08.sentiment-analysis-with-bert.ipynb

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      "name": "08.sentiment-analysis-with-bert.ipynb",
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      "name": "python3",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGnlRWvkY-2c",
        "colab_type": "text"
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      "source": [
        "# Sentiment Analysis with BERT\n",
        "\n",
        "> TL;DR In this tutorial, you'll learn how to fine-tune BERT for sentiment analysis. You'll do the required text preprocessing (special tokens, padding, and attention masks) and build a Sentiment Classifier using the amazing Transformers library by Hugging Face!\n",
        "\n",
        "- [Read the tutorial](https://www.curiousily.com/posts/sentiment-analysis-with-bert-and-hugging-face-using-pytorch-and-python/)\n",
        "- [Run the notebook in your browser (Google Colab)](https://colab.research.google.com/drive/1PHv-IRLPCtv7oTcIGbsgZHqrB5LPvB7S)\n",
        "- [Read the `Getting Things Done with Pytorch` book](https://github.com/curiousily/Getting-Things-Done-with-Pytorch)\n",
        "\n",
        "You'll learn how to:\n",
        "\n",
        "- Intuitively understand what BERT is\n",
        "- Preprocess text data for BERT and build PyTorch Dataset (tokenization, attention masks, and padding)\n",
        "- Use Transfer Learning to build Sentiment Classifier using the Transformers library by Hugging Face\n",
        "- Evaluate the model on test data\n",
        "- Predict sentiment on raw text\n",
        "\n",
        "Let's get started!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "fc1bc767-990a-486d-bb10-2a80cea128b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Apr 20 19:22:31 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.64.00    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tbodro8Fpmwr",
        "colab_type": "text"
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      "source": [
        "## What is BERT?\n",
        "\n",
        "BERT (introduced in [this paper](https://arxiv.org/abs/1810.04805)) stands for Bidirectional Encoder Representations from Transformers. If you don't know what most of that means - you've come to the right place! Let's unpack the main ideas:\n",
        "\n",
        "- Bidirectional - to understand the text  you're looking you'll have to look back (at the previous words) and forward (at the next words)\n",
        "- Transformers - The [Attention Is All You Need](https://arxiv.org/abs/1706.03762) paper presented the Transformer model. The Transformer reads entire sequences of tokens at once. In a sense, the model is non-directional, while LSTMs read sequentially (left-to-right or right-to-left). The attention mechanism allows for learning contextual relations between words (e.g. `his` in a sentence refers to Jim).\n",
        "- (Pre-trained) contextualized word embeddings - [The ELMO paper](https://arxiv.org/abs/1802.05365v2) introduced a way to encode words based on their meaning/context. Nails has multiple meanings - fingernails and metal nails.\n",
        "\n",
        "BERT was trained by masking 15% of the tokens with the goal to guess them. An additional objective was to predict the next sentence. Let's look at examples of these tasks:\n",
        "\n",
        "### Masked Language Modeling (Masked LM)\n",
        "\n",
        "The objective of this task is to guess the masked tokens. Let's look at an example, and try to not make it harder than it has to be:\n",
        "\n",
        "That's `[mask]` she `[mask]` -> That's what she said\n",
        "\n",
        "### Next Sentence Prediction (NSP)\n",
        "\n",
        "Given a pair of two sentences, the task is to say whether or not the second follows the first (binary classification). Let's continue with the example:\n",
        "\n",
        "*Input* = `[CLS]` That's `[mask]` she `[mask]`. [SEP] Hahaha, nice! [SEP]\n",
        "\n",
        "*Label* = *IsNext*\n",
        "\n",
        "*Input* = `[CLS]` That's `[mask]` she `[mask]`. [SEP] Dwight, you ignorant `[mask]`! [SEP]\n",
        "\n",
        "*Label* = *NotNext*\n",
        "\n",
        "The training corpus was comprised of two entries: [Toronto Book Corpus](https://arxiv.org/abs/1506.06724) (800M words) and English Wikipedia (2,500M words). While the original Transformer has an encoder (for reading the input) and a decoder (that makes the prediction), BERT uses only the decoder.\n",
        "\n",
        "BERT is simply a pre-trained stack of Transformer Encoders. How many Encoders? We have two versions - with 12 (BERT base) and 24 (BERT Large).\n",
        "\n",
        "### Is This Thing Useful in Practice?\n",
        "\n",
        "The BERT paper was released along with [the source code](https://github.com/google-research/bert) and pre-trained models.\n",
        "\n",
        "The best part is that you can do Transfer Learning (thanks to the ideas from OpenAI Transformer) with BERT for many NLP tasks - Classification, Question Answering, Entity Recognition, etc. You can train with small amounts of data and achieve great performance!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmj22-TcZMef",
        "colab_type": "text"
      },
      "source": [
        "## Setup\n",
        "\n",
        "We'll need [the Transformers library](https://huggingface.co/transformers/) by Hugging Face:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kj_7Tz0-pK69",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q -U watermark"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jjsbi1u3QFEM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -qq transformers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJqoaFpVpoM8",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "%reload_ext watermark\n",
        "%watermark -v -p numpy,pandas,torch,transformers"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPython 3.6.9\n",
            "IPython 5.5.0\n",
            "\n",
            "numpy 1.18.2\n",
            "pandas 1.0.3\n",
            "torch 1.4.0\n",
            "transformers 2.8.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w68CZpOwFoly",
        "colab_type": "code",
        "outputId": "9c1a0321-1650-4224-cf9c-3c8dc8661ed3",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#@title Setup & Config\n",
        "import transformers\n",
        "from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n",
        "import torch\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from collections import defaultdict\n",
        "from textwrap import wrap\n",
        "\n",
        "from torch import nn, optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.nn.functional as F\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "\n",
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
        "\n",
        "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
        "\n",
        "rcParams['figure.figsize'] = 12, 8\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufzPdoTtNikq",
        "colab_type": "text"
      },
      "source": [
        "## Data Exploration\n",
        "\n",
        "We'll load the Google Play app reviews dataset, that we've put together in the previous part:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgPRhuMzi9ot",
        "colab_type": "code",
        "outputId": "846be435-6864-4157-9510-5325cc75bced",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "!gdown --id 1S6qMioqPJjyBLpLVz4gmRTnJHnjitnuV\n",
        "!gdown --id 1zdmewp7ayS4js4VtrJEHzAheSW-5NBZv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1S6qMioqPJjyBLpLVz4gmRTnJHnjitnuV\n",
            "To: /content/apps.csv\n",
            "100% 134k/134k [00:00<00:00, 50.2MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1zdmewp7ayS4js4VtrJEHzAheSW-5NBZv\n",
            "To: /content/reviews.csv\n",
            "7.17MB [00:00, 33.4MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUKLyKc7I6Qp",
        "colab_type": "code",
        "outputId": "81441fd4-74b2-4b5b-b4f1-76c41cb6ce6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        }
      },
      "source": [
        "df = pd.read_csv(\"reviews.csv\")\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userName</th>\n",
              "      <th>userImage</th>\n",
              "      <th>content</th>\n",
              "      <th>score</th>\n",
              "      <th>thumbsUpCount</th>\n",
              "      <th>reviewCreatedVersion</th>\n",
              "      <th>at</th>\n",
              "      <th>replyContent</th>\n",
              "      <th>repliedAt</th>\n",
              "      <th>sortOrder</th>\n",
              "      <th>appId</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andrew Thomas</td>\n",
              "      <td>https://lh3.googleusercontent.com/a-/AOh14GiHd...</td>\n",
              "      <td>Update: After getting a response from the deve...</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>4.17.0.3</td>\n",
              "      <td>2020-04-05 22:25:57</td>\n",
              "      <td>According to our TOS, and the term you have ag...</td>\n",
              "      <td>2020-04-05 15:10:24</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Craig Haines</td>\n",
              "      <td>https://lh3.googleusercontent.com/-hoe0kwSJgPQ...</td>\n",
              "      <td>Used it for a fair amount of time without any ...</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>4.17.0.3</td>\n",
              "      <td>2020-04-04 13:40:01</td>\n",
              "      <td>It sounds like you logged in with a different ...</td>\n",
              "      <td>2020-04-05 15:11:35</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>steven adkins</td>\n",
              "      <td>https://lh3.googleusercontent.com/a-/AOh14GiXw...</td>\n",
              "      <td>Your app sucks now!!!!! Used to be good but no...</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>4.17.0.3</td>\n",
              "      <td>2020-04-01 16:18:13</td>\n",
              "      <td>This sounds odd! We are not aware of any issue...</td>\n",
              "      <td>2020-04-02 16:05:56</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lars Panzerbjørn</td>\n",
              "      <td>https://lh3.googleusercontent.com/a-/AOh14Gg-h...</td>\n",
              "      <td>It seems OK, but very basic. Recurring tasks n...</td>\n",
              "      <td>1</td>\n",
              "      <td>192</td>\n",
              "      <td>4.17.0.2</td>\n",
              "      <td>2020-03-12 08:17:34</td>\n",
              "      <td>We do offer this option as part of the Advance...</td>\n",
              "      <td>2020-03-15 06:20:13</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Scott Prewitt</td>\n",
              "      <td>https://lh3.googleusercontent.com/-K-X1-YsVd6U...</td>\n",
              "      <td>Absolutely worthless. This app runs a prohibit...</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>4.17.0.2</td>\n",
              "      <td>2020-03-14 17:41:01</td>\n",
              "      <td>We're sorry you feel this way! 90% of the app ...</td>\n",
              "      <td>2020-03-15 23:45:51</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           userName  ...      appId\n",
              "0     Andrew Thomas  ...  com.anydo\n",
              "1      Craig Haines  ...  com.anydo\n",
              "2     steven adkins  ...  com.anydo\n",
              "3  Lars Panzerbjørn  ...  com.anydo\n",
              "4     Scott Prewitt  ...  com.anydo\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dB2jE6am7Dpo",
        "colab_type": "code",
        "outputId": "19acca28-2336-43f1-b714-29d993bc114c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15746, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWqVNHJbn10l",
        "colab_type": "text"
      },
      "source": [
        "We have about 16k examples. Let's check for missing values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VA_wGSLQLKCh",
        "colab_type": "code",
        "outputId": "9468ddba-47d0-46a8-d7bb-6ff51c4bdb8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15746 entries, 0 to 15745\n",
            "Data columns (total 11 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   userName              15746 non-null  object\n",
            " 1   userImage             15746 non-null  object\n",
            " 2   content               15746 non-null  object\n",
            " 3   score                 15746 non-null  int64 \n",
            " 4   thumbsUpCount         15746 non-null  int64 \n",
            " 5   reviewCreatedVersion  13533 non-null  object\n",
            " 6   at                    15746 non-null  object\n",
            " 7   replyContent          7367 non-null   object\n",
            " 8   repliedAt             7367 non-null   object\n",
            " 9   sortOrder             15746 non-null  object\n",
            " 10  appId                 15746 non-null  object\n",
            "dtypes: int64(2), object(9)\n",
            "memory usage: 1.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H3cL_1qVn_6h",
        "colab_type": "text"
      },
      "source": [
        "Great, no missing values in the score and review texts! Do we have class imbalance?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wwh_rW4Efhs3",
        "colab_type": "code",
        "outputId": "e39b9955-3c5e-45f3-f960-38bfa03447c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "sns.countplot(df.score)\n",
        "plt.xlabel('review score');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 745,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZM0GKviobjM",
        "colab_type": "text"
      },
      "source": [
        "That's hugely imbalanced, but it's okay. We're going to convert the dataset into negative, neutral and positive sentiment:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ei0xmdi1Chp0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def to_sentiment(rating):\n",
        "  rating = int(rating)\n",
        "  if rating <= 2:\n",
        "    return 0\n",
        "  elif rating == 3:\n",
        "    return 1\n",
        "  else: \n",
        "    return 2\n",
        "\n",
        "df['sentiment'] = df.score.apply(to_sentiment)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-155O-SFSqE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['negative', 'neutral', 'positive']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3tY3ECJDPaz",
        "colab_type": "code",
        "outputId": "b4ff4686-f568-4f3c-8eef-006485c6d660",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "ax = sns.countplot(df.sentiment)\n",
        "plt.xlabel('review sentiment')\n",
        "ax.set_xticklabels(class_names);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 745,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOssB4CKnAX2",
        "colab_type": "text"
      },
      "source": [
        "The balance was (mostly) restored."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aHyGuTFgyPO",
        "colab_type": "text"
      },
      "source": [
        "## Data Preprocessing\n",
        "\n",
        "You might already know that Machine Learning models don't work with raw text. You need to convert text to numbers (of some sort). BERT requires even more attention (good one, right?). Here are the requirements: \n",
        "\n",
        "- Add special tokens to separate sentences and do classification\n",
        "- Pass sequences of constant length (introduce padding)\n",
        "- Create array of 0s (pad token) and 1s (real token) called *attention mask*\n",
        "\n",
        "The Transformers library provides (you've guessed it) a wide variety of Transformer models (including BERT). It works with TensorFlow and PyTorch! It also includes prebuild tokenizers that do the heavy lifting for us!\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7Mj-0ne--5t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PRE_TRAINED_MODEL_NAME = 'bert-base-cased'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMSr7C-F_sey",
        "colab_type": "text"
      },
      "source": [
        "> You can use a cased and uncased version of BERT and tokenizer. I've experimented with both. The cased version works better. Intuitively, that makes sense, since \"BAD\" might convey more sentiment than \"bad\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiLb-ltM-ZRz",
        "colab_type": "text"
      },
      "source": [
        "Let's load a pre-trained [BertTokenizer](https://huggingface.co/transformers/model_doc/bert.html#berttokenizer):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3AfJSZ8NNLF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfrSbwTQ-wi_",
        "colab_type": "text"
      },
      "source": [
        "We'll use this text to understand the tokenization process:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZMitwrqm2eb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample_txt = 'When was I last outside? I am stuck at home for 2 weeks.'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yO2qBTVl_KPs",
        "colab_type": "text"
      },
      "source": [
        "Some basic operations can convert the text to tokens and tokens to unique integers (ids):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTFhpHpsoWO7",
        "colab_type": "code",
        "outputId": "b20afc9d-6481-4d95-8fa9-c398d0d167db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "tokens = tokenizer.tokenize(sample_txt)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "print(f' Sentence: {sample_txt}')\n",
        "print(f'   Tokens: {tokens}')\n",
        "print(f'Token IDs: {token_ids}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Sentence: When was I last outside? I am stuck at home for 2 weeks.\n",
            "   Tokens: ['When', 'was', 'I', 'last', 'outside', '?', 'I', 'am', 'stuck', 'at', 'home', 'for', '2', 'weeks', '.']\n",
            "Token IDs: [1332, 1108, 146, 1314, 1796, 136, 146, 1821, 5342, 1120, 1313, 1111, 123, 2277, 119]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzbbKLR8lZbu",
        "colab_type": "text"
      },
      "source": [
        "### Special Tokens\n",
        "\n",
        "`[SEP]` - marker for ending of a sentence\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXwz47bQvCbc",
        "colab_type": "code",
        "outputId": "aa94d74a-326a-41df-80b4-a59ac690ee9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tokenizer.sep_token, tokenizer.sep_token_id"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[SEP]', 102)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mip_eGeXwLFF",
        "colab_type": "text"
      },
      "source": [
        "`[CLS]` - we must add this token to the start of each sentence, so BERT knows we're doing classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6K4it5HwE6l",
        "colab_type": "code",
        "outputId": "73351498-edf2-410d-b444-8d7ba31781e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tokenizer.cls_token, tokenizer.cls_token_id"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[CLS]', 101)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qi6O-yEY09gl",
        "colab_type": "text"
      },
      "source": [
        "There is also a special token for padding:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vx7gD5xf1AFK",
        "colab_type": "code",
        "outputId": "fa5cf8e7-5cd5-4056-afd4-5a781797515d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tokenizer.pad_token, tokenizer.pad_token_id"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[PAD]', 0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6GWCfijM0TWB",
        "colab_type": "text"
      },
      "source": [
        "BERT understands tokens that were in the training set. Everything else can be encoded using the `[UNK]` (unknown) token:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cmfFsbEKQDT",
        "colab_type": "code",
        "outputId": "2a7d9f8e-6c61-443b-d0f1-d7206cfbb00a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tokenizer.unk_token, tokenizer.unk_token_id"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[UNK]', 100)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9ap7jdL0LYU",
        "colab_type": "text"
      },
      "source": [
        "All of that work can be done using the [`encode_plus()`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.PreTrainedTokenizer.encode_plus) method:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vea9edaaxSPO",
        "colab_type": "code",
        "outputId": "389562b7-89d5-4fb3-e2d7-23a41fc15bb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "encoding = tokenizer.encode_plus(\n",
        "  sample_txt,\n",
        "  max_length=32,\n",
        "  add_special_tokens=True, # Add '[CLS]' and '[SEP]'\n",
        "  return_token_type_ids=False,\n",
        "  pad_to_max_length=True,\n",
        "  return_attention_mask=True,\n",
        "  return_tensors='pt',  # Return PyTorch tensors\n",
        ")\n",
        "\n",
        "encoding.keys()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sS69c8WvdOED",
        "colab_type": "text"
      },
      "source": [
        "The token ids are now stored in a Tensor and padded to a length of 32:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzBmcOla0yQR",
        "colab_type": "code",
        "outputId": "921d377a-4fd6-4939-e4a4-76ce57d4ed34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(len(encoding['input_ids'][0]))\n",
        "encoding['input_ids'][0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 101, 1332, 1108,  146, 1314, 1796,  136,  146, 1821, 5342, 1120, 1313,\n",
              "        1111,  123, 2277,  119,  102,    0,    0,    0,    0,    0,    0,    0,\n",
              "           0,    0,    0,    0,    0,    0,    0,    0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itAyVPsNdyc1",
        "colab_type": "text"
      },
      "source": [
        "The attention mask has the same length:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wiv5LLiw03Ox",
        "colab_type": "code",
        "outputId": "8fd1b81a-a5b4-461a-f6f9-53d5ca497562",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(len(encoding['attention_mask'][0]))\n",
        "encoding['attention_mask']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1RvhC4jNHHy",
        "colab_type": "text"
      },
      "source": [
        "We can inverse the tokenization to have a look at the special tokens:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IagGoafKLUwW",
        "colab_type": "code",
        "outputId": "a93cdc3c-518a-4d23-d708-f9cee1aea079",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "source": [
        "tokenizer.convert_ids_to_tokens(encoding['input_ids'][0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'When',\n",
              " 'was',\n",
              " 'I',\n",
              " 'last',\n",
              " 'outside',\n",
              " '?',\n",
              " 'I',\n",
              " 'am',\n",
              " 'stuck',\n",
              " 'at',\n",
              " 'home',\n",
              " 'for',\n",
              " '2',\n",
              " 'weeks',\n",
              " '.',\n",
              " '[SEP]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waKjYxTDuaWt",
        "colab_type": "text"
      },
      "source": [
        "### Choosing Sequence Length\n",
        "\n",
        "BERT works with fixed-length sequences. We'll use a simple strategy to choose the max length. Let's store the token length of each review:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUnE5CT9hbeZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "token_lens = []\n",
        "\n",
        "for txt in df.content:\n",
        "  tokens = tokenizer.encode(txt, max_length=512)\n",
        "  token_lens.append(len(tokens))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tI4goUrHf6da",
        "colab_type": "text"
      },
      "source": [
        "and plot the distribution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SzE1j4jxmUtd",
        "colab_type": "code",
        "outputId": "cf03f40b-88a7-43b0-bc2c-32eb6e16c935",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "sns.distplot(token_lens)\n",
        "plt.xlim([0, 256]);\n",
        "plt.xlabel('Token count');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 739,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oW6ajl30t6du",
        "colab_type": "text"
      },
      "source": [
        "Most of the reviews seem to contain less than 128 tokens, but we'll be on the safe side and choose a maximum length of 160."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7xSmJtLuoxW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "MAX_LEN = 160"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvvcoU6nurHy",
        "colab_type": "text"
      },
      "source": [
        "We have all building blocks required to create a PyTorch dataset. Let's do it:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2BPgRJ7YBK0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class GPReviewDataset(Dataset):\n",
        "\n",
        "  def __init__(self, reviews, targets, tokenizer, max_len):\n",
        "    self.reviews = reviews\n",
        "    self.targets = targets\n",
        "    self.tokenizer = tokenizer\n",
        "    self.max_len = max_len\n",
        "  \n",
        "  def __len__(self):\n",
        "    return len(self.reviews)\n",
        "  \n",
        "  def __getitem__(self, item):\n",
        "    review = str(self.reviews[item])\n",
        "    target = self.targets[item]\n",
        "\n",
        "    encoding = self.tokenizer.encode_plus(\n",
        "      review,\n",
        "      add_special_tokens=True,\n",
        "      max_length=self.max_len,\n",
        "      return_token_type_ids=False,\n",
        "      pad_to_max_length=True,\n",
        "      return_attention_mask=True,\n",
        "      return_tensors='pt',\n",
        "    )\n",
        "\n",
        "    return {\n",
        "      'review_text': review,\n",
        "      'input_ids': encoding['input_ids'].flatten(),\n",
        "      'attention_mask': encoding['attention_mask'].flatten(),\n",
        "      'targets': torch.tensor(target, dtype=torch.long)\n",
        "    }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2uwsvCYqDJK",
        "colab_type": "text"
      },
      "source": [
        "The tokenizer is doing most of the heavy lifting for us. We also return the review texts, so it'll be easier to evaluate the predictions from our model. Let's split the data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-vWzoo81dvO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train, df_test = train_test_split(df, test_size=0.1, random_state=RANDOM_SEED)\n",
        "df_val, df_test = train_test_split(df_test, test_size=0.5, random_state=RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xz3ZOQXVPCwh",
        "colab_type": "code",
        "outputId": "dd8d2844-3b22-425d-dc40-725f7f46e52a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df_train.shape, df_val.shape, df_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((14171, 12), (787, 12), (788, 12))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J4tQ1x-vqNab",
        "colab_type": "text"
      },
      "source": [
        "We also need to create a couple of data loaders. Here's a helper function to do it:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEGqcvkuOuTX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_data_loader(df, tokenizer, max_len, batch_size):\n",
        "  ds = GPReviewDataset(\n",
        "    reviews=df.content.to_numpy(),\n",
        "    targets=df.sentiment.to_numpy(),\n",
        "    tokenizer=tokenizer,\n",
        "    max_len=max_len\n",
        "  )\n",
        "\n",
        "  return DataLoader(\n",
        "    ds,\n",
        "    batch_size=batch_size,\n",
        "    num_workers=4\n",
        "  )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vODDxMKsPHqI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BATCH_SIZE = 16\n",
        "\n",
        "train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A6dlOptwqlhF",
        "colab_type": "text"
      },
      "source": [
        "Let's have a look at an example batch from our training data loader:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y93ldSN47FeT",
        "colab_type": "code",
        "outputId": "ee6eaa1a-3f03-4e18-c059-02dbf8b8bc14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data = next(iter(train_data_loader))\n",
        "data.keys()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['review_text', 'input_ids', 'attention_mask', 'targets'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IdU4YVqb7N8M",
        "colab_type": "code",
        "outputId": "1f67fe37-6634-484f-caa2-1517e80a29d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(data['input_ids'].shape)\n",
        "print(data['attention_mask'].shape)\n",
        "print(data['targets'].shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([16, 160])\n",
            "torch.Size([16, 160])\n",
            "torch.Size([16])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H63Y-TjyRC7S",
        "colab_type": "text"
      },
      "source": [
        "## Sentiment Classification with BERT and Hugging Face"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "440Nd31VTHER",
        "colab_type": "text"
      },
      "source": [
        "There are a lot of helpers that make using BERT easy with the Transformers library. Depending on the task you might want to use [BertForSequenceClassification](https://huggingface.co/transformers/model_doc/bert.html#bertforsequenceclassification), [BertForQuestionAnswering](https://huggingface.co/transformers/model_doc/bert.html#bertforquestionanswering) or something else. \n",
        "\n",
        "But who cares, right? We're *hardcore*! We'll use the basic [BertModel](https://huggingface.co/transformers/model_doc/bert.html#bertmodel) and build our sentiment classifier on top of it. Let's load the model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0P41FayISNRI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFE7YSbFdY4t",
        "colab_type": "text"
      },
      "source": [
        "And try to use it on the encoding of our sample text:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1aoFxbQSn15",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "last_hidden_state_pooled_output = bert_model(\n",
        "  input_ids=encoding['input_ids'], \n",
        "  attention_mask=encoding['attention_mask']\n",
        ")",
        "last_hidden_state, pooled_output = last_hidden_state_pooled_output[0], last_hidden_state_pooled_output[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLLu8zmqbaHV",
        "colab_type": "text"
      },
      "source": [
        "The `last_hidden_state` is a sequence of hidden states of the last layer of the model. Obtaining the `pooled_output` is done by applying the [BertPooler](https://github.com/huggingface/transformers/blob/edf0582c0be87b60f94f41c659ea779876efc7be/src/transformers/modeling_bert.py#L426) on `last_hidden_state`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUJHXNpIbcci",
        "colab_type": "code",
        "outputId": "74906d2c-153b-4f40-e682-b6f501d1ecfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "last_hidden_state.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 32, 768])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4dAot4zbz8k",
        "colab_type": "text"
      },
      "source": [
        "We have the hidden state for each of our 32 tokens (the length of our example sequence). But why 768? This is the number of hidden units in the feedforward-networks. We can verify that by checking the config:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsxB7Qy7b5YN",
        "colab_type": "code",
        "outputId": "8aa72ce7-ff62-4075-c36f-4a4f7cda8182",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "bert_model.config.hidden_size"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTKi8-rTd_j4",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "You can think of the `pooled_output` as a summary of the content, according to BERT. Albeit, you might try and do better. Let's look at the shape of the output:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jIAtRhaSz9c",
        "colab_type": "code",
        "outputId": "907e30cd-ad8b-4686-9d50-516f29703ebe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "pooled_output.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 768])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0o_NiS3WgOFf",
        "colab_type": "text"
      },
      "source": [
        "We can use all of this knowledge to create a classifier that uses the BERT model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_mRflxPl32F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class SentimentClassifier(nn.Module):\n",
        "\n",
        "  def __init__(self, n_classes):\n",
        "    super(SentimentClassifier, self).__init__()\n",
        "    self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)\n",
        "    self.drop = nn.Dropout(p=0.3)\n",
        "    self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\n",
        "  \n",
        "  def forward(self, input_ids, attention_mask):\n",
        "    _, pooled_output = self.bert(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "    output = self.drop(pooled_output)\n",
        "    return self.out(output)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJg8m3NQJahc",
        "colab_type": "text"
      },
      "source": [
        "Our classifier delegates most of the heavy lifting to the BertModel. We use a dropout layer for some regularization and a fully-connected layer for our output. Note that we're returning the raw output of the last layer since that is required for the cross-entropy loss function in PyTorch to work.\n",
        "\n",
        "This should work like any other PyTorch model. Let's create an instance and move it to the GPU:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0yQnuSFsjDp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = SentimentClassifier(len(class_names))\n",
        "model = model.to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCPCFDLlKIQd",
        "colab_type": "text"
      },
      "source": [
        "We'll move the example batch of our training data to the GPU:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mz7p__CqdaMO",
        "colab_type": "code",
        "outputId": "7a933577-8c04-42f3-c3ea-ecb9c1c30a5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "input_ids = data['input_ids'].to(device)\n",
        "attention_mask = data['attention_mask'].to(device)\n",
        "\n",
        "print(input_ids.shape) # batch size x seq length\n",
        "print(attention_mask.shape) # batch size x seq length"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([16, 160])\n",
            "torch.Size([16, 160])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hr1EgkEtKOIB",
        "colab_type": "text"
      },
      "source": [
        "To get the predicted probabilities from our trained model, we'll apply the softmax function to the outputs:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rTCj46Zamry",
        "colab_type": "code",
        "outputId": "04ecb643-ccda-461f-886f-aefe01f9a248",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "F.softmax(model(input_ids, attention_mask), dim=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.5879, 0.0842, 0.3279],\n",
              "        [0.4308, 0.1888, 0.3804],\n",
              "        [0.4871, 0.1766, 0.3363],\n",
              "        [0.3364, 0.0778, 0.5858],\n",
              "        [0.4025, 0.1040, 0.4935],\n",
              "        [0.3599, 0.1026, 0.5374],\n",
              "        [0.5054, 0.1552, 0.3394],\n",
              "        [0.5962, 0.1464, 0.2574],\n",
              "        [0.3274, 0.1967, 0.4759],\n",
              "        [0.3026, 0.1118, 0.5856],\n",
              "        [0.4103, 0.1571, 0.4326],\n",
              "        [0.4879, 0.2121, 0.3000],\n",
              "        [0.3811, 0.1477, 0.4712],\n",
              "        [0.3354, 0.1354, 0.5292],\n",
              "        [0.3999, 0.2822, 0.3179],\n",
              "        [0.5075, 0.1684, 0.3242]], device='cuda:0', grad_fn=<SoftmaxBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9xikRdtRN1N",
        "colab_type": "text"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "76g7FV85H-T8"
      },
      "source": [
        "To reproduce the training procedure from the BERT paper, we'll use the [AdamW](https://huggingface.co/transformers/main_classes/optimizer_schedules.html#adamw) optimizer provided by Hugging Face. It corrects weight decay, so it's similar to the original paper. We'll also use a linear scheduler with no warmup steps:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5v-ArJ2fCCcU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "EPOCHS = 10\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)\n",
        "total_steps = len(train_data_loader) * EPOCHS\n",
        "\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "  optimizer,\n",
        "  num_warmup_steps=0,\n",
        "  num_training_steps=total_steps\n",
        ")\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss().to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8522g7JIu5J",
        "colab_type": "text"
      },
      "source": [
        "How do we come up with all hyperparameters? The BERT authors have some recommendations for fine-tuning:\n",
        "\n",
        "- Batch size: 16, 32\n",
        "- Learning rate (Adam): 5e-5, 3e-5, 2e-5\n",
        "- Number of epochs: 2, 3, 4\n",
        "\n",
        "We're going to ignore the number of epochs recommendation but stick with the rest. Note that increasing the batch size reduces the training time significantly, but gives you lower accuracy.\n",
        "\n",
        "Let's continue with writing a helper function for training our model for one epoch:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzl9UhuNx1_Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_epoch(\n",
        "  model, \n",
        "  data_loader, \n",
        "  loss_fn, \n",
        "  optimizer, \n",
        "  device, \n",
        "  scheduler, \n",
        "  n_examples\n",
        "):\n",
        "  model = model.train()\n",
        "\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "  \n",
        "  for d in data_loader:\n",
        "    input_ids = d[\"input_ids\"].to(device)\n",
        "    attention_mask = d[\"attention_mask\"].to(device)\n",
        "    targets = d[\"targets\"].to(device)\n",
        "\n",
        "    outputs = model(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    loss = loss_fn(outputs, targets)\n",
        "\n",
        "    correct_predictions += torch.sum(preds == targets)\n",
        "    losses.append(loss.item())\n",
        "\n",
        "    loss.backward()\n",
        "    nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "    optimizer.step()\n",
        "    scheduler.step()\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4PniYIte0fr",
        "colab_type": "text"
      },
      "source": [
        "Training the model should look familiar, except for two things. The scheduler gets called every time a batch is fed to the model. We're avoiding exploding gradients by clipping the gradients of the model using [clip_grad_norm_](https://pytorch.org/docs/stable/nn.html#clip-grad-norm).\n",
        "\n",
        "Let's write another one that helps us evaluate the model on a given data loader:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXeRorVGIKre",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def eval_model(model, data_loader, loss_fn, device, n_examples):\n",
        "  model = model.eval()\n",
        "\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      targets = d[\"targets\"].to(device)\n",
        "\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "      loss = loss_fn(outputs, targets)\n",
        "\n",
        "      correct_predictions += torch.sum(preds == targets)\n",
        "      losses.append(loss.item())\n",
        "\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_rdSDBHhhCh",
        "colab_type": "text"
      },
      "source": [
        "Using those two, we can write our training loop. We'll also store the training history:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zhHoFNsxufs",
        "colab_type": "code",
        "outputId": "2f11710a-700e-4933-b57e-5d50e5ed1f78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        }
      },
      "source": [
        "%%time\n",
        "\n",
        "history = defaultdict(list)\n",
        "best_accuracy = 0\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "\n",
        "  print(f'Epoch {epoch + 1}/{EPOCHS}')\n",
        "  print('-' * 10)\n",
        "\n",
        "  train_acc, train_loss = train_epoch(\n",
        "    model,\n",
        "    train_data_loader,    \n",
        "    loss_fn, \n",
        "    optimizer, \n",
        "    device, \n",
        "    scheduler, \n",
        "    len(df_train)\n",
        "  )\n",
        "\n",
        "  print(f'Train loss {train_loss} accuracy {train_acc}')\n",
        "\n",
        "  val_acc, val_loss = eval_model(\n",
        "    model,\n",
        "    val_data_loader,\n",
        "    loss_fn, \n",
        "    device, \n",
        "    len(df_val)\n",
        "  )\n",
        "\n",
        "  print(f'Val   loss {val_loss} accuracy {val_acc}')\n",
        "  print()\n",
        "\n",
        "  history['train_acc'].append(train_acc)\n",
        "  history['train_loss'].append(train_loss)\n",
        "  history['val_acc'].append(val_acc)\n",
        "  history['val_loss'].append(val_loss)\n",
        "\n",
        "  if val_acc > best_accuracy:\n",
        "    torch.save(model.state_dict(), 'best_model_state.bin')\n",
        "    best_accuracy = val_acc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "----------\n",
            "Train loss 0.7330631300571541 accuracy 0.6653729447463129\n",
            "Val   loss 0.5767546480894089 accuracy 0.7776365946632783\n",
            "\n",
            "Epoch 2/10\n",
            "----------\n",
            "Train loss 0.4158683338330777 accuracy 0.8420012701997036\n",
            "Val   loss 0.5365073362737894 accuracy 0.832274459974587\n",
            "\n",
            "Epoch 3/10\n",
            "----------\n",
            "Train loss 0.24015077009679367 accuracy 0.922023851527768\n",
            "Val   loss 0.5074492372572422 accuracy 0.8716645489199493\n",
            "\n",
            "Epoch 4/10\n",
            "----------\n",
            "Train loss 0.16012676668187295 accuracy 0.9546962105708843\n",
            "Val   loss 0.6009970247745514 accuracy 0.8703939008894537\n",
            "\n",
            "Epoch 5/10\n",
            "----------\n",
            "Train loss 0.11209654617575301 accuracy 0.9675393409074872\n",
            "Val   loss 0.7367783848941326 accuracy 0.8742058449809403\n",
            "\n",
            "Epoch 6/10\n",
            "----------\n",
            "Train loss 0.08572274737026433 accuracy 0.9764307388328276\n",
            "Val   loss 0.7251267762482166 accuracy 0.8843710292249047\n",
            "\n",
            "Epoch 7/10\n",
            "----------\n",
            "Train loss 0.06132202987342602 accuracy 0.9833462705525369\n",
            "Val   loss 0.7083295831084251 accuracy 0.889453621346887\n",
            "\n",
            "Epoch 8/10\n",
            "----------\n",
            "Train loss 0.050604159273123096 accuracy 0.9849693035071626\n",
            "Val   loss 0.753860274553299 accuracy 0.8907242693773825\n",
            "\n",
            "Epoch 9/10\n",
            "----------\n",
            "Train loss 0.04373276197092931 accuracy 0.9862395032107826\n",
            "Val   loss 0.7506809896230697 accuracy 0.8919949174078781\n",
            "\n",
            "Epoch 10/10\n",
            "----------\n",
            "Train loss 0.03768671146314381 accuracy 0.9880036694658105\n",
            "Val   loss 0.7431786182522774 accuracy 0.8932655654383737\n",
            "\n",
            "CPU times: user 29min 54s, sys: 13min 28s, total: 43min 23s\n",
            "Wall time: 43min 43s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4r8-5zWsiVur",
        "colab_type": "text"
      },
      "source": [
        "Note that we're storing the state of the best model, indicated by the highest validation accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLQf52c7fbzr",
        "colab_type": "text"
      },
      "source": [
        "Whoo, this took some time! We can look at the training vs validation accuracy:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FWG7kBm372V",
        "colab_type": "code",
        "outputId": "9dd7f8cf-8f36-4280-dfff-bdaa8b9b89f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        }
      },
      "source": [
        "plt.plot(history['train_acc'], label='train accuracy')\n",
        "plt.plot(history['val_acc'], label='validation accuracy')\n",
        "\n",
        "plt.title('Training history')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.ylim([0, 1]);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 732,
              "height": 506
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZsHqkLAuf8pv",
        "colab_type": "text"
      },
      "source": [
        "The training accuracy starts to approach 100% after 10 epochs or so. You might try to fine-tune the parameters a bit more, but this will be good enough for us.\n",
        "\n",
        "Don't want to wait? Uncomment the next cell to download my pre-trained model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoGUH8VZ-pPQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !gdown --id 1V8itWtowCYnb2Bc9KlK9SxGff9WwmogA\n",
        "\n",
        "# model = SentimentClassifier(len(class_names))\n",
        "# model.load_state_dict(torch.load('best_model_state.bin'))\n",
        "# model = model.to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3HZb3NWFtFf",
        "colab_type": "text"
      },
      "source": [
        "## Evaluation\n",
        "\n",
        "So how good is our model on predicting sentiment? Let's start by calculating the accuracy on the test data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jS3gJ_qBEljD",
        "colab_type": "code",
        "outputId": "21f968b6-fd29-4e74-dee0-8dc9eacd301e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_acc, _ = eval_model(\n",
        "  model,\n",
        "  test_data_loader,\n",
        "  loss_fn,\n",
        "  device,\n",
        "  len(df_test)\n",
        ")\n",
        "\n",
        "test_acc.item()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.883248730964467"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mdQ7-ylCj8Gd",
        "colab_type": "text"
      },
      "source": [
        "The accuracy is about 1% lower on the test set. Our model seems to generalize well.\n",
        "\n",
        "We'll define a helper function to get the predictions from our model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EgR6MuNS8jr_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_predictions(model, data_loader):\n",
        "  model = model.eval()\n",
        "  \n",
        "  review_texts = []\n",
        "  predictions = []\n",
        "  prediction_probs = []\n",
        "  real_values = []\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\n",
        "\n",
        "      texts = d[\"review_text\"]\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      targets = d[\"targets\"].to(device)\n",
        "\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "      probs = F.softmax(outputs, dim=1)\n",
        "\n",
        "      review_texts.extend(texts)\n",
        "      predictions.extend(preds)\n",
        "      prediction_probs.extend(probs)\n",
        "      real_values.extend(targets)\n",
        "\n",
        "  predictions = torch.stack(predictions).cpu()\n",
        "  prediction_probs = torch.stack(prediction_probs).cpu()\n",
        "  real_values = torch.stack(real_values).cpu()\n",
        "  return review_texts, predictions, prediction_probs, real_values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dkbnBTI7kd_y",
        "colab_type": "text"
      },
      "source": [
        "This is similar to the evaluation function, except that we're storing the text of the reviews and the predicted probabilities (by applying the softmax on the model outputs):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHdPZr60-0c_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(\n",
        "  model,\n",
        "  test_data_loader\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVwoVij2lC7F",
        "colab_type": "text"
      },
      "source": [
        "Let's have a look at the classification report"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8a9_8-ND3Is",
        "colab_type": "code",
        "outputId": "9b2c48cc-b62e-41f3-dba5-af90457a37de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(classification_report(y_test, y_pred, target_names=class_names))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.89      0.87      0.88       245\n",
            "     neutral       0.83      0.85      0.84       254\n",
            "    positive       0.92      0.93      0.92       289\n",
            "\n",
            "    accuracy                           0.88       788\n",
            "   macro avg       0.88      0.88      0.88       788\n",
            "weighted avg       0.88      0.88      0.88       788\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFAekw3mmWUi",
        "colab_type": "text"
      },
      "source": [
        "Looks like it is really hard to classify neutral (3 stars) reviews. And I can tell you from experience, looking at many reviews, those are hard to classify.\n",
        "\n",
        "We'll continue with the confusion matrix:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6d1qxsc__DTh",
        "colab_type": "code",
        "outputId": "14b8839c-4e14-430c-b185-46b09bd4231e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        }
      },
      "source": [
        "def show_confusion_matrix(confusion_matrix):\n",
        "  hmap = sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "  hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')\n",
        "  hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=30, ha='right')\n",
        "  plt.ylabel('True sentiment')\n",
        "  plt.xlabel('Predicted sentiment');\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "df_cm = pd.DataFrame(cm, index=class_names, columns=class_names)\n",
        "show_confusion_matrix(df_cm)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 725,
              "height": 516
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wx0U7oNsnZ3A",
        "colab_type": "text"
      },
      "source": [
        "This confirms that our model is having difficulty classifying neutral reviews. It mistakes those for negative and positive at a roughly equal frequency.\n",
        "\n",
        "That's a good overview of the performance of our model. But let's have a look at an example from our test data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iANBiY3sLo-K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "idx = 2\n",
        "\n",
        "review_text = y_review_texts[idx]\n",
        "true_sentiment = y_test[idx]\n",
        "pred_df = pd.DataFrame({\n",
        "  'class_names': class_names,\n",
        "  'values': y_pred_probs[idx]\n",
        "})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8D0rb1yfnv4",
        "colab_type": "code",
        "outputId": "3c2aa437-9c0d-4421-adf6-9d12e87f4a83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "print(\"\\n\".join(wrap(review_text)))\n",
        "print()\n",
        "print(f'True sentiment: {class_names[true_sentiment]}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I used to use Habitica, and I must say this is a great step up. I'd\n",
            "like to see more social features, such as sharing tasks - only one\n",
            "person has to perform said task for it to be checked off, but only\n",
            "giving that person the experience and gold. Otherwise, the price for\n",
            "subscription is too steep, thus resulting in a sub-perfect score. I\n",
            "could easily justify $0.99/month or eternal subscription for $15. If\n",
            "that price could be met, as well as fine tuning, this would be easily\n",
            "worth 5 stars.\n",
            "\n",
            "True sentiment: neutral\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7hj_IZFnn2X",
        "colab_type": "text"
      },
      "source": [
        "Now we can look at the confidence of each sentiment of our model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qj4d8lZyMkhf",
        "colab_type": "code",
        "outputId": "3e1e4f5d-3ae0-41bd-8ddc-348e85c13e98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "sns.barplot(x='values', y='class_names', data=pred_df, orient='h')\n",
        "plt.ylabel('sentiment')\n",
        "plt.xlabel('probability')\n",
        "plt.xlim([0, 1]);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 779,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WL5pDmvFyaU",
        "colab_type": "text"
      },
      "source": [
        "### Predicting on Raw Text\n",
        "\n",
        "Let's use our model to predict the sentiment of some raw text:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEPi7zQRsDhH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "review_text = \"I love completing my todos! Best app ever!!!\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GaN4RnqMnxYw",
        "colab_type": "text"
      },
      "source": [
        "We have to use the tokenizer to encode the text:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zA5Or4D2sLc9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoded_review = tokenizer.encode_plus(\n",
        "  review_text,\n",
        "  max_length=MAX_LEN,\n",
        "  add_special_tokens=True,\n",
        "  return_token_type_ids=False,\n",
        "  pad_to_max_length=True,\n",
        "  return_attention_mask=True,\n",
        "  return_tensors='pt',\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "et8xlDrKpH60",
        "colab_type": "text"
      },
      "source": [
        "Let's get the predictions from our model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qr_t3rUksumr",
        "colab_type": "code",
        "outputId": "4a69d750-c56a-40c1-822a-0b3e7df16b3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "input_ids = encoded_review['input_ids'].to(device)\n",
        "attention_mask = encoded_review['attention_mask'].to(device)\n",
        "\n",
        "output = model(input_ids, attention_mask)\n",
        "_, prediction = torch.max(output, dim=1)\n",
        "\n",
        "print(f'Review text: {review_text}')\n",
        "print(f'Sentiment  : {class_names[prediction]}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Review text: I love completing my todos! Best app ever!!!\n",
            "Sentiment  : positive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PVhwzq7bpPRl",
        "colab_type": "text"
      },
      "source": [
        "## Summary\n",
        "\n",
        "Nice job! You learned how to use BERT for sentiment analysis. You built a custom classifier using the Hugging Face library and trained it on our app reviews dataset!\n",
        "\n",
        "- [Read the tutorial](https://www.curiousily.com/posts/sentiment-analysis-with-bert-and-hugging-face-using-pytorch-and-python/)\n",
        "- [Run the notebook in your browser (Google Colab)](https://colab.research.google.com/drive/1PHv-IRLPCtv7oTcIGbsgZHqrB5LPvB7S)\n",
        "- [Read the `Getting Things Done with Pytorch` book](https://github.com/curiousily/Getting-Things-Done-with-Pytorch)\n",
        "\n",
        "You learned how to:\n",
        "\n",
        "- Intuitively understand what BERT is\n",
        "- Preprocess text data for BERT and build PyTorch Dataset (tokenization, attention masks, and padding)\n",
        "- Use Transfer Learning to build Sentiment Classifier using the Transformers library by Hugging Face\n",
        "- Evaluate the model on test data\n",
        "- Predict sentiment on raw text\n",
        "\n",
        "Next, we'll learn how to deploy our trained model behind a REST API and build a simple web app to access it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wf39tauBa2V2",
        "colab_type": "text"
      },
      "source": [
        "## References\n",
        "\n",
        "- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)\n",
        "- [L11 Language Models - Alec Radford (OpenAI)](https://www.youtube.com/watch?v=BnpB3GrpsfM)\n",
        "- [The Illustrated BERT, ELMo, and co.](https://jalammar.github.io/illustrated-bert/)\n",
        "- [BERT Fine-Tuning Tutorial with PyTorch](https://mccormickml.com/2019/07/22/BERT-fine-tuning/)\n",
        "- [How to Fine-Tune BERT for Text Classification?](https://arxiv.org/pdf/1905.05583.pdf)\n",
        "- [Huggingface Transformers](https://huggingface.co/transformers/)\n",
        "- [BERT Explained: State of the art language model for NLP](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270)"
      ]
    }
  ]
}