BERT_PSG.ipynb

{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4S72s27e4uKK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9cfe2c2-afe3-4bbb-fb90-ac5f22f28368"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.4 MB 4.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 101 kB 14.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 66.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 47.7 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -qq transformers\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tPyxSw2V6xnr"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n",
        "import torch\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",
        "from torch import nn, optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
        "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
        "rcParams['figure.figsize'] = 12, 8\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "YMsFqsow7Baw",
        "outputId": "323c8cda-8058-4d6a-f00a-0361ee0ee2f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          clean_text  category\n",
              "0  when modi promised “minimum government maximum...      -1.0\n",
              "1  talk all the nonsense and continue all the dra...       0.0\n",
              "2  what did just say vote for modi  welcome bjp t...       1.0\n",
              "3  asking his supporters prefix chowkidar their n...       1.0\n",
              "4  answer who among these the most powerful world...       1.0"
            ],
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              "      <th></th>\n",
              "      <th>clean_text</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>when modi promised “minimum government maximum...</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>talk all the nonsense and continue all the dra...</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>what did just say vote for modi  welcome bjp t...</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>asking his supporters prefix chowkidar their n...</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>answer who among these the most powerful world...</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
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              "          const element = document.querySelector('#df-1744a981-9c84-43ec-9b67-3ed7a4929fc0');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 3
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      ],
      "source": [
        "url=\"https://raw.githubusercontent.com/ShethNidhi06/Twitter_Data/main/Twitter_Data.csv\"\n",
        "df=pd.read_csv(url)\n",
        "df=df.dropna()\n",
        "df.head()"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "9_ns4MwA8WXU",
        "outputId": "7a3bba55-f70a-4a72-fd73-56fe9efacfdc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 162969 entries, 0 to 162979\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count   Dtype  \n",
            "---  ------      --------------   -----  \n",
            " 0   clean_text  162969 non-null  object \n",
            " 1   category    162969 non-null  float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 3.7+ MB\n"
          ]
        }
      ],
      "source": [
        "\n",
        "df.shape\n",
        "df.info()\n",
        "df['category'].isnull().sum()\n",
        "def to_sentiment(rating):\n",
        "   rating = int(rating) \n",
        "   if rating == -1: \n",
        "     return 0 \n",
        "   elif rating == 0: \n",
        "     return 1 \n",
        "   else: \n",
        "     return 2 \n",
        "df['sentiment'] = df.category.apply(to_sentiment)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DpUKzQRY8Zat"
      },
      "source": [
        "def to_sentiment(rating):\n",
        "  rating = int(rating)\n",
        "  if rating == -1:\n",
        "    return 0\n",
        "  elif rating == 0:\n",
        "    return 1\n",
        "  else:\n",
        "    return 2\n",
        "df['sentiment'] = df.category.apply(to_sentiment)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "07VLM6_q8aPs",
        "outputId": "c7704b37-c314-4605-aa52-9ccc2d47d96c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "image/png": {
              "width": 753,
              "height": 489
            }
          }
        }
      ],
      "source": [
        "sns.countplot(df.sentiment)\n",
        "plt.xlabel('review-score');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1n2jdyTEo3eD"
      },
      "outputs": [],
      "source": [
        "PRE_TRAINED_MODEL_NAME = 'bert-base-cased'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1OpcO2zW8hjn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "f20e5a75c27048c4b3d81b025d5ce586",
            "f8e5d0e7d30e44ad885d71b5d73efcb1",
            "772c6c1e50d84397981b4469e9a39976",
            "3381fd101f8848089092b2c4d520a56d",
            "e94c2927dd2b4e31863fd7338f7ae08b",
            "c1d721740a524a6aa39af1e44621a69e",
            "1f462ae3012c4c218d02df94b361d2ce",
            "2372f17cdc844cd39f7268525d5e5592",
            "c8b0ecdcf8bb4f8e9190bcde55ec5b61",
            "b7d725f6ffe64796a8b094108c316b4c",
            "f5bf15d63a604da1a8405545a560d6ed",
            "bd48aae064e041f4b543970a134e60e1",
            "b07dbcca94a4485b8106caf1c071b204",
            "634a0f803afa4bd8b4cfee435b25b497",
            "945b650d06fa49cd9dde0f6aa47cffa2",
            "35220861a14141218607ee23a339db1a",
            "65fb018fa66644129792112fe6410411",
            "698c38ce5bf84fdeb72547ccd467ede4",
            "7085657c600344a6be92a18ccb8028d1",
            "9d9eafb9f74646a5b19a72718cdb0518",
            "4cfe017cff364f949584128cfe55c3cf",
            "7f8c1058f1b4498c915de124376fa4ae",
            "ff89f625a15343beaad9893a30069bd9",
            "01f3f26ffc334ce1b3ff8a8af94c49d6",
            "29319c6b461f47c2a4a4a12e62ff8b10",
            "cf29bea0f5fc4f7f862185e072eb43a0",
            "80d84e0b64f74e8496c54d3c82836a63",
            "27edeeb1a09a40d5aeb05daedd4f0c83",
            "fa4d333690bc4676a7e163dceab05cdf",
            "6beb7d8a7c5b49dcabbbc24959e16511",
            "f73c53ec064c42d89d1a1a144b66bb6a",
            "6c70e5abd87a46e49d7ddf6fd02cb71a",
            "fdb9efd4b76943088778142763ad0aab"
          ]
        },
        "outputId": "2dfe9332-14a9-4c4e-8721-0583bc6f5e9c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/208k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f20e5a75c27048c4b3d81b025d5ce586"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd48aae064e041f4b543970a134e60e1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff89f625a15343beaad9893a30069bd9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME, return_dict=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zqIUgkBe8vGh"
      },
      "outputs": [],
      "source": [
        "sample_txt = 'When was I last outside? I am stuck at home for 2 weeks.'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuUGBJQ699Ry",
        "outputId": "11d97608-7f95-42df-cc7c-3075bf982bf9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ],
      "source": [
        "tokens = tokenizer.tokenize(sample_txt)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "print(f' Sentence: {sample_txt}')\n",
        "print(f'   Tokens: {tokens}')\n",
        "print(f'Token IDs: {token_ids}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0uEshpHd-isL",
        "outputId": "26912e20-edee-49e4-9ad4-04105d6cb709"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2318: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask'])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "encoding = tokenizer.encode_plus(\n",
        "  \n",
        "  sample_txt,\n",
        "  max_length=32,\n",
        "  add_special_tokens=True, # Add '[CLS]' and '[SEP]'\n",
        "  return_token_type_ids=False,\n",
        "  padding=True,\n",
        "  return_attention_mask=True,\n",
        "  return_tensors='pt',  # Return PyTorch tensors\n",
        ")\n",
        "encoding.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fCkJpyR_GDg",
        "outputId": "e411fe72-523b-452d-a908-f6c8bce49ec2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17\n"
          ]
        },
        {
          "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])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "print(len(encoding['input_ids'][0]))\n",
        "encoding['input_ids'][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mMtM9bxlkFz"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHUU_eJrlk5o",
        "outputId": "529998a0-522e-458d-f8bf-df25207e798d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17\n"
          ]
        },
        {
          "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]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "print(len(encoding['attention_mask'][0]))\n",
        "encoding['attention_mask']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NiErfnTBngRf",
        "outputId": "08df0b35-c66a-45f9-937a-be2b9c0bfb54"
      },
      "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]']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "tokenizer.convert_ids_to_tokens(encoding['input_ids'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LD-KI403oWAZ",
        "outputId": "bdaf0096-baa1-4bec-9f09-bed0e316eb19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"
          ]
        }
      ],
      "source": [
        "token_lens = []\n",
        "for txt in df.clean_text:\n",
        "  tokens = tokenizer.encode(str(txt), max_length=512)\n",
        "  token_lens.append(len(tokens))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "tTrCAMxziWyf",
        "outputId": "9bd8ef26-88c3-48f1-b816-ffb6c03a5af0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 749,
              "height": 489
            }
          }
        }
      ],
      "source": [
        "sns.distplot(token_lens)\n",
        "plt.xlim([0, 256]);\n",
        "plt.xlabel('Token count');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gC5RIHTO-zYN"
      },
      "outputs": [],
      "source": [
        "MAX_LEN = 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3q09bA3L_VvR"
      },
      "outputs": [],
      "source": [
        "class GPReviewDataset(Dataset):\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",
        "  def __len__(self):\n",
        "    return len(self.reviews)\n",
        "  def __getitem__(self, item):\n",
        "    review = str(self.reviews[item])\n",
        "    target = self.targets[item]\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",
        "    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",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dCZTmXenAWvv"
      },
      "outputs": [],
      "source": [
        "df_train, df_test = train_test_split(\n",
        "  df,\n",
        "  test_size=0.1,\n",
        "  random_state=RANDOM_SEED\n",
        ")\n",
        "df_val, df_test = train_test_split(\n",
        "  df_test,\n",
        "  test_size=0.5,\n",
        "  random_state=RANDOM_SEED\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6nxsCIsYAadz"
      },
      "outputs": [],
      "source": [
        "def create_data_loader(df, tokenizer, max_len, batch_size):\n",
        "  ds = GPReviewDataset(\n",
        "    reviews=df.clean_text.to_numpy(),\n",
        "    targets=df.sentiment.to_numpy(),\n",
        "    tokenizer=tokenizer,\n",
        "    max_len=max_len\n",
        "  )\n",
        "  return DataLoader(\n",
        "    ds,\n",
        "    batch_size=batch_size,\n",
        "    num_workers=2\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Th4cpw_cAdiR"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 16\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)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8yByW54ArAy",
        "outputId": "416b9403-7b6c-4dbf-f056-495ec568a6e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['review_text', 'input_ids', 'attention_mask', 'targets'])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "data = next(iter(train_data_loader))\n",
        "data.keys()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtfJC6ioBEj0",
        "outputId": "c2df5294-e640-4271-9501-77f301ed4032"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([16, 100])\n",
            "torch.Size([16, 100])\n",
            "torch.Size([16])\n"
          ]
        }
      ],
      "source": [
        "print(data['input_ids'].shape)\n",
        "print(data['attention_mask'].shape)\n",
        "print(data['targets'].shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
          "referenced_widgets": [
            "f9d9cfb983ca4675939e8a0be4734307",
            "8a873a6219a948bbbc7f1889e439a344",
            "ef3f85918e2e4dd1bfcc7fdc13b68db7",
            "71e243ea47c74740bb11065bedc1c8c9",
            "124046efa5584c0cbe43edbe0d593940",
            "7bfc7d538cb8478eb6d217c1d451d5fa",
            "f879999a1ab6437bb18a569d583190e6",
            "f22b57a925f142b1b4a901d0e98eb21d",
            "132d066ff3b6477c8c510cb3b89b66ed",
            "0bdfa11d3b3540539818206409ac5165",
            "c04485ad173a4534a4c1dc52f11138f2"
          ]
        },
        "id": "hjDho5AlBVAc",
        "outputId": "21a1a62b-a0d2-4984-a871-9b64f8442bd0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/416M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f9d9cfb983ca4675939e8a0be4734307"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME,return_dict=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tFx8ZpDrB6rt"
      },
      "outputs": [],
      "source": [
        "last_hidden_state, pooled_output = bert_model(\n",
        "  input_ids=encoding['input_ids'],\n",
        "  attention_mask=encoding['attention_mask']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71snrEvwB9Ui",
        "outputId": "d02f3042-6f5d-44de-a0b7-e80250c96c46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 17, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "last_hidden_state.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5Zzq9xqCCwd",
        "outputId": "62721230-8d64-4aec-fb2f-a5229b43486a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "bert_model.config.hidden_size\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIgW8bIbCLQc",
        "outputId": "771e21c9-f385-40da-cd12-77a56d403eaa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "pooled_output.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q8a9n2pkh-rO"
      },
      "outputs": [],
      "source": [
        "class SentimentClassifier(nn.Module):\n",
        "  def __init__(self, n_classes):\n",
        "    super(SentimentClassifier, self).__init__()\n",
        "    self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME, return_dict=False)\n",
        "    self.drop = nn.Dropout(p=0.3)\n",
        "    self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EHIOsGmiKsT",
        "outputId": "9a97f231-9d29-410e-ee4b-83684eaeef49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "model = SentimentClassifier(3)\n",
        "model = model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tAb76Q-SiPfO",
        "outputId": "31865b63-cb18-429a-a605-40ee122b7406"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([16, 100])\n",
            "torch.Size([16, 100])\n"
          ]
        }
      ],
      "source": [
        "input_ids = data['input_ids'].to(device)\n",
        "attention_mask = data['attention_mask'].to(device)\n",
        "print(input_ids.shape) # batch size x seq length\n",
        "print(attention_mask.shape) # batch size x seq length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_FxzOl0Jjkmt",
        "outputId": "8cb9c413-876c-4a2d-eace-c37535451dbe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.2410, 0.4455, 0.3136],\n",
              "        [0.2821, 0.2673, 0.4506],\n",
              "        [0.3770, 0.2228, 0.4001],\n",
              "        [0.2188, 0.3663, 0.4149],\n",
              "        [0.5862, 0.1901, 0.2237],\n",
              "        [0.2593, 0.3743, 0.3664],\n",
              "        [0.2579, 0.3443, 0.3978],\n",
              "        [0.3789, 0.2749, 0.3462],\n",
              "        [0.3700, 0.1901, 0.4399],\n",
              "        [0.3139, 0.2184, 0.4677],\n",
              "        [0.3462, 0.2141, 0.4397],\n",
              "        [0.3448, 0.1951, 0.4600],\n",
              "        [0.2342, 0.3940, 0.3718],\n",
              "        [0.2503, 0.3069, 0.4428],\n",
              "        [0.3402, 0.2390, 0.4208],\n",
              "        [0.1620, 0.3459, 0.4922]], device='cuda:0', grad_fn=<SoftmaxBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "from torch.nn import functional as F\n",
        "F.softmax(model(input_ids, attention_mask), dim=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooVsPLGVjoXC",
        "outputId": "49583b09-5dd0-4981-c776-6ca1fcd8ef5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 5\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)\n",
        "total_steps = len(train_data_loader) * EPOCHS\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "  optimizer,\n",
        "  num_warmup_steps=0,\n",
        "  num_training_steps=total_steps\n",
        ")\n",
        "loss_fn = nn.CrossEntropyLoss().to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F8_N_FoSuVCz"
      },
      "outputs": [],
      "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",
        "  losses = []\n",
        "  correct_predictions = 0\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",
        "    outputs = model(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    loss = loss_fn(outputs, targets)\n",
        "    correct_predictions += torch.sum(preds == targets)\n",
        "    losses.append(loss.item())\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",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z7F2mt68uZq1"
      },
      "outputs": [],
      "source": [
        "def eval_model(model, data_loader, loss_fn, device, n_examples):\n",
        "  model = model.eval()\n",
        "  losses = []\n",
        "  correct_predictions = 0\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",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "      loss = loss_fn(outputs, targets)\n",
        "      correct_predictions += torch.sum(preds == targets)\n",
        "      losses.append(loss.item())\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKHEcsvOugJ0",
        "outputId": "6acb3a0b-336e-4e80-afa0-e67a890db9c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss 0.27953443949545786 accuracy 0.9133713319515654\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val   loss 0.19187128786750904 accuracy 0.9572901325478644\n",
            "\n",
            "Epoch 2/5\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss 0.10842709229439049 accuracy 0.9734918730227992\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val   loss 0.13810955809227274 accuracy 0.9729995090819833\n",
            "\n",
            "Epoch 3/5\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss 0.0710047009476826 accuracy 0.9837528635322352\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val   loss 0.12284604992464129 accuracy 0.9755768286696122\n",
            "\n",
            "Epoch 4/5\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss 0.04668692803046557 accuracy 0.9896435584160577\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val   loss 0.09324776490915718 accuracy 0.9810996563573883\n",
            "\n",
            "Epoch 5/5\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss 0.03127510907812303 accuracy 0.9933456965201266\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val   loss 0.09539169470911421 accuracy 0.9830633284241531\n",
            "\n"
          ]
        }
      ],
      "source": [
        "history = defaultdict(list)\n",
        "best_accuracy = 0\n",
        "for epoch in range(EPOCHS):\n",
        "  print(f'Epoch {epoch + 1}/{EPOCHS}')\n",
        "  print('-' * 10)\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",
        "  print(f'Train loss {train_loss} accuracy {train_acc}')\n",
        "  val_acc, val_loss = eval_model(\n",
        "    model,\n",
        "    val_data_loader,\n",
        "    loss_fn,\n",
        "    device,\n",
        "    len(df_val)\n",
        "  )\n",
        "  print(f'Val   loss {val_loss} accuracy {val_acc}')\n",
        "  print()\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",
        "  if val_acc > best_accuracy:\n",
        "    torch.save(model.state_dict(), 'best_model_state.bin')\n",
        "    best_accuracy = val_acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1xlf6NYAuluO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d50c2e1c-153d-4b19-e3c8-27967a7f465e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.43539084550251567"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "test_acc, _ = eval_model(\n",
        "  model,\n",
        "  test_data_loader,\n",
        "  loss_fn,\n",
        "  device,\n",
        "  len(df_test)\n",
        ")\n",
        "test_acc.item()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_predictions(model, data_loader):\n",
        "  model = model.eval()\n",
        "  review_texts = []\n",
        "  predictions = []\n",
        "  prediction_probs = []\n",
        "  real_values = []\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\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",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "      review_texts.extend(texts)\n",
        "      predictions.extend(preds)\n",
        "      prediction_probs.extend(outputs)\n",
        "      real_values.extend(targets)\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"
      ],
      "metadata": {
        "id": "8bluXFijdqEq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(\n",
        "  model,\n",
        "  test_data_loader\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-AQ5EKKd7UQ",
        "outputId": "71bcc102-821e-4d00-e83a-4251ecffb489"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['negative', 'neutral', 'positive']\n",
        "print(classification_report(y_test, y_pred, target_names=class_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaH3oZiRd8z2",
        "outputId": "1181fecb-085b-44b3-ac92-2e2c69d043fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.00      0.00      0.00      1776\n",
            "     neutral       0.41      0.00      0.01      2825\n",
            "    positive       0.44      1.00      0.61      3548\n",
            "\n",
            "    accuracy                           0.44      8149\n",
            "   macro avg       0.28      0.33      0.20      8149\n",
            "weighted avg       0.33      0.44      0.27      8149\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
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