SentimentAnalysisUsingImdb.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Rakshith-Karthikeya/Sentiment-Analysis/blob/main/SentimentAnalysisUsingImdb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "0d106a43-d131-41df-f6f3-73f3f3e617de"
      },
      "source": [
        "pip install transformers"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/88/b1/41130a228dd656a1a31ba281598a968320283f48d42782845f6ba567f00b/transformers-4.2.2-py3-none-any.whl (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 7.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.8)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
            "Collecting tokenizers==0.9.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0f/1c/e789a8b12e28be5bc1ce2156cf87cb522b379be9cadc7ad8091a4cc107c4/tokenizers-0.9.4-cp36-cp36m-manylinux2010_x86_64.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 34.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from transformers) (3.4.0)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 49.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.0.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=7043a4e0eee649ad20c7e774e97d9506614444b8c52cb0f187b2d4d723e9b581\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.43 tokenizers-0.9.4 transformers-4.2.2\n"
          ],
          "name": "stdout"
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    {
      "cell_type": "code",
      "metadata": {
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          "height": 230,
          "referenced_widgets": [
            "11bbfd489b06415a9f51777a87863a60",
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            "a7bf62d71b174ff4bbb600c529ff468f"
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        "id": "lwRr3Q6lDM1m",
        "outputId": "35adc156-efba-42e9-8b71-089bfb3563bb"
      },
      "source": [
        "from transformers import BertTokenizer, TFBertForSequenceClassification\r\n",
        "from transformers import InputExample, InputFeatures\r\n",
        "\r\n",
        "model = TFBertForSequenceClassification.from_pretrained(\"bert-base-uncased\")\r\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
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              "model_id": "11bbfd489b06415a9f51777a87863a60",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8c408b1ea1e41c7977392450043ed1a",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=536063208.0, style=ProgressStyle(descri…"
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVpnwWgnD1Ik",
        "outputId": "f145a255-1914-4444-f1bf-081c9d643b76"
      },
      "source": [
        "model.summary()\r\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"tf_bert_for_sequence_classification\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "bert (TFBertMainLayer)       multiple                  109482240 \n",
            "_________________________________________________________________\n",
            "dropout_37 (Dropout)         multiple                  0         \n",
            "_________________________________________________________________\n",
            "classifier (Dense)           multiple                  1538      \n",
            "=================================================================\n",
            "Total params: 109,483,778\n",
            "Trainable params: 109,483,778\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKLtB6HsEGKE"
      },
      "source": [
        "import tensorflow as tf\r\n",
        "import pandas as pd"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Pcl0vL1ENg8",
        "outputId": "64015ca3-1660-4c14-fc6a-8cfef65b3743"
      },
      "source": [
        "URL = \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\"\r\n",
        "\r\n",
        "dataset = tf.keras.utils.get_file(fname=\"aclImdb_v1.tar.gz\", \r\n",
        "                                  origin=URL,\r\n",
        "                                  untar=True,\r\n",
        "                                  cache_dir='.',\r\n",
        "                                  cache_subdir='')\r\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
            "84131840/84125825 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1B1l7LLEYpk",
        "outputId": "a98fdea0-ec9f-4922-c6cf-ffa55fe3629e"
      },
      "source": [
        "# The shutil module offers a number of high-level \r\n",
        "# operations on files and collections of files.\r\n",
        "import os\r\n",
        "import shutil\r\n",
        "# Create main directory path (\"/aclImdb\")\r\n",
        "main_dir = os.path.join(os.path.dirname(dataset), 'aclImdb')\r\n",
        "# Create sub directory path (\"/aclImdb/train\")\r\n",
        "train_dir = os.path.join(main_dir, 'train')\r\n",
        "# Remove unsup folder since this is a supervised learning task\r\n",
        "remove_dir = os.path.join(train_dir, 'unsup')\r\n",
        "shutil.rmtree(remove_dir)\r\n",
        "# View the final train folder\r\n",
        "print(os.listdir(train_dir))\r\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['neg', 'unsupBow.feat', 'urls_unsup.txt', 'urls_neg.txt', 'urls_pos.txt', 'labeledBow.feat', 'pos']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mas-N82NEvsL",
        "outputId": "6cd0bc8c-4712-469c-f4a8-e88aa332d865"
      },
      "source": [
        "# We create a training dataset and a validation \r\n",
        "# dataset from our \"aclImdb/train\" directory with a 80/20 split.\r\n",
        "train = tf.keras.preprocessing.text_dataset_from_directory(\r\n",
        "    'aclImdb/train', batch_size=30000, validation_split=0.2, \r\n",
        "    subset='training', seed=123)\r\n",
        "test = tf.keras.preprocessing.text_dataset_from_directory(\r\n",
        "    'aclImdb/train', batch_size=30000, validation_split=0.2, \r\n",
        "    subset='validation', seed=123)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 25000 files belonging to 2 classes.\n",
            "Using 20000 files for training.\n",
            "Found 25000 files belonging to 2 classes.\n",
            "Using 5000 files for validation.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "vRAgBTVNHqnK",
        "outputId": "d5a4b120-6315-47e7-c479-c566f17e6294"
      },
      "source": [
        "for i in train.take(1):\r\n",
        "  train_feat = i[0].numpy()\r\n",
        "  train_lab = i[1].numpy()\r\n",
        "\r\n",
        "train = pd.DataFrame([train_feat, train_lab]).T\r\n",
        "train.columns = ['DATA_COLUMN', 'LABEL_COLUMN']\r\n",
        "train['DATA_COLUMN'] = train['DATA_COLUMN'].str.decode(\"utf-8\")\r\n",
        "train.head()\r\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                         DATA_COLUMN LABEL_COLUMN\n",
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        "outputId": "378bb794-9943-4859-d86a-03b96c3e8a28"
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      "source": [
        "for j in test.take(1):\r\n",
        "  test_feat = j[0].numpy()\r\n",
        "  test_lab = j[1].numpy()\r\n",
        "\r\n",
        "test = pd.DataFrame([test_feat, test_lab]).T\r\n",
        "test.columns = ['DATA_COLUMN', 'LABEL_COLUMN']\r\n",
        "test['DATA_COLUMN'] = test['DATA_COLUMN'].str.decode(\"utf-8\")\r\n",
        "test.head()\r\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>This movie blows - let's get that straight rig...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The saddest thing about this \"tribute\" is that...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I'm only rating this film as a 3 out of pity b...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Something surprised me about this movie - it w...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         DATA_COLUMN LABEL_COLUMN\n",
              "0  I can't believe that so much talent can be was...            0\n",
              "1  This movie blows - let's get that straight rig...            0\n",
              "2  The saddest thing about this \"tribute\" is that...            0\n",
              "3  I'm only rating this film as a 3 out of pity b...            0\n",
              "4  Something surprised me about this movie - it w...            1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjP2vrX5HwsB",
        "outputId": "408a13a6-1f93-48ef-89a0-cd28b6311074"
      },
      "source": [
        "InputExample(guid=None,\r\n",
        "             text_a = \"Hello, world\",\r\n",
        "             text_b = None,\r\n",
        "             label = 1)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InputExample(guid=None, text_a='Hello, world', text_b=None, label=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5OgeU9CIHlz"
      },
      "source": [
        "def convert_data_to_examples(train, test, DATA_COLUMN, LABEL_COLUMN): \r\n",
        "  train_InputExamples = train.apply(lambda x: InputExample(guid=None, # Globally unique ID for bookkeeping, unused in this case\r\n",
        "                                                          text_a = x[DATA_COLUMN], \r\n",
        "                                                          text_b = None,\r\n",
        "                                                          label = x[LABEL_COLUMN]), axis = 1)\r\n",
        "\r\n",
        "  validation_InputExamples = test.apply(lambda x: InputExample(guid=None, # Globally unique ID for bookkeeping, unused in this case\r\n",
        "                                                          text_a = x[DATA_COLUMN], \r\n",
        "                                                          text_b = None,\r\n",
        "                                                          label = x[LABEL_COLUMN]), axis = 1)\r\n",
        "  \r\n",
        "  return train_InputExamples, validation_InputExamples\r\n",
        "\r\n",
        "  train_InputExamples, validation_InputExamples = convert_data_to_examples(train, \r\n",
        "                                                                           test, \r\n",
        "                                                                           'DATA_COLUMN', \r\n",
        "                                                                           'LABEL_COLUMN')\r\n",
        "  \r\n",
        "def convert_examples_to_tf_dataset(examples, tokenizer, max_length=128):\r\n",
        "    features = [] # -> will hold InputFeatures to be converted later\r\n",
        "\r\n",
        "    for e in examples:\r\n",
        "        # Documentation is really strong for this method, so please take a look at it\r\n",
        "        input_dict = tokenizer.encode_plus(\r\n",
        "            e.text_a,\r\n",
        "            add_special_tokens=True,\r\n",
        "            max_length=max_length, # truncates if len(s) > max_length\r\n",
        "            return_token_type_ids=True,\r\n",
        "            return_attention_mask=True,\r\n",
        "            pad_to_max_length=True, # pads to the right by default # CHECK THIS for pad_to_max_length\r\n",
        "            truncation=True\r\n",
        "        )\r\n",
        "\r\n",
        "        input_ids, token_type_ids, attention_mask = (input_dict[\"input_ids\"],\r\n",
        "            input_dict[\"token_type_ids\"], input_dict['attention_mask'])\r\n",
        "\r\n",
        "        features.append(\r\n",
        "            InputFeatures(\r\n",
        "                input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, label=e.label\r\n",
        "            )\r\n",
        "        )\r\n",
        "\r\n",
        "    def gen():\r\n",
        "        for f in features:\r\n",
        "            yield (\r\n",
        "                {\r\n",
        "                    \"input_ids\": f.input_ids,\r\n",
        "                    \"attention_mask\": f.attention_mask,\r\n",
        "                    \"token_type_ids\": f.token_type_ids,\r\n",
        "                },\r\n",
        "                f.label,\r\n",
        "            )\r\n",
        "\r\n",
        "    return tf.data.Dataset.from_generator(\r\n",
        "        gen,\r\n",
        "        ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\r\n",
        "        (\r\n",
        "            {\r\n",
        "                \"input_ids\": tf.TensorShape([None]),\r\n",
        "                \"attention_mask\": tf.TensorShape([None]),\r\n",
        "                \"token_type_ids\": tf.TensorShape([None]),\r\n",
        "            },\r\n",
        "            tf.TensorShape([]),\r\n",
        "        ),\r\n",
        "    )\r\n",
        "\r\n",
        "\r\n",
        "DATA_COLUMN = 'DATA_COLUMN'\r\n",
        "LABEL_COLUMN = 'LABEL_COLUMN'"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lpkyZhHhITuz",
        "outputId": "758f7de8-435d-49e3-8c89-9243ffd0d83d"
      },
      "source": [
        "train_InputExamples, validation_InputExamples = convert_data_to_examples(train, test, DATA_COLUMN, LABEL_COLUMN)\r\n",
        "\r\n",
        "train_data = convert_examples_to_tf_dataset(list(train_InputExamples), tokenizer)\r\n",
        "train_data = train_data.shuffle(100).batch(32).repeat(2)\r\n",
        "\r\n",
        "validation_data = convert_examples_to_tf_dataset(list(validation_InputExamples), tokenizer)\r\n",
        "validation_data = validation_data.batch(32)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py:2143: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4Lke8_cIc_i",
        "outputId": "792368eb-c8df-4a08-aa6f-b94a7657e4e4"
      },
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0), \r\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), \r\n",
        "              metrics=[tf.keras.metrics.SparseCategoricalAccuracy('accuracy')])\r\n",
        "\r\n",
        "model.fit(train_data, epochs=2, validation_data=validation_data)\r\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.sugar.socket.Socket object at 0x7fc5815936c8>> and will run it as-is.\n",
            "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
            "Cause: <cyfunction Socket.send at 0x7fc59e9ea110> is not a module, class, method, function, traceback, frame, or code object\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "WARNING: AutoGraph could not transform <bound method Socket.send of <zmq.sugar.socket.Socket object at 0x7fc5815936c8>> and will run it as-is.\n",
            "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
            "Cause: <cyfunction Socket.send at 0x7fc59e9ea110> is not a module, class, method, function, traceback, frame, or code object\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:AutoGraph could not transform <function wrap at 0x7fc59c8cbb70> and will run it as-is.\n",
            "Cause: while/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING: AutoGraph could not transform <function wrap at 0x7fc59c8cbb70> and will run it as-is.\n",
            "Cause: while/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "   1250/Unknown - 1140s 882ms/step - loss: 0.3616 - accuracy: 0.8338"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1250/1250 [==============================] - 1185s 918ms/step - loss: 0.3615 - accuracy: 0.8338 - val_loss: 0.3264 - val_accuracy: 0.8756\n",
            "Epoch 2/2\n",
            "1250/1250 [==============================] - 1148s 919ms/step - loss: 0.1061 - accuracy: 0.9616 - val_loss: 0.4761 - val_accuracy: 0.8830\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc442a94f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oiw_FWn7I3lq"
      },
      "source": [
        "pred_sentences = ['one of the sarcastic movie ever']\r\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCDEzWzvJYVp",
        "outputId": "bf79a2c6-139f-4269-cc53-351945da7240"
      },
      "source": [
        "tf_batch = tokenizer(pred_sentences, max_length=128, padding=True, truncation=True, return_tensors='tf')\r\n",
        "tf_outputs = model(tf_batch)\r\n",
        "tf_predictions = tf.nn.softmax(tf_outputs[0], axis=-1)\r\n",
        "labels = ['Negative','Positive']\r\n",
        "label = tf.argmax(tf_predictions, axis=1)\r\n",
        "label = label.numpy()\r\n",
        "for i in range(len(pred_sentences)):\r\n",
        "  print(pred_sentences[i], \": \\n\", labels[label[i]])\r\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "one of the sarcastic movie ever : \n",
            " Positive\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}