ParsBert-deep-network.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Ua3lGFLk2lbJ"
      },
      "outputs": [],
      "source": [
        "SEQ_LEN = 64\n",
        "BATCH_SIZE = 256"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ZYlbA6OR0xZd"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('cleaned-text-version-5.csv') \n",
        "df = df[['text', 'label']]\n",
        "pd.set_option('display.max_colwidth', None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "fV4lD2g_1lif"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_data, test_data = train_test_split(df, test_size=0.25, random_state=50, shuffle=True)\n",
        "val_data, test_data = train_test_split(test_data, test_size=0.5, random_state=50, shuffle=True)"
      ]
    },
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        "id": "yDzwep0k1_OV",
        "outputId": "9b7769b1-ac24-443a-992e-af232628c214"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoConfig, AutoTokenizer\n",
        "\n",
        "parsBert_base_model_path = 'HooshvareLab/bert-fa-zwnj-base' \n",
        "config = AutoConfig.from_pretrained(parsBert_base_model_path)\n",
        "tokenizer = AutoTokenizer.from_pretrained(parsBert_base_model_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "h-TDr0JS2h8L"
      },
      "outputs": [],
      "source": [
        "tokens = tokenizer(train_data['text'].tolist(), max_length=SEQ_LEN,\n",
        "                  truncation=True, padding='max_length',\n",
        "                  add_special_tokens=True,\n",
        "                  return_tensors='tf')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "vXlJl-uyvvLe"
      },
      "outputs": [],
      "source": [
        "val_data_tokens = tokenizer(val_data['text'].tolist(), max_length=SEQ_LEN,\n",
        "                  truncation=True, padding='max_length',\n",
        "                  add_special_tokens=True,\n",
        "                  return_tensors='tf')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "wHSmMakE4Kcn"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "mlb = MultiLabelBinarizer()\n",
        "labels = mlb.fit_transform([(x,) for x in train_data['label']])\n",
        "val_labels = mlb.fit_transform([(x,) for x in val_data['label']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "SbHAH4pPDqY_"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "def map_func(input_ids, attention_mask, labels):\n",
        "    return ({'input_ids': input_ids,\n",
        "            'attention_mask': attention_mask}, labels)\n",
        "\n",
        "# train_size = int((tokens['input_ids'].shape[0] / BATCH_SIZE) * 0.8)\n",
        "\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((tokens['input_ids'], tokens['attention_mask'], labels))\n",
        "train_dataset = train_dataset.map(map_func)\n",
        "train_dataset = train_dataset.shuffle(10000).batch(BATCH_SIZE, drop_remainder=True)\n",
        "\n",
        "\n",
        "val_dataset = tf.data.Dataset.from_tensor_slices((val_data_tokens['input_ids'], val_data_tokens['attention_mask'], val_labels))\n",
        "val_dataset = val_dataset.map(map_func)\n",
        "val_dataset = val_dataset.shuffle(10000).batch(BATCH_SIZE, drop_remainder=True)"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
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        "outputId": "7ed4c249-4375-4966-c5ad-9e2441374cda"
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      "outputs": [],
      "source": [
        "from transformers import TFAutoModel, TFBertForSequenceClassification\n",
        "parsBert = TFAutoModel.from_pretrained(parsBert_base_model_path)\n",
        "\n",
        "parsBert_sentiment_model_path = 'HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi'\n",
        "sementicModel = TFBertForSequenceClassification.from_pretrained(parsBert_sentiment_model_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "BAZqYjgy5Opm"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import Attention_Class as Attention\n",
        "\n",
        "input_ids = tf.keras.layers.Input(shape=(SEQ_LEN,), name='input_ids', dtype='int32')\n",
        "mask = tf.keras.layers.Input(shape=(SEQ_LEN,), name='attention_mask', dtype='int32')\n",
        "\n",
        "embeddings = parsBert.bert(input_ids, attention_mask=mask)  \n",
        "\n",
        "CNN=tf.keras.layers.Conv1D(128, 3, activation='relu')(embeddings[0])\n",
        "Max_Pool=tf.keras.layers.MaxPooling1D(pool_size=2)(CNN)\n",
        "\n",
        "LSTM= tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=True))(Max_Pool)\n",
        "LSTM_Drop = tf.keras.layers.Dropout(0.3)(LSTM)\n",
        "# LSTM_Drop_concat = tf.keras.layers.concatenate([LSTM_Drop, Max_Pool])\n",
        "atten_lstm= Attention.attention()(LSTM_Drop)\n",
        "# atten_lstm= tf.keras.layers.Attention()(input_ids,mask)\n",
        "\n",
        "GRU1= tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64, return_sequences=True, go_backwards=True))(Max_Pool)\n",
        "GRU1_Drop = tf.keras.layers.Dropout(0.3)(GRU1)\n",
        "# GRU1_Drop_concat = tf.keras.layers.concatenate([GRU1_Drop, Max_Pool])\n",
        "atten_gru= Attention.attention()(GRU1_Drop)\n",
        "# atten_gru = tf.keras.layers.Attention()(input_ids,mask)\n",
        "\n",
        "# flatten_embeddings = tf.keras.layers.Flatten()(embeddings[0]), flatten_embeddings\n",
        "atten_merged = tf.keras.layers.concatenate([atten_lstm, atten_gru])\n",
        "flatten_parsbase = tf.keras.layers.Flatten()(atten_merged)\n",
        "\n",
        "x = tf.keras.layers.Dense(32, activation='relu')(flatten_parsbase)\n",
        "x = tf.keras.layers.Dense(32, activation='relu')(x)\n",
        "x = tf.keras.layers.Dropout(0.2)(x)\n",
        "# x = tf.keras.layers.Dense(16, activation='relu')(x)\n",
        "# x = tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "semanticAnswer = sementicModel([input_ids, mask]).logits\n",
        "flatten_sent = tf.keras.layers.Flatten()(semanticAnswer)\n",
        "\n",
        "x = tf.keras.layers.concatenate([flatten_sent, x])\n",
        "x = tf.keras.layers.Dense(8, activation='relu')(x)#, kernel_regularizer=tf.keras.regularizers.l1()\n",
        "\n",
        "Final_output = tf.keras.layers.Dense(2, activation ='linear', trainable=True)(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "HGaIcggS-p1L"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Model(inputs=[input_ids, mask], outputs=Final_output)\n",
        "for l in model.layers:\n",
        "  if 'bert' in l.name:\n",
        "    l.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4feCi_t_M-b",
        "outputId": "959638d9-b812-4f80-c7ab-bb2c3d64e38c"
      },
      "outputs": [],
      "source": [
        "for l in model.layers:\n",
        "    print(l.name, l.trainable)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kMlHPIv7-tK9",
        "outputId": "e9e0ba48-87e1-464f-d7dd-293b791d4fb6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_ids (InputLayer)         [(None, 64)]         0           []                               \n",
            "                                                                                                  \n",
            " attention_mask (InputLayer)    [(None, 64)]         0           []                               \n",
            "                                                                                                  \n",
            " bert (TFBertMainLayer)         TFBaseModelOutputWi  118297344   ['input_ids[0][0]',              \n",
            "                                thPoolingAndCrossAt               'attention_mask[0][0]']         \n",
            "                                tentions(last_hidde                                               \n",
            "                                n_state=(None, 64,                                                \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",
            " conv1d (Conv1D)                (None, 62, 128)      295040      ['bert[0][0]']                   \n",
            "                                                                                                  \n",
            " max_pooling1d (MaxPooling1D)   (None, 31, 128)      0           ['conv1d[0][0]']                 \n",
            "                                                                                                  \n",
            " bidirectional (Bidirectional)  (None, 31, 128)      98816       ['max_pooling1d[0][0]']          \n",
            "                                                                                                  \n",
            " bidirectional_1 (Bidirectional  (None, 31, 128)     74496       ['max_pooling1d[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " dropout_75 (Dropout)           (None, 31, 128)      0           ['bidirectional[0][0]']          \n",
            "                                                                                                  \n",
            " dropout_76 (Dropout)           (None, 31, 128)      0           ['bidirectional_1[0][0]']        \n",
            "                                                                                                  \n",
            " attention (attention)          (None, 128)          159         ['dropout_75[0][0]']             \n",
            "                                                                                                  \n",
            " attention_1 (attention)        (None, 128)          159         ['dropout_76[0][0]']             \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 256)          0           ['attention[0][0]',              \n",
            "                                                                  'attention_1[0][0]']            \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 256)          0           ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 32)           8224        ['flatten[0][0]']                \n",
            "                                                                                                  \n",
            " tf_bert_for_sequence_classific  TFSequenceClassifie  162845189  ['input_ids[0][0]',              \n",
            " ation (TFBertForSequenceClassi  rOutput(loss=None,               'attention_mask[0][0]']         \n",
            " fication)                      logits=(None, 5),                                                 \n",
            "                                 hidden_states=None                                               \n",
            "                                , attentions=None)                                                \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 32)           1056        ['dense[0][0]']                  \n",
            "                                                                                                  \n",
            " flatten_1 (Flatten)            (None, 5)            0           ['tf_bert_for_sequence_classifica\n",
            "                                                                 tion[0][0]']                     \n",
            "                                                                                                  \n",
            " dropout_77 (Dropout)           (None, 32)           0           ['dense_1[0][0]']                \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate)    (None, 37)           0           ['flatten_1[0][0]',              \n",
            "                                                                  'dropout_77[0][0]']             \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 8)            304         ['concatenate_1[0][0]']          \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 2)            18          ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 281,620,805\n",
            "Trainable params: 478,272\n",
            "Non-trainable params: 281,142,533\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "7ef8cgK4_lmG"
      },
      "outputs": [],
      "source": [
        "optimizer = tf.keras.optimizers.Adam() #learning_rate=1e-4, decay=1e-6\n",
        "loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "acc = tf.keras.metrics.CategoricalAccuracy('accuracy')\n",
        "\n",
        "model.compile(optimizer=optimizer, loss=loss, metrics=[acc])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "fIbX7uJx_2FY"
      },
      "outputs": [],
      "source": [
        "checkpoint_filepath = 'checkpoint/checkpoint'\n",
        "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath=checkpoint_filepath,\n",
        "    save_weights_only=True,\n",
        "    monitor='val_loss',\n",
        "    mode='min',\n",
        "    save_best_only=True)\n",
        "\n",
        "history = tf.keras.callbacks.History()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iU6mzkYwAgJd",
        "outputId": "f297e2f0-7be8-4d54-985c-0385cfc2c171"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.4784 - accuracy: 0.7822 - val_loss: 0.5529 - val_accuracy: 0.7617\n",
            "Epoch 2/10\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.4509 - accuracy: 0.7900 - val_loss: 0.5805 - val_accuracy: 0.7656\n",
            "Epoch 3/10\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.4374 - accuracy: 0.7910 - val_loss: 0.6283 - val_accuracy: 0.6875\n",
            "Epoch 4/10\n",
            "8/8 [==============================] - 20s 3s/step - loss: 0.4231 - accuracy: 0.8037 - val_loss: 0.6084 - val_accuracy: 0.7383\n",
            "Epoch 5/10\n",
            "8/8 [==============================] - 22s 3s/step - loss: 0.3825 - accuracy: 0.8315 - val_loss: 0.6905 - val_accuracy: 0.7266\n",
            "Epoch 6/10\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.3631 - accuracy: 0.8384 - val_loss: 0.6847 - val_accuracy: 0.7305\n",
            "Epoch 7/10\n",
            "8/8 [==============================] - 22s 3s/step - loss: 0.3383 - accuracy: 0.8574 - val_loss: 0.6841 - val_accuracy: 0.7500\n",
            "Epoch 8/10\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.2852 - accuracy: 0.8838 - val_loss: 0.7065 - val_accuracy: 0.7344\n",
            "Epoch 9/10\n",
            "8/8 [==============================] - 20s 3s/step - loss: 0.2813 - accuracy: 0.8818 - val_loss: 0.8583 - val_accuracy: 0.7227\n",
            "Epoch 10/10\n",
            "8/8 [==============================] - 20s 3s/step - loss: 0.2534 - accuracy: 0.9019 - val_loss: 0.6748 - val_accuracy: 0.7539\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_dataset, validation_data=val_dataset, epochs=10, callbacks=history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "lY64ShIrUD66",
        "outputId": "df660cb5-64c6-43e8-f3d1-c783905328fe"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Epochs')"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rcParams[\"font.weight\"] = \"bold\"\n",
        "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
        "plt.figure(figsize = (5,5))\n",
        "plt.grid(linestyle = 'dashed')\n",
        "plt.plot(history.history['accuracy'][:], c = 'green')\n",
        "plt.plot(history.history['val_accuracy'][:])\n",
        "plt.title('Accuracy Metric with respect to RCNN-parsBert')\n",
        "plt.legend(['Train Set', 'Validation Set'])\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epochs')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "Uo7SONW3UJrX",
        "outputId": "19ce9e9b-efe4-4d0f-cf2f-f6302e17c40e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Epochs')"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# \"Loss\"\n",
        "plt.rcParams[\"font.weight\"] = \"bold\"\n",
        "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
        "plt.figure(figsize = (5,5))\n",
        "plt.grid(linestyle = 'dashed')\n",
        "plt.plot(history.history['loss'][:], c = 'green')\n",
        "plt.plot(history.history['val_loss'][:])\n",
        "plt.title('Cross Entropy Loss with respect to RCNN-parsBert')\n",
        "plt.legend(['Train Set', 'Validation Set'])\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XG8OXfGmN1RS",
        "outputId": "b2f837e5-d4c8-47bd-cbf1-e8c9bee5474b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7fa8c4199c50>"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.load_weights('checkpoint/checkpoint')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "9K2vMf3xO0Do"
      },
      "outputs": [],
      "source": [
        "def prep_data(text):\n",
        "    tokens = tokenizer(text, max_length=SEQ_LEN, truncation=True, padding='max_length', add_special_tokens=True, return_tensors='tf')\n",
        "    return ({'input_ids': tokens['input_ids'], 'attention_mask': tokens['attention_mask']})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "O_SXeekhO8I_",
        "outputId": "359f86d0-33be-43b3-c546-249e37e8b50e"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "test_data['predicted-label'] = None\n",
        "\n",
        "for i, text in zip(test_data['number'], test_data['text']):\n",
        "    tokens = prep_data(text)\n",
        "    probs = model.predict(tokens)\n",
        "    pred = np.argmax(probs)\n",
        "    test_data['predicted-label'][i] = pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3N6PcJMP1OE",
        "outputId": "8a061e55-e77b-49b9-e894-3a552cbe0283"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TEST:\n",
            "recall : 0.5576923076923077\n",
            "percision : 0.7131147540983607\n",
            "f1-score : 0.6258992805755396\n",
            "accuracy : 0.717391304347826\n"
          ]
        }
      ],
      "source": [
        "test_true_positive = 0\n",
        "test_true_negetive = 0\n",
        "test_false_positive = 0\n",
        "test_false_negetive = 0\n",
        "\n",
        "for p,g in zip(test_data['predicted-label'],test_data['label']):\n",
        "    test_true_negetive += 1 if (p == 0 and g == 0) else 0\n",
        "    test_true_positive += 1 if (p == 1 and g == 1) else 0\n",
        "    test_false_negetive += 1 if (p == 0 and g == 1) else 0\n",
        "    test_false_positive += 1 if (p == 1 and g == 0) else 0\n",
        "\n",
        "\n",
        "test_recall = test_true_positive/ (test_true_positive+test_false_negetive)\n",
        "test_percision = test_true_positive/ (test_true_positive+test_false_positive)\n",
        "test_f1 = (2*test_percision*test_recall) / (test_percision+test_recall)\n",
        "test_accuracy = (test_true_positive + test_true_negetive) / len(test_data)\n",
        "\n",
        "print('TEST:')\n",
        "print('recall : {}'.format(test_recall))\n",
        "print('percision : {}'.format(test_percision))\n",
        "print('f1-score : {}'.format(test_f1))\n",
        "print('accuracy : {}'.format(test_accuracy))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pvqGWLb2SJB",
        "outputId": "f2fc2104-dc3a-49ec-b7ba-cfaaa612555f"
      },
      "outputs": [],
      "source": [
        "val_data['predicted-label'] = None\n",
        "\n",
        "for i, text in zip(val_data['number'], val_data['text']):\n",
        "    tokens = prep_data(text)\n",
        "    probs = model.predict(tokens)\n",
        "    pred = np.argmax(probs)\n",
        "    val_data['predicted-label'][i] = pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lRBbQjv2Zun",
        "outputId": "1c810e8a-5221-41d4-943c-c123b5d197c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "VALIDATION:\n",
            "recall : 0.8317757009345794\n",
            "percision : 0.536144578313253\n",
            "f1-score : 0.652014652014652\n",
            "accuracy : 0.7418478260869565\n"
          ]
        }
      ],
      "source": [
        "val_true_positive = 0\n",
        "val_true_negetive = 0\n",
        "val_false_positive = 0\n",
        "val_false_negetive = 0\n",
        "\n",
        "for p,g in zip(val_data['label'],val_data['predicted-label']):\n",
        "    val_true_negetive += 1 if (p == 0 and g == 0) else 0\n",
        "    val_true_positive += 1 if (p == 1 and g == 1) else 0\n",
        "    val_false_negetive += 1 if (p == 0 and g == 1) else 0\n",
        "    val_false_positive += 1 if (p == 1 and g == 0) else 0\n",
        "\n",
        "\n",
        "val_recall = val_true_positive/ (val_true_positive+val_false_negetive)\n",
        "val_percision = val_true_positive/ (val_true_positive+val_false_positive)\n",
        "val_f1 = (2*val_percision*val_recall) / (val_percision+val_recall)\n",
        "val_accuracy = (val_true_positive + val_true_negetive) / len(val_data)\n",
        "\n",
        "print('VALIDATION:')\n",
        "print('recall : {}'.format(val_recall))\n",
        "print('percision : {}'.format(val_percision))\n",
        "print('f1-score : {}'.format(val_f1))\n",
        "print('accuracy : {}'.format(val_accuracy))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jUGc2f3xRbQ4"
      },
      "outputs": [],
      "source": [
        "train_data['predicted-label'] = None\n",
        "\n",
        "for i, text in zip(train_data['number'], train_data['text']):\n",
        "    tokens = prep_data(text)\n",
        "    probs = model.predict(tokens)\n",
        "    pred = np.argmax(probs)\n",
        "    train_data['predicted-label'][i] = pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIximkchS1R2",
        "outputId": "0ab9dccc-4bfe-4754-c2fc-b8c06abe2d9d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TRAIN:\n",
            "recall : 1.0\n",
            "percision : 1.0\n",
            "f1-score : 1.0\n",
            "accuracy : 1.0\n"
          ]
        }
      ],
      "source": [
        "train_true_positive = 0\n",
        "train_true_negetive = 0\n",
        "train_false_positive = 0\n",
        "train_false_negetive = 0\n",
        "\n",
        "for p,g in zip(train_data['label'],train_data['predicted-label']):\n",
        "    train_true_negetive += 1 if (p == 0 and g == 0) else 0\n",
        "    train_true_positive += 1 if (p == 1 and g == 1) else 0\n",
        "    train_false_negetive += 1 if (p == 0 and g == 1) else 0\n",
        "    train_false_positive += 1 if (p == 1 and g == 0) else 0\n",
        "\n",
        "\n",
        "train_recall = train_true_positive/ (train_true_positive+train_false_negetive)\n",
        "train_percision = train_true_positive/ (train_true_positive+train_false_positive)\n",
        "train_f1 = (2*train_percision*train_recall) / (train_percision+train_recall)\n",
        "train_accuracy = (train_true_positive + train_true_negetive) / len(train_data)\n",
        "\n",
        "print('TRAIN:')\n",
        "print('recall : {}'.format(train_recall))\n",
        "print('percision : {}'.format(train_percision))\n",
        "print('f1-score : {}'.format(train_f1))\n",
        "print('accuracy : {}'.format(train_accuracy))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdVUpBy0TX8V",
        "outputId": "46008195-6b1d-4439-f503-b11b1d4e79f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ALL:\n",
            "recall : 0.9812206572769953\n",
            "percision : 0.9819890368050117\n",
            "f1-score : 0.9816046966731897\n",
            "accuracy : 0.9840190411424685\n"
          ]
        }
      ],
      "source": [
        "all_true_positive = train_true_positive + test_true_positive\n",
        "all_true_negetive = train_true_negetive + test_true_negetive\n",
        "all_false_positive = train_false_positive + test_false_positive\n",
        "all_false_negetive = train_false_negetive + test_false_negetive\n",
        "all_count = len(test_data) + len (train_data)\n",
        "\n",
        "all_recall = all_true_positive/ (all_true_positive+all_false_negetive)\n",
        "all_percision = all_true_positive/ (all_true_positive+all_false_positive)\n",
        "all_f1 = (2*all_percision*all_recall) / (all_percision+all_recall)\n",
        "all_accuracy = (all_true_positive + all_true_negetive) / all_count\n",
        "\n",
        "print('ALL:')\n",
        "print('recall : {}'.format(all_recall))\n",
        "print('percision : {}'.format(all_percision))\n",
        "print('f1-score : {}'.format(all_f1))\n",
        "print('accuracy : {}'.format(all_accuracy))\n",
        "\n"
      ]
    }
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