BERT NLP - Multi Class Classification Using BERT and Tensorflow.ipynb

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      "cell_type": "markdown",
      "source": [
        "**BERT NLP - Multi Class Classification Using BERT and Tensorflow**"
      ],
      "metadata": {
        "id": "p49tOdPfSY-b"
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    },
    {
      "cell_type": "code",
      "source": [
        "%pip install -qq transformers"
      ],
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        "id": "AMmnveuvsKwt"
      },
      "execution_count": 1,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "**Import Data**"
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      "metadata": {
        "id": "6jU34Q7COHyt"
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      "source": [
        "import pandas as pd\n",
        "\n",
        "train = pd.read_csv(\"/content/train.tsv\", sep=\"\\t\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
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              "   PhraseId  SentenceId                                             Phrase  \\\n",
              "0         1           1  A series of escapades demonstrating the adage ...   \n",
              "1         2           1  A series of escapades demonstrating the adage ...   \n",
              "2         3           1                                           A series   \n",
              "3         4           1                                                  A   \n",
              "4         5           1                                             series   \n",
              "\n",
              "   Sentiment  \n",
              "0          1  \n",
              "1          2  \n",
              "2          2  \n",
              "3          2  \n",
              "4          2  "
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        "y = train[\"Sentiment\"]\n",
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          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         A series of escapades demonstrating the adage ...\n",
              "1         A series of escapades demonstrating the adage ...\n",
              "2                                                  A series\n",
              "3                                                         A\n",
              "4                                                    series\n",
              "                                ...                        \n",
              "156055                                            Hearst 's\n",
              "156056                            forced avuncular chortles\n",
              "156057                                   avuncular chortles\n",
              "156058                                            avuncular\n",
              "156059                                             chortles\n",
              "Name: Phrase, Length: 156060, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
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      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "id": "WoKbHljietbe",
        "outputId": "c69b9513-f8fd-4dcc-9ec7-985e451ef6d4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         1\n",
              "1         2\n",
              "2         2\n",
              "3         2\n",
              "4         2\n",
              "         ..\n",
              "156055    2\n",
              "156056    1\n",
              "156057    3\n",
              "156058    2\n",
              "156059    2\n",
              "Name: Sentiment, Length: 156060, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train and Test Split**"
      ],
      "metadata": {
        "id": "0NPlk2nFf6OD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test =  train_test_split(x, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "uK0CYDWjf83h"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test"
      ],
      "metadata": {
        "id": "8b8nJLacgc0r",
        "outputId": "419a763d-716d-44d6-f599-e690c06a0083",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22538                                                cheesy\n",
              " 99237                                 unintentionally -RRB-\n",
              " 60377     will need all the luck they can muster just fi...\n",
              " 128317    somewhere between Sling Blade and South of Hea...\n",
              " 20776     reminds at every turn of Elizabeth Berkley 's ...\n",
              "                                 ...                        \n",
              " 119879                                its trademark villain\n",
              " 103694    earn her share of the holiday box office pie ,...\n",
              " 131932    A moving tale of love and destruction in unexp...\n",
              " 146867    If you love reading and\\/or poetry , then by a...\n",
              " 121958                         and drag audience enthusiasm\n",
              " Name: Phrase, Length: 124848, dtype: object,\n",
              " 95722                                           's as sorry\n",
              " 147312    Romantic comedy and Dogme 95 filmmaking may se...\n",
              " 36991                                         of these days\n",
              " 150211                 flinch from its unsettling prognosis\n",
              " 140655                             are clinically depressed\n",
              "                                 ...                        \n",
              " 115807                                     erotically frank\n",
              " 103403                                            idea work\n",
              " 78143                   the air-conditioning in the theater\n",
              " 55596                             current political climate\n",
              " 118795                                    the class warfare\n",
              " Name: Phrase, Length: 31212, dtype: object,\n",
              " 22538     1\n",
              " 99237     2\n",
              " 60377     0\n",
              " 128317    2\n",
              " 20776     1\n",
              "          ..\n",
              " 119879    2\n",
              " 103694    2\n",
              " 131932    3\n",
              " 146867    3\n",
              " 121958    2\n",
              " Name: Sentiment, Length: 124848, dtype: int64,\n",
              " 95722     2\n",
              " 147312    4\n",
              " 36991     2\n",
              " 150211    2\n",
              " 140655    1\n",
              "          ..\n",
              " 115807    3\n",
              " 103403    2\n",
              " 78143     2\n",
              " 55596     2\n",
              " 118795    1\n",
              " Name: Sentiment, Length: 31212, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.unique()"
      ],
      "metadata": {
        "id": "VGAdlsB5e1_y",
        "outputId": "1c24aa3a-d9ff-4c98-d0b6-4295d78f0ead",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 0, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**OHE**"
      ],
      "metadata": {
        "id": "OEnXFUqqe0Ps"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "ohe = tf.keras.layers.CategoryEncoding(num_tokens=len(y_train.unique()), output_mode=\"one_hot\")\n",
        "y_test = ohe(y_test)\n",
        "y_train = ohe(y_train)"
      ],
      "metadata": {
        "id": "XN51VWpGe_sc"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Preprocessing**"
      ],
      "metadata": {
        "id": "B5C9h0TLgmef"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install -qq tensorflow_hub \n",
        "%pip install -qq tensorflow_text"
      ],
      "metadata": {
        "id": "frZKeX8xgoyJ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_hub as hub\n",
        "import tensorflow_text as text"
      ],
      "metadata": {
        "id": "LW8T-O8ch88m"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessor = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\")"
      ],
      "metadata": {
        "id": "H0glgMALjGuG"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def pre_processor(example):\n",
        "  return preprocessor(example)"
      ],
      "metadata": {
        "id": "2ZAH5aSNgwsM"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Encoder**"
      ],
      "metadata": {
        "id": "aSGOkKLtjLP_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4\")"
      ],
      "metadata": {
        "id": "k-28JRDMjOeT"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def encode_input(preprocessed_text):\n",
        "  return encoder(preprocessed_text)"
      ],
      "metadata": {
        "id": "UpjJ8FF9jbe9"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model**"
      ],
      "metadata": {
        "id": "LLl6RIYZjijV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "Ywg4lK71jnJR"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.layers.Input(shape=(), dtype=tf.string, name=\"inputs\")\n",
        "preprocess = pre_processor(inputs)\n",
        "encode = encode_input(preprocess)\n",
        "\n",
        "nn1 = keras.layers.Dropout(0.1, name=\"dropout\")(encode[\"pooled_output\"])\n",
        "nn1 = keras.layers.Dense(5, activation=keras.activations.softmax, name=\"output\")(nn1)\n",
        "\n",
        "model = keras.Model(inputs=[inputs], outputs=[nn1])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "FodMYHc1jkHx",
        "outputId": "416df6fa-4d28-4d78-89d3-29e415feb064",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " inputs (InputLayer)            [(None,)]            0           []                               \n",
            "                                                                                                  \n",
            " keras_layer_4 (KerasLayer)     {'input_word_ids':   0           ['inputs[0][0]']                 \n",
            "                                (None, 128),                                                      \n",
            "                                 'input_mask': (Non                                               \n",
            "                                e, 128),                                                          \n",
            "                                 'input_type_ids':                                                \n",
            "                                (None, 128)}                                                      \n",
            "                                                                                                  \n",
            " keras_layer_5 (KerasLayer)     {'sequence_output':  109482241   ['keras_layer_4[1][0]',          \n",
            "                                 (None, 128, 768),                'keras_layer_4[1][1]',          \n",
            "                                 'encoder_outputs':               'keras_layer_4[1][2]']          \n",
            "                                 [(None, 128, 768),                                               \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768)],                                               \n",
            "                                 'default': (None,                                                \n",
            "                                768),                                                             \n",
            "                                 'pooled_output': (                                               \n",
            "                                None, 768)}                                                       \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 768)          0           ['keras_layer_5[1][13]']         \n",
            "                                                                                                  \n",
            " output (Dense)                 (None, 5)            3845        ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 109,486,086\n",
            "Trainable params: 3,845\n",
            "Non-trainable params: 109,482,241\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Compile Model\n",
        "METRICS = [\n",
        "           tf.keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
        "           tf.keras.metrics.Precision(name=\"precision\"),\n",
        "           tf.keras.metrics.Recall(name=\"recall\"),\n",
        "]\n",
        "\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), \n",
        "              loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "              metrics=METRICS)"
      ],
      "metadata": {
        "id": "ffDCY3VbkPJG"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=2)"
      ],
      "metadata": {
        "id": "C5o3MD8QkdPx",
        "outputId": "522eb7dc-ae96-4ccf-e59f-ac59254b5dd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "   7/3902 [..............................] - ETA: 15:01:51 - loss: 1.8864 - accuracy: 0.7768 - precision: 0.2174 - recall: 0.0446"
          ]
        }
      ]
    }
  ]
}