RoBERTa.ipynb

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      "cell_type": "code",
      "source": [
        "!pip install sentencepiece transformers"
      ],
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        },
        "id": "GEaTXQm5vbHZ",
        "outputId": "7621411b-6322-4598-9227-a2312727bbd4"
      },
      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 5.4 MB/s \n",
            "\u001b[?25hCollecting transformers\n",
            "  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 46.0 MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 60.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 57.1 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
            "\u001b[K     |████████████████████████████████| 67 kB 4.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.10.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 56.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.7.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers, sentencepiece\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.4.0 pyyaml-6.0 sacremoses-0.0.47 sentencepiece-0.1.96 tokenizers-0.10.3 transformers-4.15.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import regex as re\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import statistics\n",
        "import math\n",
        "import os\n",
        "\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras.backend as K\n",
        "from transformers import RobertaTokenizer, TFRobertaModel\n",
        "\n",
        "from collections import Counter\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "Yl7BxhN6vNHF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tokenizers"
      ],
      "metadata": {
        "id": "FJ_gqaA3yMGH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    # TPU detection. No parameters necessary if TPU_NAME environment variable is set (always set in Kaggle)\n",
        "    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n",
        "    tf.config.experimental_connect_to_cluster(tpu)\n",
        "    tf.tpu.experimental.initialize_tpu_system(tpu)\n",
        "    strategy = tf.distribute.experimental.TPUStrategy(tpu)\n",
        "    print('Running on TPU ', tpu.master())\n",
        "except ValueError:\n",
        "    # Default distribution strategy in Tensorflow. Works on CPU and single GPU.\n",
        "    strategy = tf.distribute.get_strategy()\n",
        "\n",
        "print('Number of replicas:', strategy.num_replicas_in_sync)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XHmGlgnGvisu",
        "outputId": "d74c6424-6459-4d57-fb19-596fdceb9736"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:Entering into master device scope: /job:worker/replica:0/task:0/device:CPU:0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.110.202.130:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.110.202.130:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n",
            "WARNING:absl:`tf.distribute.experimental.TPUStrategy` is deprecated, please use  the non experimental symbol `tf.distribute.TPUStrategy` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running on TPU  grpc://10.110.202.130:8470\n",
            "Number of replicas: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = 'roberta-base'\n",
        "MAX_LEN = 256\n",
        "ARTIFACTS_PATH = '../artifacts/'\n",
        "\n",
        "BATCH_SIZE = 8 * strategy.num_replicas_in_sync\n",
        "EPOCHS = 60\n",
        "\n",
        "if not os.path.exists(ARTIFACTS_PATH):\n",
        "    os.makedirs(ARTIFACTS_PATH)"
      ],
      "metadata": {
        "id": "95G_FulIvwnD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df=pd.read_csv(\"/content/FINAL_DATA2.csv\")\n",
        "df.cleanData=df.cleanData.astype(str)"
      ],
      "metadata": {
        "id": "1fQaiFsYv47n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def roberta_encode(texts, tokenizer):\n",
        "    ct = len(texts)\n",
        "    input_ids = np.ones((ct, MAX_LEN), dtype='int32')\n",
        "    attention_mask = np.zeros((ct, MAX_LEN), dtype='int32')\n",
        "    token_type_ids = np.zeros((ct, MAX_LEN), dtype='int32') # Not used in text classification\n",
        "\n",
        "    for k, text in enumerate(texts):\n",
        "        # Tokenize\n",
        "        tok_text = tokenizer.tokenize(text)\n",
        "        \n",
        "        # Truncate and convert tokens to numerical IDs\n",
        "        enc_text = tokenizer.convert_tokens_to_ids(tok_text[:(MAX_LEN-2)])\n",
        "        \n",
        "        input_length = len(enc_text) + 2\n",
        "        input_length = input_length if input_length < MAX_LEN else MAX_LEN\n",
        "        \n",
        "        # Add tokens [CLS] and [SEP] at the beginning and the end\n",
        "        input_ids[k,:input_length] = np.asarray([0] + enc_text + [2], dtype='int32')\n",
        "        \n",
        "        # Set to 1s in the attention input\n",
        "        attention_mask[k,:input_length] = 1\n",
        "\n",
        "    return {\n",
        "        'input_word_ids': input_ids,\n",
        "        'input_mask': attention_mask,\n",
        "        'input_type_ids': token_type_ids\n",
        "    }"
      ],
      "metadata": {
        "id": "KVHasGyWwdE0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df.cleanData,df.search_word, test_size=0.3, random_state=777) "
      ],
      "metadata": {
        "id": "0hptR5m_wlAD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer1 = RobertaTokenizer.from_pretrained(MODEL_NAME)"
      ],
      "metadata": {
        "id": "86_JG_kjwwzD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144,
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      "execution_count": null,
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          "output_type": "display_data",
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        {
          "output_type": "display_data",
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          "metadata": {}
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        {
          "output_type": "display_data",
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    },
    {
      "cell_type": "code",
      "source": [
        "X_train = roberta_encode(X_train, tokenizer1)\n",
        "X_test = roberta_encode(X_test, tokenizer1)\n",
        "\n",
        "y_train = np.asarray(y_train, dtype='int32')\n",
        "y_test = np.asarray(y_test, dtype='int32')"
      ],
      "metadata": {
        "id": "KpvloVxnw4e3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(n_categories):\n",
        "    with strategy.scope():\n",
        "        input_word_ids = tf.keras.Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_word_ids')\n",
        "        input_mask = tf.keras.Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_mask')\n",
        "        input_type_ids = tf.keras.Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_type_ids')\n",
        "\n",
        "        # Import RoBERTa model from HuggingFace\n",
        "        roberta_model = TFRobertaModel.from_pretrained(MODEL_NAME)\n",
        "        x = roberta_model(input_word_ids, attention_mask=input_mask, token_type_ids=input_type_ids)\n",
        "\n",
        "        # Huggingface transformers have multiple outputs, embeddings are the first one,\n",
        "        # so let's slice out the first position\n",
        "        x = x[0]\n",
        "\n",
        "        x = tf.keras.layers.Dropout(0.1)(x)\n",
        "        x = tf.keras.layers.Flatten()(x)\n",
        "        x = tf.keras.layers.Dense(256, activation='relu')(x)\n",
        "        x = tf.keras.layers.Dense(n_categories, activation='softmax')(x)\n",
        "\n",
        "        model = tf.keras.Model(inputs=[input_word_ids, input_mask, input_type_ids], outputs=x)\n",
        "        model.compile(\n",
        "            optimizer=tf.keras.optimizers.Adam(lr=1e-5),\n",
        "            loss='sparse_categorical_crossentropy',\n",
        "            metrics=['accuracy'])\n",
        "\n",
        "        return model"
      ],
      "metadata": {
        "id": "TOcizWYz2IRI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with strategy.scope():\n",
        "    model = build_model(3)\n",
        "    model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825,
          "referenced_widgets": [
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        "id": "bQJttAax2UhZ",
        "outputId": "68e703a7-1f36-40d2-ab0d-16916568eb08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b47785272dc44328ec3eb3b1a8df3e6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at roberta-base were not used when initializing TFRobertaModel: ['lm_head']\n",
            "- This IS expected if you are initializing TFRobertaModel 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 TFRobertaModel 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 TFRobertaModel were initialized from the model checkpoint at roberta-base.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_word_ids (InputLayer)    [(None, 256)]        0           []                               \n",
            "                                                                                                  \n",
            " input_mask (InputLayer)        [(None, 256)]        0           []                               \n",
            "                                                                                                  \n",
            " input_type_ids (InputLayer)    [(None, 256)]        0           []                               \n",
            "                                                                                                  \n",
            " tf_roberta_model (TFRobertaMod  TFBaseModelOutputWi  124645632  ['input_word_ids[0][0]',         \n",
            " el)                            thPoolingAndCrossAt               'input_mask[0][0]',             \n",
            "                                tentions(last_hidde               'input_type_ids[0][0]']         \n",
            "                                n_state=(None, 256,                                               \n",
            "                                 768),                                                            \n",
            "                                 pooler_output=(Non                                               \n",
            "                                e, 768),                                                          \n",
            "                                 past_key_values=No                                               \n",
            "                                ne, hidden_states=N                                               \n",
            "                                one, attentions=Non                                               \n",
            "                                e, cross_attentions                                               \n",
            "                                =None)                                                            \n",
            "                                                                                                  \n",
            " dropout_37 (Dropout)           (None, 256, 768)     0           ['tf_roberta_model[0][0]']       \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 196608)       0           ['dropout_37[0][0]']             \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 256)          50331904    ['flatten[0][0]']                \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 3)            771         ['dense[0][0]']                  \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 174,978,307\n",
            "Trainable params: 174,978,307\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "\n",
        "es = EarlyStopping(monitor = 'val_loss', mode = 'min', verbose = 1, patience = 8)\n",
        "mc = ModelCheckpoint('./model_gensim.h5', monitor = 'val_accuracy', mode = 'max', verbose = 1, save_best_only = True)"
      ],
      "metadata": {
        "id": "fYZ_SAnm3kQt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with strategy.scope():\n",
        "    print('Training...')\n",
        "    history = model.fit(X_train,\n",
        "                        y_train,\n",
        "                        epochs=EPOCHS,\n",
        "                        batch_size=BATCH_SIZE,\n",
        "                        verbose=1,\n",
        "                        validation_data=(X_test, y_test),callbacks= [es, mc])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUTSSC9J2rzZ",
        "outputId": "689b619f-129d-45c4-f5a8-c0b774fe278d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training...\n",
            "Epoch 1/60\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:TPU has inputs with dynamic shapes: [<tf.Tensor 'Const:0' shape=() dtype=int32>, <tf.Tensor 'cond/Identity:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_8:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_16:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_24:0' shape=(None,) dtype=int32>]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model/roberta/pooler/dense/kernel:0', 'tf_roberta_model/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model/roberta/pooler/dense/kernel:0', 'tf_roberta_model/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:2970: StrategyBase.unwrap (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "use `experimental_local_results` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:2970: StrategyBase.unwrap (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "use `experimental_local_results` instead.\n",
            "INFO:absl:TPU has inputs with dynamic shapes: [<tf.Tensor 'Const:0' shape=() dtype=int32>, <tf.Tensor 'cond/Identity:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_8:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_16:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_24:0' shape=(None,) dtype=int32>]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model/roberta/pooler/dense/kernel:0', 'tf_roberta_model/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model/roberta/pooler/dense/kernel:0', 'tf_roberta_model/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "27/27 [==============================] - ETA: 0s - loss: 0.4294 - accuracy: 0.8537"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:TPU has inputs with dynamic shapes: [<tf.Tensor 'Const:0' shape=() dtype=int32>, <tf.Tensor 'cond/Identity:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_8:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_16:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_24:0' shape=(None,) dtype=int32>]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.93775, saving model to ./model_gensim.h5\n",
            "27/27 [==============================] - 124s 2s/step - loss: 0.4294 - accuracy: 0.8537 - val_loss: 0.1922 - val_accuracy: 0.9378\n",
            "Epoch 2/60\n",
            "27/27 [==============================] - ETA: 0s - loss: 0.1635 - accuracy: 0.9460\n",
            "Epoch 00002: val_accuracy improved from 0.93775 to 0.94858, saving model to ./model_gensim.h5\n",
            "27/27 [==============================] - 53s 2s/step - loss: 0.1635 - accuracy: 0.9460 - val_loss: 0.1603 - val_accuracy: 0.9486\n",
            "Epoch 3/60\n",
            "27/27 [==============================] - ETA: 0s - loss: 0.0874 - accuracy: 0.9698\n",
            "Epoch 00003: val_accuracy improved from 0.94858 to 0.95535, saving model to ./model_gensim.h5\n",
            "27/27 [==============================] - 47s 2s/step - loss: 0.0874 - accuracy: 0.9698 - val_loss: 0.1713 - val_accuracy: 0.9553\n",
            "Epoch 4/60\n",
            "27/27 [==============================] - ETA: 0s - loss: 0.0453 - accuracy: 0.9867\n",
            "Epoch 00004: val_accuracy did not improve from 0.95535\n",
            "27/27 [==============================] - 6s 215ms/step - loss: 0.0453 - accuracy: 0.9867 - val_loss: 0.2091 - val_accuracy: 0.9459\n",
            "Epoch 5/60\n",
            "27/27 [==============================] - ETA: 0s - loss: 0.0431 - accuracy: 0.9867\n",
            "Epoch 00005: val_accuracy did not improve from 0.95535\n",
            "27/27 [==============================] - 6s 214ms/step - loss: 0.0431 - accuracy: 0.9867 - val_loss: 0.2261 - val_accuracy: 0.9540\n",
            "Epoch 6/60\n",
            "27/27 [==============================] - ETA: 0s - loss: 0.0278 - accuracy: 0.9913\n",
            "Epoch 00006: val_accuracy improved from 0.95535 to 0.95670, saving model to ./model_gensim.h5\n",
            "27/27 [==============================] - 34s 1s/step - loss: 0.0278 - accuracy: 0.9913 - val_loss: 0.1952 - val_accuracy: 0.9567\n",
            "Epoch 7/60\n",
            "27/27 [==============================] - ETA: 0s - loss: 0.0089 - accuracy: 0.9971\n",
            "Epoch 00007: val_accuracy did not improve from 0.95670\n",
            "27/27 [==============================] - 6s 212ms/step - loss: 0.0089 - accuracy: 0.9971 - val_loss: 0.2572 - val_accuracy: 0.9553\n",
            "Epoch 8/60\n",
            "27/27 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9994\n",
            "Epoch 00008: val_accuracy did not improve from 0.95670\n",
            "27/27 [==============================] - 6s 211ms/step - loss: 0.0049 - accuracy: 0.9994 - val_loss: 0.2636 - val_accuracy: 0.9567\n",
            "Epoch 9/60\n",
            "27/27 [==============================] - ETA: 0s - loss: 0.0045 - accuracy: 0.9994\n",
            "Epoch 00009: val_accuracy did not improve from 0.95670\n",
            "27/27 [==============================] - 6s 214ms/step - loss: 0.0045 - accuracy: 0.9994 - val_loss: 0.2895 - val_accuracy: 0.9540\n",
            "Epoch 10/60\n",
            "27/27 [==============================] - ETA: 0s - loss: 0.0013 - accuracy: 1.0000\n",
            "Epoch 00010: val_accuracy improved from 0.95670 to 0.95940, saving model to ./model_gensim.h5\n",
            "27/27 [==============================] - 32s 1s/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.3357 - val_accuracy: 0.9594\n",
            "Epoch 00010: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.title('Accuracy')\n",
        "\n",
        "xaxis = np.arange(len(history.history['accuracy']))\n",
        "plt.plot(xaxis, history.history['accuracy'], label='Train set')\n",
        "plt.plot(xaxis, history.history['val_accuracy'], label='Validation set')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "XeZOF8Jh3Z0y",
        "outputId": "ab46bf7b-0b46-40e0-945f-5e143a8b28e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5af5a5dcd0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "y_pred=model.predict(X_test)\n",
        "predicted = np.argmax(y_pred, axis=1)\n",
        "report = classification_report(y_test, predicted)\n",
        "print(report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUrFpho8jBhU",
        "outputId": "263b85c9-88d0-469d-cfe7-18afee4bc783"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:TPU has inputs with dynamic shapes: [<tf.Tensor 'Const:0' shape=() dtype=int32>, <tf.Tensor 'cond/Identity:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_8:0' shape=(None, 256) dtype=int32>, <tf.Tensor 'cond/Identity_16:0' shape=(None, 256) dtype=int32>]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.79      0.85       101\n",
            "           1       0.96      0.99      0.98       628\n",
            "           2       1.00      0.60      0.75        10\n",
            "\n",
            "    accuracy                           0.96       739\n",
            "   macro avg       0.96      0.79      0.86       739\n",
            "weighted avg       0.96      0.96      0.96       739\n",
            "\n"
          ]
        }
      ]
    }
  ]
}