multiclass_sentiment.ipynb

{
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    {
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      "metadata": {
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        "id": "ABvCaIC-77ve",
        "outputId": "80920a70-fe80-4f8b-bf72-5db644e04941"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: tokenizers, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets\n",
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          ]
        }
      ],
      "source": [
        "pip install transformers datasets"
      ]
    },
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/16000 [00:00<?, ? examples/s]"
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              "Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
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              "version_minor": 0,
              "model_id": "a638ab8430c743d085a6039499f7623d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ac414cf8c8194aa5af38e9cecae7ebdd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "emotion = load_dataset('emotion')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YtEvL3xR864v"
      },
      "outputs": [],
      "source": [
        "train_ds = emotion['train']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "d0XggUUy9703"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "emotion.set_format(type='pandas')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "E1RicDpg-D5x"
      },
      "outputs": [],
      "source": [
        "df = emotion['train'][:]"
      ]
    },
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          "output_type": "execute_result",
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              "                                                text  label\n",
              "0                            i didnt feel humiliated      0\n",
              "1  i can go from feeling so hopeless to so damned...      0\n",
              "2   im grabbing a minute to post i feel greedy wrong      3\n",
              "3  i am ever feeling nostalgic about the fireplac...      2\n",
              "4                               i am feeling grouchy      3"
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    {
      "cell_type": "code",
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        "def label_int2str(row):\n",
        "  return emotion['train'].features['label'].int2str(row)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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      ]
    },
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              "                                                text  label label_name\n",
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              "2   im grabbing a minute to post i feel greedy wrong      3      anger\n",
              "3  i am ever feeling nostalgic about the fireplac...      2       love\n",
              "4                               i am feeling grouchy      3      anger"
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              "                                                     [key], {});\n",
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              "\n",
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    {
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      "execution_count": 10,
      "metadata": {
        "id": "sYxaYDbB_kIm"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
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      "metadata": {
        "colab": {
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df['label_name'].value_counts(ascending=True).plot.barh()\n",
        "plt.title('frequency of classes')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "odHYWlPuBgVF"
      },
      "outputs": [],
      "source": [
        "emotion.reset_format()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "9t1bpWT-CIO8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "63c7cd148096464db4d0b7752913c978",
            "fc42f103b9874d75bb3a7d8f842913ad",
            "ab42e0a5963742a3b85dfbf604171caf",
            "b1e55da05e95422281e15fd558d83583",
            "f082897c7658497687258d8093d73375",
            "f962cd2c275741e8b2cb63f9d6dafe80",
            "3f9b40e7c81644cdaba58cc9d05f4c44",
            "0c639dff68c6472dbd45e3d8d5bec986",
            "c4d643cb05494630ad0ccd1797c9e4a6",
            "e12f4b7a0389479abd5746cb6e608455",
            "a9747a1b50274b608ca5d1a8577ad557",
            "e5df57a2b9084e06bd40e155e3b40205",
            "152f683f5c234f49bae123379e5968d0",
            "891bb1b144134f53990413ffd427d52e",
            "1010ed17d92344cabd99f9a6796fe268",
            "d7c12c3c7f844dadb03cfcddbd781c96",
            "9dba3e546dc041fcb6ee01c42537cca7",
            "990434261eb043729de703309aef067c",
            "9f0b2bbf21aa4693b6eed22c93cbdf05",
            "d52c0f12d22243b6b36fec9f287fafc4",
            "a804368a17dc4ea1993d3d48fd9ee910",
            "7a76750072eb40fc9fa90be9ceb21c3e",
            "ed8b84b092e0458aa8bb5dc61693caed",
            "e7c83a7f463c4d7abd2af373f2dc8b75",
            "c0626a03ecf548fb9eb8ebea05c24e64",
            "cb44b2db61f44737b8d3187c25c3010d",
            "f0d363e023fa4b9aa59ebc077f691b4f",
            "22e8810b7bef44a5836ed438bd3f4f50",
            "a1c1f07e42904b50b2907e751a2c054b",
            "eaaf23e43e4b4e9ca4d34e6dfb48275b",
            "cf3672d75ecf4781889a8406b57bf18c",
            "3277b9f6f55a4dd2864141085d915e85",
            "4b42cd1868264c9cb16d7ae2ef1b3268",
            "2cedcc9c9c6e4b94912b23fefd954a8b",
            "a21252ad6d7745fc8a40613a6110145e",
            "69c45a1e158543268b5474bfca140aa7",
            "0fc1ea71243a46408646fe3569186a23",
            "ad6c8a909dee46bc88d0027544d4c6c3",
            "639232c16ef24ee58f6b32b2f83158b2",
            "6aa70d902b0e4cc793078571e3c0ca6c",
            "5fa9e670e9544d0bb6fe349fe260d40e",
            "4b00be36a4924b8b8eca028db2987410",
            "1e74d8d2cfa7442aa3c898478df8ca11",
            "eb2cc20ba6df423595b23af100af5d4e"
          ]
        },
        "outputId": "507e8483-7e55-410d-ea8e-863e990b8e4a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63c7cd148096464db4d0b7752913c978"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e5df57a2b9084e06bd40e155e3b40205"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed8b84b092e0458aa8bb5dc61693caed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2cedcc9c9c6e4b94912b23fefd954a8b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "model_name = 'distilbert-base-uncased'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, max_length=70)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "hZvwQ539FPIx"
      },
      "outputs": [],
      "source": [
        "def tokenize(row):\n",
        "  return tokenizer(row['text'], padding=True, truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
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        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/500 [00:00<?, ?ba/s]"
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        {
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              "model_id": "1d1f3269045b41618f5713ee8356508f"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "emotions_encoded = emotion.map(tokenize, batched=True, batch_size=32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BhQg1lorFla",
        "outputId": "159e8776-a26e-40c1-cfa1-594ccafcd3c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label', 'input_ids', 'attention_mask'],\n",
              "    num_rows: 16000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "emotions_encoded['train']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "LFJ9yI2QrPnx"
      },
      "outputs": [],
      "source": [
        "from transformers import TFAutoModel\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gi6V4389tbs2",
        "outputId": "c801120f-b1e8-499b-b18f-a7b8566e5d79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertModel: ['vocab_transform', 'activation_13', 'vocab_layer_norm', 'vocab_projector']\n",
            "- This IS expected if you are initializing TFDistilBertModel 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 TFDistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFDistilBertModel were initialized from the model checkpoint at distilbert-base-uncased.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertModel for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "model = TFAutoModel.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "ttT5BvjxzZ32"
      },
      "outputs": [],
      "source": [
        "emotions_encoded.set_format('tensorflow', columns=emotions_encoded['train'].features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCVx_AJ2DkiA",
        "outputId": "d574fd2b-5605-430b-b169-09ed8c085ca2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(16000,), dtype=int64, numpy=array([0, 0, 3, ..., 1, 3, 0])>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "emotions_encoded['train']['label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "PBPUu0Xg2xmc"
      },
      "outputs": [],
      "source": [
        "from transformers import TFAutoModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "VvgbE7DAc98U"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "1HwumtIzdGVn"
      },
      "outputs": [],
      "source": [
        "train_encoded_inputs = tokenizer(emotion['train']['text'],\n",
        "                                 add_special_tokens = True,\n",
        "                                 padding='max_length', \n",
        "                                 truncation=True, \n",
        "                                 max_length=70, \n",
        "                                 return_token_type_ids=False,\n",
        "                                 return_tensors = 'tf')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize(data):\n",
        "  return  tokenizer(data,\n",
        "                    add_special_tokens = True,\n",
        "                    padding='max_length', \n",
        "                    truncation=True, \n",
        "                    max_length=70, \n",
        "                    return_token_type_ids=False,\n",
        "                    return_tensors = 'tf')"
      ],
      "metadata": {
        "id": "PK0PmQYvnX7L"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RoNe2MPC21Ax",
        "outputId": "c62147f1-605e-48c5-bca7-c1cfd19bc100"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertModel: ['vocab_transform', 'activation_13', 'vocab_layer_norm', 'vocab_projector']\n",
            "- This IS expected if you are initializing TFDistilBertModel 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 TFDistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFDistilBertModel were initialized from the model checkpoint at distilbert-base-uncased.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertModel for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "model = TFAutoModel.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lHHD-GVMrS_2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-RzBIaEzrTIA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "VZB5nVxA3n19"
      },
      "outputs": [],
      "source": [
        "input1 = tf.keras.layers.Input(shape=(70,), dtype=tf.int32, name='input_ids')\n",
        "input2 = tf.keras.layers.Input(shape=(70,), dtype=tf.int32, name='attention_ids')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "xB9sjxYU4p58"
      },
      "outputs": [],
      "source": [
        "output = model(input1, input2)[0]\n",
        "output = tf.keras.layers.GlobalAveragePooling1D()(output)\n",
        "output = tf.keras.layers.Dense(256, activation='tanh')(output)\n",
        "output = tf.keras.layers.Dropout(0.3)(output)\n",
        "output = tf.keras.layers.Dense(6, activation='softmax')(output)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "zkD27ckQ7Yta"
      },
      "outputs": [],
      "source": [
        "mod = tf.keras.Model(inputs=[input1, input2], outputs=output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "zVp15uO2-esk"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "bqU1DzifdwJE"
      },
      "outputs": [],
      "source": [
        "mod.layers[2].trainable=True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "-WJSg1cHXxrS"
      },
      "outputs": [],
      "source": [
        "mod.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WecMt3aAYKiL",
        "outputId": "f0e3c928-2683-4a63-bbb1-f547ca5ed443"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/9\n",
            "250/250 [==============================] - 127s 433ms/step - loss: 0.4450 - accuracy: 0.8386\n",
            "Epoch 2/9\n",
            "250/250 [==============================] - 105s 421ms/step - loss: 0.1366 - accuracy: 0.9409\n",
            "Epoch 3/9\n",
            "250/250 [==============================] - 105s 419ms/step - loss: 0.1057 - accuracy: 0.9515\n",
            "Epoch 4/9\n",
            "250/250 [==============================] - 105s 420ms/step - loss: 0.0845 - accuracy: 0.9606\n",
            "Epoch 5/9\n",
            "250/250 [==============================] - 105s 419ms/step - loss: 0.0696 - accuracy: 0.9679\n",
            "Epoch 6/9\n",
            "250/250 [==============================] - 105s 418ms/step - loss: 0.0683 - accuracy: 0.9696\n",
            "Epoch 7/9\n",
            "250/250 [==============================] - 105s 419ms/step - loss: 0.0547 - accuracy: 0.9771\n",
            "Epoch 8/9\n",
            "250/250 [==============================] - 105s 418ms/step - loss: 0.0389 - accuracy: 0.9847\n",
            "Epoch 9/9\n",
            "250/250 [==============================] - 105s 419ms/step - loss: 0.0338 - accuracy: 0.9876\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f091d223790>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "mod.fit({'input_ids': train_encoded_inputs['input_ids'], 'attention_ids': train_encoded_inputs['attention_mask']}, emotions_encoded['train']['label'], epochs=9, batch_size=64)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']"
      ],
      "metadata": {
        "id": "Gw9BZLW7o8zz"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t = tokenize(['we won the match'])"
      ],
      "metadata": {
        "id": "MssFolZSnjf5"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "ajk1Q9589f5b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "449ddb8c-6a99-46cc-8ef8-87e0a07d89d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 92ms/step\n"
          ]
        }
      ],
      "source": [
        "pred = mod.predict({'input_ids': t['input_ids'], 'attention_ids': t['attention_mask']})"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr[np.argmax(pred)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "e_rz9XlBnTJQ",
        "outputId": "ba649145-d40d-46a3-cfd2-c2ac0fa331aa"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'joy'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4r9FVetTpIqf"
      },
      "execution_count": 111,
      "outputs": []
    }
  ],
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