Disasater_Tweet_Classification_bert_uncased.ipynb

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        "<a href=\"https://colab.research.google.com/github/zainali60/PIAIC-Q3/blob/main/Disasater_Tweet_Classification_bert_uncased.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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        "#Loading libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "\n",
        "import plotly.graph_objects as go\n",
        "import plotly.figure_factory as ff\n",
        "\n",
        "import nltk\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from nltk.tokenize import TweetTokenizer\n",
        "from nltk.tokenize import word_tokenize \n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "import re\n",
        "import string"
      ],
      "metadata": {
        "id": "0L5q0UOTc2de"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv(\"/content/train.csv\",encoding='latin')\n",
        "test = pd.read_csv(\"/content/test.csv\",encoding='latin')\n",
        "train.head(3)"
      ],
      "metadata": {
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              "   id keyword  ...                                               text target\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7613 entries, 0 to 7612\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   id        7613 non-null   int64 \n",
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            " 2   location  5080 non-null   object\n",
            " 3   text      7613 non-null   object\n",
            " 4   target    7613 non-null   int64 \n",
            "dtypes: int64(2), object(3)\n",
            "memory usage: 297.5+ KB\n"
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        "df = train.drop(['keyword', 'location'], axis=1)\n",
        "df.head()"
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              "                                                     [key], {});\n",
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              "   id                                               text  target\n",
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              "1   4             Forest fire near La Ronge Sask. Canada       1\n",
              "2   5  All residents asked to 'shelter in place' are ...       1\n",
              "3   6  13,000 people receive #wildfires evacuation or...       1\n",
              "4   7  Just got sent this photo from Ruby #Alaska as ...       1"
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        "fig.show()"
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        "id": "6q6259KZpakx",
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        "def cleaner(tweet):\n",
        "    \n",
        "    # remove urls\n",
        "    tweet = re.sub(r'http\\S+', ' ', tweet)\n",
        "    \n",
        "    # remove html tags\n",
        "    tweet = re.sub(r'<.*?>',' ', tweet)\n",
        "    \n",
        "    # remove digits\n",
        "    tweet = re.sub(r'\\d+',' ', tweet)\n",
        "    \n",
        "    # remove hashtags\n",
        "    tweet = re.sub(r'#\\w+',' ', tweet)\n",
        "    \n",
        "    # remove mentions\n",
        "    tweet = re.sub(r'@\\w+',' ', tweet)\n",
        "    \n",
        "    #removing stop words\n",
        "    tweet = tweet.split()\n",
        "    tweet = \" \".join([word for word in tweet if not word in stop_words])\n",
        "    return  tweet"
      ],
      "metadata": {
        "id": "j4IT7WkDqB8v"
      },
      "execution_count": 57,
      "outputs": []
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      "source": [
        "df['text'] = df['text'].apply(cleaner)\n",
        "df.head()"
      ],
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          "height": 206
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            "text/plain": [
              "   id                                               text  target\n",
              "0   1              Our Deeds Reason May ALLAH Forgive us       1\n",
              "1   4             Forest fire near La Ronge Sask. Canada       1\n",
              "2   5  All residents asked 'shelter place' notified o...       1\n",
              "3   6      , people receive evacuation orders California       1\n",
              "4   7        Just got sent photo Ruby smoke pours school       1"
            ]
          },
          "metadata": {},
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        }
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      "cell_type": "code",
      "source": [
        "df['text']=df['text'].apply(lambda x: x.translate(str.maketrans(dict.fromkeys(string.punctuation))))"
      ],
      "metadata": {
        "id": "9t1n8gbus1eN"
      },
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      "outputs": []
    },
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      "source": [
        "df.text[5]"
      ],
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        "id": "f_Y6xHvOs5xQ",
        "outputId": "dfd8a090-1811-4f1d-95cd-1787bfe47d77"
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      "outputs": [
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              "type": "string"
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            "text/plain": [
              "'Update  California Hwy closed directions due Lake County fire '"
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          },
          "metadata": {},
          "execution_count": 67
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      "cell_type": "code",
      "source": [
        "df.text[7612]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "nDKhmA7Jxmgk",
        "outputId": "25ce6751-3633-4c16-e36e-314520e951e8"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'The Latest More Homes Razed Northern California Wildfire  ABC News'"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers --q\n",
        "!pip install datasets --q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5g-UCx_nyNKf",
        "outputId": "8fca44b5-e547-44c2-949b-15835a1f6d10"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 306 kB 4.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 46.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 132 kB 48.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 243 kB 49.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 271 kB 46.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 192 kB 47.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 160 kB 49.1 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset"
      ],
      "metadata": {
        "id": "e-MbiLs51V30"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments, default_data_collator, Trainer, set_seed\n",
        "set_seed(42)\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')\n",
        "model = BertForSequenceClassification.from_pretrained('bert-large-uncased')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
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        {
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            "text/plain": [
              "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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          },
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        {
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        {
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            "text/plain": [
              "Downloading:   0%|          | 0.00/571 [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d00a035722f3440d977aaeabf8740384",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "Downloading:   0%|          | 0.00/1.25G [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-large-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at bert-large-uncased 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maxx, minn = 0, 1000000\n",
        "for index, row in df.iterrows():\n",
        "    if len(row['text'].split())>maxx:\n",
        "        maxx=len(row['text'].split())\n",
        "    if len(row['text'].split())<minn:\n",
        "        minn=len(row['text'].split())\n",
        "maxx, minn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8B6PPf1ycOU",
        "outputId": "785f28c4-9124-43ae-8406-cdb8271de955"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 0)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_length=64\n",
        "df['input_ids']=df['text'].apply(lambda x: tokenizer(x, max_length=max_length, padding='max_length')['input_ids'])\n",
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Ek8AX-xozbEo",
        "outputId": "660db0a9-b195-4561-aeb4-7fa6d0fda5f3"
      },
      "execution_count": 73,
      "outputs": [
        {
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              "          const element = document.querySelector('#df-8d267983-74d1-4f04-8961-4328ae95eb92');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   id  ...                                          input_ids\n",
              "0   1  ...  [101, 2256, 15616, 3114, 2089, 16455, 9641, 21...\n",
              "1   4  ...  [101, 3224, 2543, 2379, 2474, 6902, 3351, 2187...\n",
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              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['input_ids', 'labels']]\n",
        "df.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 206
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      "execution_count": 75,
      "outputs": [
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          "output_type": "execute_result",
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              "  </style>\n",
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              "\n",
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              "          const element = document.querySelector('#df-5a4298b2-811f-4433-be81-ccf0c1c48b90');\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",
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              "  "
            ],
            "text/plain": [
              "                                           input_ids  labels\n",
              "0  [101, 2256, 15616, 3114, 2089, 16455, 9641, 21...       1\n",
              "1  [101, 3224, 2543, 2379, 2474, 6902, 3351, 2187...       1\n",
              "2  [101, 2035, 3901, 2356, 7713, 2173, 19488, 373...       1\n",
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              "4  [101, 2074, 2288, 2741, 6302, 10090, 5610, 103...       1"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = df[:-int(len(df)*0.01)].reset_index(drop=True)\n",
        "test_df = df[-int(len(df)*0.01):].reset_index(drop=True)"
      ],
      "metadata": {
        "id": "9FhgeK150rb6"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saf_2ERE0wwh",
        "outputId": "bc56e7e3-17a1-4b89-82d5-28b026b09c31"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "input_ids    object\n",
              "labels        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_df), len(test_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "et-l6ss40z3t",
        "outputId": "fb1e0485-dc6a-49d2-eff2-13d0f5e251a0"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7537, 76)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = Dataset.from_pandas(train_df)\n",
        "test_ds = Dataset.from_pandas(test_df)"
      ],
      "metadata": {
        "id": "cHtjsfVO1FhV"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(train_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mTWqLnBR1Jp5",
        "outputId": "16fe95fb-6cb1-4875-cb74-c5d1575ec927"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datasets.arrow_dataset.Dataset"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 16\n",
        "\n",
        "args = TrainingArguments(\n",
        "    'nlp-getting-started',\n",
        "    evaluation_strategy = 'epoch',\n",
        "    save_strategy = 'epoch',\n",
        "    learning_rate=1e-5,\n",
        "    gradient_accumulation_steps=8,\n",
        "    num_train_epochs=2,\n",
        "    warmup_ratio=0.1,\n",
        "    weight_decay=0.01,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        ")\n",
        "data_collator = default_data_collator\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=test_ds,\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        ")"
      ],
      "metadata": {
        "id": "2_GOR-sU1a6h"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "jtA7fs4I1xSe",
        "outputId": "f438546f-2ab4-46d2-da20-25ade1c3f282"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 7537\n",
            "  Num Epochs = 2\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 128\n",
            "  Gradient Accumulation steps = 8\n",
            "  Total optimization steps = 118\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='118' max='118' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [118/118 20:38, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.212338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.189531</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 76\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to nlp-getting-started/checkpoint-59\n",
            "Configuration saved in nlp-getting-started/checkpoint-59/config.json\n",
            "Model weights saved in nlp-getting-started/checkpoint-59/pytorch_model.bin\n",
            "tokenizer config file saved in nlp-getting-started/checkpoint-59/tokenizer_config.json\n",
            "Special tokens file saved in nlp-getting-started/checkpoint-59/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 76\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to nlp-getting-started/checkpoint-118\n",
            "Configuration saved in nlp-getting-started/checkpoint-118/config.json\n",
            "Model weights saved in nlp-getting-started/checkpoint-118/pytorch_model.bin\n",
            "tokenizer config file saved in nlp-getting-started/checkpoint-118/tokenizer_config.json\n",
            "Special tokens file saved in nlp-getting-started/checkpoint-118/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=118, training_loss=0.5059032440185547, metrics={'train_runtime': 1246.9075, 'train_samples_per_second': 12.089, 'train_steps_per_second': 0.095, 'total_flos': 1755991671350784.0, 'train_loss': 0.5059032440185547, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"accuracy\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7c8e9cde6b2748e990a000db63a5d1b8",
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            "5e6c6214c2cc4f9bbdf32be8233e73a9",
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            "6ddc8344053f4535856941767aa3bf7d",
            "c16d1277b0ec4c9aa891b7c2f048ee5f",
            "6f31906d0a574b9b9b98e88bee02498f",
            "af1d9c0d0738485990de1976cfe98f07",
            "026f186217cf45c1927a7b18ba5790c1",
            "b86cd116d4bf49d99818b2219d638573"
          ]
        },
        "id": "Sp0aSWgP129F",
        "outputId": "59e321a0-6291-4631-a1fb-94a14831ab4c"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7c8e9cde6b2748e990a000db63a5d1b8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=test_ds,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "lTR1pzbJ6HMF",
        "outputId": "99fb0817-1870-40ef-96c9-d627e1d26b31"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 76\n",
            "  Batch size = 16\n"
          ]
        },
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              "      [5/5 00:01]\n",
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              "{'eval_accuracy': 0.9605263157894737,\n",
              " 'eval_loss': 0.18953128159046173,\n",
              " 'eval_runtime': 2.2406,\n",
              " 'eval_samples_per_second': 33.92,\n",
              " 'eval_steps_per_second': 2.232}"
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          "metadata": {},
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    {
      "cell_type": "code",
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        "test.head(2)"
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          "height": 112
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        "outputId": "73fc36a7-646e-4404-de54-d9d17cd39893"
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      "execution_count": 96,
      "outputs": [
        {
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              "\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <td>Heard different cities, stay safe everyone.</td>\n",
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              "          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",
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              "  "
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            "text/plain": [
              "   id keyword location                                         text\n",
              "0   0     NaN      NaN             Just happened terrible car crash\n",
              "1   2     NaN      NaN  Heard different cities, stay safe everyone."
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cleaner(tweet):\n",
        "    \n",
        "    # remove urls\n",
        "    tweet = re.sub(r'http\\S+', ' ', tweet)\n",
        "    \n",
        "    # remove html tags\n",
        "    tweet = re.sub(r'<.*?>',' ', tweet)\n",
        "    \n",
        "    # remove digits\n",
        "    tweet = re.sub(r'\\d+',' ', tweet)\n",
        "    \n",
        "    # remove hashtags\n",
        "    tweet = re.sub(r'#\\w+',' ', tweet)\n",
        "    \n",
        "    # remove mentions\n",
        "    tweet = re.sub(r'@\\w+',' ', tweet)\n",
        "    \n",
        "    #removing stop words\n",
        "    tweet = tweet.split()\n",
        "    tweet = \" \".join([word for word in tweet if not word in stop_words])\n",
        "    return  tweet\n",
        "test['text'] = test['text'].apply(cleaner)\n",
        "test.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "LDmdlXsb69eU",
        "outputId": "d6ce3cd5-6e91-4ab6-85b1-20cb92b4d041"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <td>Heard different cities, stay safe everyone.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e9d372f-50f5-4548-8e35-39ce9280339c')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1e9d372f-50f5-4548-8e35-39ce9280339c button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1e9d372f-50f5-4548-8e35-39ce9280339c');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   id keyword location                                         text\n",
              "0   0     NaN      NaN             Just happened terrible car crash\n",
              "1   2     NaN      NaN  Heard different cities, stay safe everyone."
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test['text']=test['text'].apply(lambda x: x.translate(str.maketrans(dict.fromkeys(string.punctuation))))\n"
      ],
      "metadata": {
        "id": "r6CDAtM67DZM"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test['input_ids']=test['text'].apply(lambda x: tokenizer(x, max_length=max_length, padding='max_length')['input_ids'])\n",
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ShOeJx-17hDw",
        "outputId": "da0c1615-815f-405f-8781-3162b78993a0"
      },
      "execution_count": 102,
      "outputs": [
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-c43e1498-4fa8-4d04-a8bb-9c2845b28c52 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c43e1498-4fa8-4d04-a8bb-9c2845b28c52');\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",
              "  "
            ],
            "text/plain": [
              "   id  ...                                          input_ids\n",
              "0   0  ...  [101, 2074, 3047, 6659, 2482, 5823, 102, 0, 0,...\n",
              "1   2  ...  [101, 2657, 2367, 3655, 2994, 3647, 3071, 102,...\n",
              "2   3  ...  [101, 3224, 2543, 3962, 8644, 28519, 14070, 24...\n",
              "3   9  ...  [101, 16976, 7497, 102, 0, 0, 0, 0, 0, 0, 0, 0...\n",
              "4  11  ...  [101, 15393, 2061, 12672, 10626, 8563, 2859, 6...\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_submission=test[['input_ids']]\n",
        "df_submission.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "j9DTcbge761s",
        "outputId": "95cc5393-7cde-4f05-b652-157e68c90c0e"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<style scoped>\n",
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              "      <th>input_ids</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    </tr>\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b314194a-a82c-4e4a-bfcd-fe82e35e6e00 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-b314194a-a82c-4e4a-bfcd-fe82e35e6e00');\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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              "        }\n",
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              "    </div>\n",
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              "  "
            ],
            "text/plain": [
              "                                           input_ids\n",
              "0  [101, 2074, 3047, 6659, 2482, 5823, 102, 0, 0,...\n",
              "1  [101, 2657, 2367, 3655, 2994, 3647, 3071, 102,...\n",
              "2  [101, 3224, 2543, 3962, 8644, 28519, 14070, 24...\n",
              "3  [101, 16976, 7497, 102, 0, 0, 0, 0, 0, 0, 0, 0...\n",
              "4  [101, 15393, 2061, 12672, 10626, 8563, 2859, 6..."
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output_ds = Dataset.from_pandas(df_submission)\n",
        "output_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSTIsQ3n8E8S",
        "outputId": "5eec5373-87bd-414c-bee6-26f12a2af79e"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids'],\n",
              "    num_rows: 3263\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output_ds.features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lFdMti2i8Iex",
        "outputId": "8a7e8881-4213-4075-c7b5-d732e5bb0c6d"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outputs=trainer.predict(output_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "PdWa_sxF8Omw",
        "outputId": "88280cce-877c-4b47-d26f-77240045598f"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Prediction *****\n",
            "  Num examples = 3263\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='209' max='5' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5/5 10:02]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
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    {
      "cell_type": "code",
      "source": [
        "outputs.predictions.argmax(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eNPTavv8qid",
        "outputId": "b31fd546-86cf-4c08-df47-c6b35ac6509a"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, ..., 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "KV0Set2R8uBU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}