells]\n",
        "null_rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "qWM7Tq-a68X6",
        "outputId": "de08688c-e31d-4cc1-fbe6-8a67c69fba35"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [sentence_id, words, labels]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c6f1f08b-b7dd-423d-b3f1-dddceba07f94\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>sentence_id</th>\n",
              "      <th>words</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6f1f08b-b7dd-423d-b3f1-dddceba07f94')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  "
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          "metadata": {},
          "execution_count": 18
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        "df.info()"
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      "execution_count": 19,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 17274 entries, 0 to 17286\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   sentence_id  17274 non-null  int64 \n",
            " 1   words        17274 non-null  object\n",
            " 2   labels       17274 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 539.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "O9fTZggs6D-5"
      },
      "outputs": [],
      "source": [
        "df[\"labels\"] = df[\"labels\"].str.upper()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "e0BbNZuZ6W58"
      },
      "outputs": [],
      "source": [
        "df[\"words\"] = df[\"words\"].str.lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
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        "colab": {
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        "id": "yjK22X4O6HIE",
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              "       sentence_id      words              labels\n",
              "2227            85     veggie  B-FOOD_INGREDIENTS\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df.sample(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "3mu0Pav4J2Fs"
      },
      "outputs": [],
      "source": [
        "## shuffle if needed\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# extract the unique sentence IDs from the dataframe\n",
        "sentence_ids = df['sentence_id'].unique()\n",
        "\n",
        "# shuffle the sentence IDs randomly\n",
        "np.random.shuffle(sentence_ids)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "J5UWHAIIpIgv"
      },
      "outputs": [],
      "source": [
        "# split the list of sentence IDs into two lists, according to the desired split ratio\n",
        "split_index = int(len(sentence_ids) * 0.8)  # 80% for the training set\n",
        "train_ids = sentence_ids[:split_index]\n",
        "test_ids = sentence_ids[split_index:]\n",
        "\n",
        "# use the lists of sentence IDs to filter the original dataframe and create the train and test sets\n",
        "train_df = df[df['sentence_id'].isin(train_ids)]\n",
        "test_df = df[df['sentence_id'].isin(test_ids)]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ch5B_HgNgKzl",
        "outputId": "3d1ca50d-dbb7-4f5e-a909-8ff50c7664db"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 13830 entries, 31 to 17273\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   sentence_id  13830 non-null  int64 \n",
            " 1   words        13830 non-null  object\n",
            " 2   labels       13830 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 432.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['labels'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uxotejCYhGtX",
        "outputId": "4bfcbf1a-abbc-47b5-edea-1bac14b5719c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "O                     7140\n",
              "I-FOOD_INGREDIENTS    1970\n",
              "B-FOOD_INGREDIENTS    1041\n",
              "B-FOOD                 639\n",
              "B-FOOD_QUANTITY        583\n",
              "I-FOOD                 541\n",
              "B-DELIVERY_ADDRESS     338\n",
              "B-PAYMENT              309\n",
              "B-DRINK                250\n",
              "B-DRINK_SIZE           245\n",
              "I-DRINK                241\n",
              "B-DRINK_QUANTITY       210\n",
              "I-DELIVERY_ADDRESS     117\n",
              "B-FOOD_SIZE            104\n",
              "I-PAYMENT               99\n",
              "I-DRINK_SIZE             2\n",
              "I-FOOD_SIZE              1\n",
              "Name: labels, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "U_NWWusN5l6w"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "i2gYads75x6A"
      },
      "outputs": [],
      "source": [
        "df[\"sentence_id\"] = LabelEncoder().fit_transform(df[\"sentence_id\"] )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "1HTenUIq56Pk",
        "outputId": "bf783e08-d6f8-474a-de08-55af7ff9af7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    sentence_id     words              labels\n",
              "0             0         i                   O\n",
              "1             0     would                   O\n",
              "2             0      like                   O\n",
              "3             0        to                   O\n",
              "4             0     order                   O\n",
              "5             0     three     B-FOOD_QUANTITY\n",
              "6             0     large         B-FOOD_SIZE\n",
              "7             0    veggie              B-FOOD\n",
              "8             0      subs              I-FOOD\n",
              "9             0      with                   O\n",
              "10            0     extra  B-FOOD_INGREDIENTS\n",
              "11            0   avocado  I-FOOD_INGREDIENTS\n",
              "12            0       and                   O\n",
              "13            0         a                   O\n",
              "14            0     large        B-DRINK_SIZE\n",
              "15            0  six-pack    B-DRINK_QUANTITY\n",
              "16            0        of                   O\n",
              "17            0     water             B-DRINK\n",
              "18            0         i                   O\n",
              "19            0      will                   O"
            ],
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              "      <td>0</td>\n",
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              "      <td>large</td>\n",
              "      <td>B-DRINK_SIZE</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>six-pack</td>\n",
              "      <td>B-DRINK_QUANTITY</td>\n",
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              "      <th>16</th>\n",
              "      <td>0</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9da742c4-203b-4217-b971-b10e17621c62');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df.head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "pmIJWwyMI-lc",
        "outputId": "338d5427-4bb5-492b-99ec-80b3d025654e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sentence_id   words              labels\n",
              "17241          698    send                   O\n",
              "17242          698      me                   O\n",
              "17243          698       a     B-FOOD_QUANTITY\n",
              "17244          698   manti              B-FOOD\n",
              "17245          698     and                   O\n",
              "17246          698      an    B-DRINK_QUANTITY\n",
              "17247          698   ayran             B-DRINK\n",
              "17248          698      to                   O\n",
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              "17264          700    send                   O\n",
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              "17266          700   three     B-FOOD_QUANTITY\n",
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              "17268          700     and                   O\n",
              "17269          700     six    B-DRINK_QUANTITY\n",
              "17270          700   ayran             B-DRINK\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8b51b4b7-18f8-49f4-a7b6-f2bbb5521d37 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8b51b4b7-18f8-49f4-a7b6-f2bbb5521d37');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "train_df.tail(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ENuQ_jAZ5iMC"
      },
      "source": [
        "# train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jeYceicI6ql0",
        "outputId": "4b9b5644-e2b8-4fc1-a36d-708c512ed483"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting simpletransformers\n",
            "  Downloading simpletransformers-0.63.9-py3-none-any.whl (250 kB)\n",
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            "Installing collected packages: tokenizers, sentencepiece, pathtools, appdirs, xxhash, watchdog, validators, smmap, setproctitle, sentry-sdk, semver, pympler, pygments, multidict, mdurl, frozenlist, docker-pycreds, dill, charset-normalizer, blinker, async-timeout, yarl, responses, pydeck, multiprocess, markdown-it-py, huggingface-hub, gitdb, aiosignal, transformers, seqeval, rich, GitPython, aiohttp, wandb, streamlit, datasets, simpletransformers\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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          ]
        }
      ],
      "source": [
        "pip install simpletransformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "F0IBUxON6IPI"
      },
      "outputs": [],
      "source": [
        "# Import necessary packages\n",
        "from sklearn.metrics import accuracy_score\n",
        "from simpletransformers.ner import NERModel, NERArgs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "vCUgV78f5YaU"
      },
      "outputs": [],
      "source": [
        "# Initializing NER model configurations\n",
        "label = df[\"labels\"].unique().tolist()\n",
        "args = NERArgs()\n",
        "args.classification_report = True\n",
        "args.num_train_epochs = 5\n",
        "args.learning_rate = 3e-5\n",
        "args.overwrite_output_dir =True\n",
        "args.train_batch_size = 4\n",
        "args.eval_batch_size = 8\n",
        "# for loss graph\n",
        "#args.logging_steps=100\n",
        "#args.evaluate_during_training=True   # hata veriyor\n",
        "args.evaluate_during_training_verbose = True\n",
        "#args.reprocess_input_data=True\n",
        "#args.overwrite_output_dir = True\n",
        "# if you use for grapph with tensorboard, after below:  tensorboard --logdir logs    and   pip install tensorflow\n",
        "#args.tensorboard_logdir = logs"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label"
      ],
      "metadata": {
        "id": "jHFyDbPyMWoQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dce517f4-5664-45cb-942e-f67f3fb79ee5"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O',\n",
              " 'B-FOOD_QUANTITY',\n",
              " 'B-FOOD_SIZE',\n",
              " 'B-FOOD',\n",
              " 'I-FOOD',\n",
              " 'B-FOOD_INGREDIENTS',\n",
              " 'I-FOOD_INGREDIENTS',\n",
              " 'B-DRINK_SIZE',\n",
              " 'B-DRINK_QUANTITY',\n",
              " 'B-DRINK',\n",
              " 'B-PAYMENT',\n",
              " 'I-PAYMENT',\n",
              " 'B-DELIVERY_ADDRESS',\n",
              " 'I-DRINK_SIZE',\n",
              " 'I-DRINK',\n",
              " 'I-FOOD_SIZE',\n",
              " 'I-DELIVERY_ADDRESS']"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForTokenClassification: ['lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight']\n",
            "- This IS expected if you are initializing RobertaForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a97610da7a7047ed87d75b411723d839"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d39275bb712b4a918e8aa4799e6c3bc2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "54499934f3184143b6aa599706b59214"
            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90cc2881397746ce98659f16a0d5fa08"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3cf65405a4954baa9efdc2d551b3e3ae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 5:   0%|          | 0/140 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9c632e604c2431d99b2ea051da7f896"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 1 of 5:   0%|          | 0/140 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "48bd2f7e80204f40a97ade2f64d35b54"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 2 of 5:   0%|          | 0/140 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91b9acb040824cc483c9fbb7766d2f14"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 3 of 5:   0%|          | 0/140 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba12b3c58f4c428eb4ecbdd22d37bb71"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 4 of 5:   0%|          | 0/140 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d068e149f9b643da8ca7aaac945367d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(700, 0.1980807541172752)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "# Train BERT based NER model\n",
        "model = NERModel(\"bert\", \"bert-base-uncased\", labels=label, args=args, use_cuda= True)\n",
        "model.train_model(train_df, eval_data=test_df, acc=accuracy_score)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "-gO98PMZ5vQi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "a16cfe67c51448bab79545388f58eaa1",
            "736db7a7e3a14a09ba27d06d28acf342",
            "2114a3f318c94f4998719f60d2b4b24d",
            "4b55f48022b843dc84403608a2c5c7f7",
            "5623488dab974553a43ff640a887dc12",
            "279653db800c45359f7b8ff8c2ce0e1b",
            "b0be708a113849558872be35946e8dae",
            "17db649a2e2a41e2b8afdc1549229043",
            "c23241e8daa8472f9fba5229551f82dc",
            "f7aa4200102a41cb88a678c2bbcb6579",
            "ff01dcccef584f8cba03b31d668efa0f",
            "a6d6cbf4c77d4af9b548263198d12417",
            "970329a3d27d48ceb21417f946fa7885",
            "d9ffa90ceb574e4fb9075d5bd993bb8d",
            "5069d1cfea6e4369bb505dcc79bf28c7",
            "c4a42b52809b4efdbb6bb920283a6487",
            "82b19f6dbb424589857292f640bf6cde",
            "fc432371a6dc4f5bb4fbd24c8ac5b672",
            "fa968dbaf6f8463786f08ff91d23dd5c",
            "aca726fcc74e46c9bedd8e69a0117ccb",
            "8a34e34dd7c44512a0dc26f48d6de132",
            "2a4867dbbddb499ea8c9abe41b441e11"
          ]
        },
        "outputId": "aeaa7886-a6d5-4f03-c8e6-e2430c1f6169"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a16cfe67c51448bab79545388f58eaa1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Evaluation:   0%|          | 0/18 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6d6cbf4c77d4af9b548263198d12417"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Evaluate the performance of NER model\n",
        "result, model_outputs, preds_list = model.eval_model(test_df)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "aIP56tKSnBKd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee110325-649b-46ca-ad8b-9bf29b56893d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.02323725293778504,\n",
              " 'precision': 0.9824380165289256,\n",
              " 'recall': 0.9885654885654885,\n",
              " 'f1_score': 0.9854922279792746}"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4F4OJg8-nEF-"
      },
      "outputs": [],
      "source": [
        "model_outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kz-em46enbiE"
      },
      "outputs": [],
      "source": [
        "preds_list"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hP5DC3fKhSMy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform NER for inference text\n",
        "inference_text = \"I would like to order a medium chicken Caesar salad with a side of chips with no onions and a bottle of orange juice I will pay with a debit card Please deliver it to my home\""
      ],
      "metadata": {
        "id": "BIPwdx1Vva8B"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inference_text = inference_text.lower()"
      ],
      "metadata": {
        "id": "UCqyFVozva8C"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inference_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "63e093cd-0644-4f23-d6f0-889cbbf16680",
        "id": "8k2LnoyOva8C"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'i would like to order a medium chicken caesar salad with a side of chips with no onions and a bottle of orange juice i will pay with a debit card please deliver it to my home'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "69a52b82d82542ab8a6776f1de829289",
            "1083c204da3043da9ce2f9692537d696",
            "5a4d4cc41de04cb9beb36753c18e67fe",
            "1c6debdf19e74280b0c2af5a2ebd0d09",
            "2fad2b54cefa44c4990d15793a2e6a16",
            "aa76b74665b74a9baa247dffc8c00359",
            "f194f1c807204312938fc1ef7cd3db28",
            "0062e5ad056d403d9070366c3df45157",
            "1a429b228bf74fc292dd4b59e4ab7fd4",
            "2f4eb3ffb20c49a0bef1fe2ce27f972e",
            "602e1394027f40638fe847b570302f78",
            "a5d8dd47a78743589393cb4f4eb6b76a",
            "288b8ff8d8b3419a9305c42e54e394a8",
            "cd8024adddb0476f891ac85f3ff29be3",
            "1c67e685d94546be907405cf14ae2edf",
            "d9365921f0b94c9dbbcc8c8b00bdd0a0",
            "ab5b88bb79ac443685aa48310bb03459",
            "c6ef2253ef524392a62a83ba2c645485",
            "d434bacface64c1798246bdc225793ce",
            "117283da89ee462ca13c5249b233a77b",
            "18ccd1f9003c486e851ee9d6bd036d23",
            "9ab784555bea4eafa89dfa552b1a25d3"
          ]
        },
        "outputId": "e6834fa9-83a1-4a64-c122-0163433c7cea",
        "id": "oSmHDNa4va8C"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "69a52b82d82542ab8a6776f1de829289"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Prediction:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5d8dd47a78743589393cb4f4eb6b76a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "prediction, model_output = model.predict([inference_text])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "886f94ad-435a-4e80-bbf0-a1ac64916480",
        "id": "i4EDe5jhva8D"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[{'i': 'O'}, {'would': 'O'}, {'like': 'O'}, {'to': 'O'}, {'order': 'O'}, {'a': 'B-FOOD_QUANTITY'}, {'medium': 'B-FOOD_SIZE'}, {'chicken': 'B-FOOD'}, {'caesar': 'I-FOOD'}, {'salad': 'I-FOOD'}, {'with': 'O'}, {'a': 'O'}, {'side': 'B-FOOD_INGREDIENTS'}, {'of': 'O'}, {'chips': 'I-FOOD_INGREDIENTS'}, {'with': 'O'}, {'no': 'B-FOOD_INGREDIENTS'}, {'onions': 'I-FOOD_INGREDIENTS'}, {'and': 'O'}, {'a': 'O'}, {'bottle': 'B-DRINK_SIZE'}, {'of': 'O'}, {'orange': 'B-DRINK'}, {'juice': 'I-DRINK'}, {'i': 'O'}, {'will': 'O'}, {'pay': 'O'}, {'with': 'O'}, {'a': 'O'}, {'debit': 'B-PAYMENT'}, {'card': 'I-PAYMENT'}, {'please': 'O'}, {'deliver': 'O'}, {'it': 'O'}, {'to': 'O'}, {'my': 'O'}, {'home': 'B-DELIVERY_ADDRESS'}]]\n"
          ]
        }
      ],
      "source": [
        "print(prediction)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wC85ANEdhSDQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-7iIKxG1ZeH3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "h8xG6LnTZeDm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vOpir8WSZd_z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G83tmSUz_iAV"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# model Load"
      ],
      "metadata": {
        "id": "LIHY2_8yd48I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install simpletransformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SYvJKoYvbyjJ",
        "outputId": "380e4598-6f6d-4521-f2cd-8e8021fc558b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting simpletransformers\n",
            "  Downloading simpletransformers-0.63.9-py3-none-any.whl (250 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.5/250.5 KB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting transformers>=4.6.0\n",
            "  Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m58.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from simpletransformers) (1.3.5)\n",
            "Requirement already satisfied: tqdm>=4.47.0 in /usr/local/lib/python3.8/dist-packages (from simpletransformers) (4.64.1)\n",
            "Collecting wandb>=0.10.32\n",
            "  Downloading wandb-0.13.7-py2.py3-none-any.whl (1.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m72.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting seqeval\n",
            "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 KB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m55.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from simpletransformers) (1.7.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from simpletransformers) (1.0.2)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.8/dist-packages (from simpletransformers) (2022.6.2)\n",
            "Requirement already satisfied: tensorboard in /usr/local/lib/python3.8/dist-packages (from simpletransformers) (2.9.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from simpletransformers) (1.21.6)\n",
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            "Installing collected packages: tokenizers, sentencepiece, pathtools, commonmark, xxhash, watchdog, validators, urllib3, smmap, shortuuid, setproctitle, semver, rich, pympler, multiprocess, docker-pycreds, blinker, sentry-sdk, pydeck, gitdb, seqeval, responses, huggingface-hub, GitPython, wandb, transformers, streamlit, datasets, simpletransformers\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "Successfully installed GitPython-3.1.30 blinker-1.5 commonmark-0.9.1 datasets-2.8.0 docker-pycreds-0.4.0 gitdb-4.0.10 huggingface-hub-0.11.1 multiprocess-0.70.14 pathtools-0.1.2 pydeck-0.8.0 pympler-1.0.1 responses-0.18.0 rich-13.0.1 semver-2.13.0 sentencepiece-0.1.97 sentry-sdk-1.12.1 seqeval-1.2.2 setproctitle-1.3.2 shortuuid-1.0.11 simpletransformers-0.63.9 smmap-5.0.0 streamlit-1.16.0 tokenizers-0.13.2 transformers-4.25.1 urllib3-1.26.13 validators-0.20.0 wandb-0.13.7 watchdog-2.2.1 xxhash-3.2.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from simpletransformers.ner import NERModel, NERArgs"
      ],
      "metadata": {
        "id": "lsF3dJlrk1Zl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "model = NERModel(\"bert\", \"/content/drive/MyDrive/NLP/foodorder1\",use_cuda=False)\n"
      ],
      "metadata": {
        "id": "sRKIhiBKZSgW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kr9zJjYV88cM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NVvHOOkuuXBn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform NER for inference text\n",
        "inference_text = \"send me two pizzas and a bottle of beer to my homie\""
      ],
      "metadata": {
        "id": "LxMSe3DEfG71"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inference_text = inference_text.lower()"
      ],
      "metadata": {
        "id": "6HT01GPtuX0d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inference_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "_fCv0Zhzu6qa",
        "outputId": "699c8605-7503-4dbc-fa79-b0d3422db1b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'send me two pizzas and a bottle of beer to my homie'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "e487a66ad11d48d4abe1920d1cbbea25",
            "500c1fd33ddc4504b0305ea8f5d6a154",
            "1368ef7fa933438da8fba50c6a5717c7",
            "6fb3c86d5b0e4954aa90c3913ed98952",
            "d149f68aabc340729b0a6fc7b4f5b4f2",
            "0dc949f772f44d4c8196f2fc294ec8ad",
            "a405445cde5e493692cd64a320424e5c",
            "4c740a436f414844a0cb8d85513f964e",
            "32b409a7fc4341898a4ffa36eb33e245",
            "65ad594afa094a4f878d2f048b45a620",
            "b56515bb70e04a4399fbf4f4f470c490",
            "c05dcfc5f9034167886b4ab00130422d",
            "f986e927c62b4b4a83c4b52d55933567",
            "8dfbec95655f46968c5ac532c46a6b1b",
            "50d0a5cba10547cc8eab9bc882a6c013",
            "f87c6ae231724d1b91ce54011d238990",
            "7464cfc3051c4ace83c8ea476d639ab2",
            "0ba4e4dd56b047989a9e57848005cc68",
            "8027cde3cd9f40f6bd28e77b1c7b27bd",
            "8d602d377a6f4b54aa492c54af2b0102",
            "633d51dda0434e67ac033cbcacd5559e",
            "92d382a9bbcb4f74898a93deee0464c3"
          ]
        },
        "id": "SueU_HbZfHdK",
        "outputId": "07d7918a-de76-4db4-c33c-43a51c2ff2e0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e487a66ad11d48d4abe1920d1cbbea25"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Prediction:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c05dcfc5f9034167886b4ab00130422d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "prediction, model_output = model.predict([inference_text])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jn-ZGM1DfHdK"
      },
      "outputs": [],
      "source": [
        "print(prediction)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TAktm75nZSad"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zKoEZvwtiZ-l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_xIRtBjJiZ3N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#gradio"
      ],
      "metadata": {
        "id": "kOGRTsc2ZyA6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BRz5_5Gacx4",
        "outputId": "1292d1d8-edea-4cd0-97a2-be71c8f02d6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: ffmpy, python-multipart\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4711 sha256=8b23c2ef15bc1d7d4c446ebfa12fe2741f06ad167c84b6b8fb2eb66ecaf6e965\n",
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            "  Created wheel for python-multipart: filename=python_multipart-0.0.5-py3-none-any.whl size=31678 sha256=2a45a6c60a2fdd97c52a2ad714e54fe5cd502de5505cbb384bb83883abaca95d\n",
            "  Stored in directory: /root/.cache/pip/wheels/9e/fc/1c/cf980e6413d3ee8e70cd8f39e2366b0f487e3e221aeb452eb0\n",
            "Successfully built ffmpy python-multipart\n",
            "Installing collected packages: rfc3986, pydub, ffmpy, websockets, uc-micro-py, sniffio, python-multipart, pycryptodome, orjson, mdurl, h11, aiofiles, uvicorn, markdown-it-py, linkify-it-py, anyio, starlette, mdit-py-plugins, httpcore, httpx, fastapi, gradio\n",
            "Successfully installed aiofiles-22.1.0 anyio-3.6.2 fastapi-0.89.1 ffmpy-0.3.0 gradio-3.16.2 h11-0.14.0 httpcore-0.16.3 httpx-0.23.3 linkify-it-py-1.0.3 markdown-it-py-2.1.0 mdit-py-plugins-0.3.3 mdurl-0.1.2 orjson-3.8.5 pycryptodome-3.17 pydub-0.25.1 python-multipart-0.0.5 rfc3986-1.5.0 sniffio-1.3.0 starlette-0.22.0 uc-micro-py-1.0.1 uvicorn-0.20.0 websockets-10.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr"
      ],
      "metadata": {
        "id": "fGvaKE0b4nWp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M2clom7V8riI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install simpletransformers"
      ],
      "metadata": {
        "id": "Ju3Ml3s88r8w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4fa6332-696e-4587-f839-71f258276903"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: tokenizers, sentencepiece, pathtools, xxhash, watchdog, validators, urllib3, smmap, setproctitle, semver, pympler, pygments, multiprocess, docker-pycreds, blinker, sentry-sdk, rich, pydeck, gitdb, seqeval, responses, huggingface-hub, GitPython, wandb, transformers, streamlit, datasets, simpletransformers\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed GitPython-3.1.30 blinker-1.5 datasets-2.9.0 docker-pycreds-0.4.0 gitdb-4.0.10 huggingface-hub-0.12.0 multiprocess-0.70.14 pathtools-0.1.2 pydeck-0.8.0 pygments-2.14.0 pympler-1.0.1 responses-0.18.0 rich-13.3.1 semver-2.13.0 sentencepiece-0.1.97 sentry-sdk-1.14.0 seqeval-1.2.2 setproctitle-1.3.2 simpletransformers-0.63.9 smmap-5.0.0 streamlit-1.17.0 tokenizers-0.13.2 transformers-4.26.0 urllib3-1.26.14 validators-0.20.0 wandb-0.13.9 watchdog-2.2.1 xxhash-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from simpletransformers.ner import NERModel, NERArgs"
      ],
      "metadata": {
        "id": "ra4fxaQEP2_1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n"
      ],
      "metadata": {
        "id": "5OVUBCYtLgVZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ner(text):\n",
        "    model = NERModel(\"bert\", \"/content/drive/MyDrive/NLP/foodordereng\",use_cuda=False)\n",
        "    trans_table = text.maketrans('', '', string.punctuation)\n",
        "    text = text.translate(trans_table)\n",
        "\n",
        "    text=text.lower()\n",
        "    \n",
        "    prediction, model_output = model.predict([text])\n",
        "    filtered_output = [[{v: k} for d in sublist for k, v in d.items() if v.startswith(\"B-\")] for sublist in prediction]\n",
        "    entities = []\n",
        "    for sublist in filtered_output:\n",
        "        for d in sublist:\n",
        "            for k, v in d.items():\n",
        "                label = k.split(\"-\")[1]\n",
        "                entities.append((label, v))\n",
        "    return entities"
      ],
      "metadata": {
        "id": "80nhVBvseSe8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zxR0CU2uiQPO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "demo = gr.Interface(fn=ner, inputs=gr.Textbox(lines=5, placeholder='Enter your input here'), outputs=gr.Textbox(lines=10, placeholder='Sipariş dökümü'),examples=['Bize iki buçuk pide ve iki büyük ayran istiyorum, kapıda nakit öderim, evime yollayın...' , 'bir mantı, bir döner, bir mevsim salata, bir kola ve 1 sprite istiyorum' ],\n",
        "    title=\"Söyle Gelsin\", description=\"Yemek siparişi uygulamamızı kullanabilirsiniz...\")"
      ],
      "metadata": {
        "id": "j5mRCktEIuKl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    demo.launch(share=False,debug=False)\n"
      ],
      "metadata": {
        "id": "7vpfm6vQZYq0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "5e23e8b7-9bee-480c-da66-bb9b0ac9769c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7860, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iCdBEEQdiZzf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fiaNvl3GiZuS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PhrBJ8FR4hz8"
      },
      "source": [
        "# not used codes"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In PyTorch, the learnable parameters (i.e. weights and biases) of an torch.nn.Module model are contained in the model’s parameters (accessed with model.parameters()). A state_dict is simply a Python dictionary object that maps each layer to its parameter tensor. So, you can save a model in different ways,\n",
        "First way is to store a model like you have stored torch.save(model.state_dict(), PATH) and to load the same model on a different machine or some different place then first you have to make the instance of that model and then assign that model to the model parameter like this.\n"
      ],
      "metadata": {
        "id": "52PvFZXEvg9-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = TheModelClass(*args, **kwargs)\n"
      ],
      "metadata": {
        "id": "KtgqhRtyvngT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "after making the instance of the class then load the model like this.\n"
      ],
      "metadata": {
        "id": "eAqgRfpPvofd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_state_dict(torch.load(PATH))\n"
      ],
      "metadata": {
        "id": "IbSAEvanvrT-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's a recommended way of saving and loading a model. If you are saving the model then before loading the model on a different machine first make the instance of that model then you can run the model."
      ],
      "metadata": {
        "id": "nU4-C9pyvtXc"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w26JPgNtz8ZI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LFgoeH5cz8Ws"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Jhdh4ug_Md3j"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GWv8A6nKpFcH"
      },
      "outputs": [],
      "source": [
        "# Train test split\n",
        "X = df[[\"sentence_id\",\"words\"]]\n",
        "Y = df[\"labels\"]\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size =0.2)#,random_state=42)\n",
        "\n",
        "# Building up train data and test data\n",
        "train_df = pd.DataFrame({\"sentence_id\":x_train[\"sentence_id\"],\"words\":x_train[\"words\"],\"labels\":y_train})\n",
        "test_df = pd.DataFrame({\"sentence_id\":x_test[\"sentence_id\"],\"words\":x_test[\"words\"],\"labels\":y_test})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6aZ8UoA25-ZR"
      },
      "outputs": [],
      "source": [
        "#### burasını çalıştırma#########\n",
        "#################################\n",
        "\n",
        "\n",
        "\n",
        "# Read sample training NER data\n",
        "data = pd.read_csv(\"sample_ner_dataset.csv\", encoding=\"latin1\")\n",
        "data = data.fillna(method =\"ffill\")\n",
        "\n",
        "# Label Encode\n",
        "data[\"Sentence #\"] = LabelEncoder().fit_transform(data[\"Sentence #\"] )\n",
        "data.rename(columns={\"Sentence #\":\"sentence_id\",\"Word\":\"words\",\"Tag\":\"labels\"}, inplace =True)\n",
        "data[\"labels\"] = data[\"labels\"].str.upper()\n",
        "\n",
        "# Train test split\n",
        "X = data[[\"sentence_id\",\"words\"]]\n",
        "Y = data[\"labels\"]\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size =0.2)\n",
        "\n",
        "# Building up train data and test data\n",
        "train_data = pd.DataFrame({\"sentence_id\":x_train[\"sentence_id\"],\"words\":x_train[\"words\"],\"labels\":y_train})\n",
        "test_data = pd.DataFrame({\"sentence_id\":x_test[\"sentence_id\"],\"words\":x_test[\"words\"],\"labels\":y_test})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pj0dY1GW5Y3D"
      },
      "source": [
        "# second way"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lX1xZ_feRAEZ"
      },
      "source": [
        "NerDataset expects a .txt file for each train/test/dev set. So our next step is to create these CoNLL formatted files into a directory (“data\\”) where we will keep our training and testing .txt files:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ukqZu6_Q_ND"
      },
      "outputs": [],
      "source": [
        "train_df.to_csv('train.txt', sep='\\t', index=False, header=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1qUKD05ARWos"
      },
      "outputs": [],
      "source": [
        "test_df.to_csv('test.txt', sep='\\t', index=False)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgLsz1yMPzTo"
      },
      "source": [
        "NerDataset\n",
        "Our trainer object will expect the training input through NerDataset object. We already prepared the train.txt file so now we need to provide the BERT tokenizer and specify the needed parameter (files directory, max sequence length..etc.). First, we must download utils_ner.py which contains the definition of NerDataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SWGBiDV7I-dX"
      },
      "outputs": [],
      "source": [
        "!wget https://github.com/huggingface/transformers/blob/main/examples/legacy/token-classification/utils_ner.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_jFmhpIP5Zs"
      },
      "source": [
        "This script must be added to python build path so you can simply import it. Now we must specify the model parameters, these are needed to initialize BERT tokenizer and the model. We will keep BERT model arguement in two seperate python dictionaries, one for BERT model parameters, and the other for data related parameters. The model we’re using is a cased base BERT model (BERTurk) pre-trained on a Turkish corpus of size 35GB and 44,04,976,662 tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pI0mRxXgkMZO"
      },
      "outputs": [],
      "source": [
        "model_args = dict()\n",
        "\n",
        "# Path to pretrained model or model identifier from huggingface.co/models\n",
        "model_args['model_name_or_path'] = 'dbmdz/bert-base-turkish-cased' \n",
        "\n",
        "# Where do you want to store the pretrained models downloaded from s3\n",
        "model_args['cache_dir'] = \"BERTurk_cache/\"\n",
        "\n",
        "# we skip basic white-space tokenization by passing do_basic_tokenize = False to the tokenizer\n",
        "model_args['do_basic_tokenize'] = False\n",
        "\n",
        "data_dir = '/content/'\n",
        "\n",
        "data_args = dict()\n",
        "\n",
        "data_args['data_dir'] = data_dir \n",
        "\n",
        "# \"The maximum total input sequence length after tokenization. Sequences longer \"\n",
        "# \"than this will be truncated, sequences shorter will be padded.\"\n",
        "data_args['max_seq_length'] = 512 \n",
        "\n",
        "# Overwrite the cached training and evaluation sets\n",
        "# this means the model does not have to tokenize/preprocess and cache the data each time it's called\n",
        "# this can be made different for each NerDataset (training NerDataset, testing NerDataset)\n",
        "data_args['overwrite_cache'] = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tzxzHOymkMWd"
      },
      "outputs": [],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VXxSag5kkMTx"
      },
      "outputs": [],
      "source": [
        "from transformers import (\n",
        "    AutoConfig,\n",
        "    AutoModelForTokenClassification,\n",
        "    AutoTokenizer\n",
        ")\n",
        "\n",
        "config = AutoConfig.from_pretrained(\n",
        "    model_args['model_name_or_path'],\n",
        "    num_labels=num_labels,\n",
        "    id2label=label_map,\n",
        "    label2id={label: i for i, label in enumerate(labels)},\n",
        "    cache_dir=model_args['cache_dir']\n",
        ")\n",
        "\n",
        "# we skip basic white-space tokenization by passing do_basic_tokenize = False to the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    model_args['model_name_or_path'],\n",
        "    cache_dir=model_args['cache_dir'],\n",
        "    do_basic_tokenize = model_args['do_basic_tokenize']\n",
        ")\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\n",
        "    model_args['model_name_or_path'],\n",
        "    config=config,\n",
        "    cache_dir=model_args['cache_dir']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6SlcJioqSuYE"
      },
      "outputs": [],
      "source": [
        "!pip install utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NbGNwKvsTleQ"
      },
      "outputs": [],
      "source": [
        "from utils_ner import NerDataset, Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XBda8ZWUkLBj"
      },
      "outputs": [],
      "source": [
        "train_dataset = NerDataset(\n",
        "  data_dir=data_args['data_dir'],\n",
        "  tokenizer=tokenizer,\n",
        "  labels=labels,\n",
        "  model_type=config.model_type,\n",
        "  max_seq_length=data_args['max_seq_length'],\n",
        "  overwrite_cache=data_args['overwrite_cache'],\n",
        "  mode=Split.train)"
      ]
    }
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