bert001.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/tweet-sentiment-extraction/sample_submission.csv\n",
      "/kaggle/input/tweet-sentiment-extraction/test.csv\n",
      "/kaggle/input/tweet-sentiment-extraction/train.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 20.2.4 is available.\r\n",
      "You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install -q -U watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -qq transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.7.6\n",
      "IPython 7.13.0\n",
      "\n",
      "numpy 1.18.5\n",
      "pandas 1.1.3\n",
      "torch 1.6.0\n",
      "transformers 3.0.2\n"
     ]
    }
   ],
   "source": [
    "%reload_ext watermark\n",
    "%watermark -v -p numpy,pandas,torch,transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>text</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I`d have responded, if I were going</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text sentiment\n",
       "0                I`d have responded, if I were going   neutral\n",
       "1      Sooo SAD I will miss you here in San Diego!!!  negative\n",
       "2                          my boss is bullying me...  negative\n",
       "3                     what interview! leave me alone  negative\n",
       "4   Sons of ****, why couldn`t they put them on t...  negative"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/kaggle/input/tweet-sentiment-extraction/train.csv\")\n",
    "df = df[['text','sentiment']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8951925a37f148eda45d6cf64ceeecaa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213450.0, style=ProgressStyle(descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "PRE_TRAINED_MODEL_NAME = 'bert-base-cased'\n",
    "tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)\n",
    "sentiment_map = {'negative':0,'neutral':1, 'positive':2}\n",
    "df.sentiment = df.sentiment.apply(lambda x: sentiment_map[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_LEN = 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GPReviewDataset(Dataset):\n",
    "\n",
    "  def __init__(self, text, sentiment, tokenizer, max_len):\n",
    "    self.text = text\n",
    "    self.sentiment = sentiment\n",
    "    self.tokenizer = tokenizer\n",
    "    self.max_len = max_len\n",
    "  \n",
    "  def __len__(self):\n",
    "    return len(self.text)\n",
    "  \n",
    "  def __getitem__(self, item):\n",
    "    text = str(self.text[item])\n",
    "    sentiment = self.sentiment[item]\n",
    "\n",
    "    encoding = self.tokenizer.encode_plus(\n",
    "      text,\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",
    "        truncation='longest_first'\n",
    "    )\n",
    "\n",
    "    return {\n",
    "      'text': text,\n",
    "      'input_ids': encoding['input_ids'].flatten(),\n",
    "      'attention_mask': encoding['attention_mask'].flatten(),\n",
    "      'sentiment': torch.tensor(sentiment, dtype=torch.long)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\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>text</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I`d have responded, if I were going</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27476</th>\n",
       "      <td>wish we could come see u on Denver  husband l...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27477</th>\n",
       "      <td>I`ve wondered about rake to.  The client has ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27478</th>\n",
       "      <td>Yay good for both of you. Enjoy the break - y...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27479</th>\n",
       "      <td>But it was worth it  ****.</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27480</th>\n",
       "      <td>All this flirting going on - The ATG smiles...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27480 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  sentiment\n",
       "0                    I`d have responded, if I were going          1\n",
       "1          Sooo SAD I will miss you here in San Diego!!!          0\n",
       "2                              my boss is bullying me...          0\n",
       "3                         what interview! leave me alone          0\n",
       "4       Sons of ****, why couldn`t they put them on t...          0\n",
       "...                                                  ...        ...\n",
       "27476   wish we could come see u on Denver  husband l...          0\n",
       "27477   I`ve wondered about rake to.  The client has ...          0\n",
       "27478   Yay good for both of you. Enjoy the break - y...          2\n",
       "27479                         But it was worth it  ****.          2\n",
       "27480     All this flirting going on - The ATG smiles...          1\n",
       "\n",
       "[27480 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)\n",
    "df_val, df_test = train_test_split(df_test, test_size=0.5, random_state=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f547787a110>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 467,
       "width": 722
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train.sentiment.value_counts(normalize=True).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f547748fd90>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 467,
       "width": 722
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_val.sentiment.value_counts(normalize=True).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.sentiment.value_counts(normalize=True).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data_loader(df, tokenizer, max_len, batch_size):\n",
    "  ds = GPReviewDataset(\n",
    "    text=df.text.to_numpy(),\n",
    "    sentiment=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=1\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['text', 'input_ids', 'attention_mask', 'sentiment'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = next(iter(train_data_loader))\n",
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7955f3607e5e4f15814d1478a94184c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44020150cf0c453dad2321d2ff79bce6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=435779157.0, style=ProgressStyle(descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "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",
    "    \n",
    "    return self.out(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = sentiment_map.keys()\n",
    "model = SentimentClassifier(len(class_names))\n",
    "\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_ids = data['input_ids'].to(device)\n",
    "attention_mask = data['attention_mask'].to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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",
    "\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[\"sentiment\"].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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "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[\"sentiment\"].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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "----------\n",
      "Train loss 0.6140300456776656 accuracy 0.7465227235969595\n",
      "Val   loss 0.5313228455393814 accuracy 0.7852983988355168\n",
      "\n",
      "Epoch 2/10\n",
      "----------\n",
      "Train loss 0.4034805538453268 accuracy 0.849264111272845\n",
      "Val   loss 0.5841651354418245 accuracy 0.7707423580786027\n",
      "\n",
      "Epoch 3/10\n",
      "----------\n",
      "Train loss 0.273313520139476 accuracy 0.9103590490053373\n",
      "Val   loss 0.7965844692878945 accuracy 0.7707423580786027\n",
      "\n",
      "Epoch 4/10\n",
      "----------\n",
      "Train loss 0.19414973430786425 accuracy 0.9429079734756591\n",
      "Val   loss 1.0289867040105598 accuracy 0.7707423580786027\n",
      "\n",
      "Epoch 5/10\n",
      "----------\n",
      "Train loss 0.14482669324336547 accuracy 0.963043829856057\n",
      "Val   loss 1.2823598516677224 accuracy 0.7620087336244542\n",
      "\n",
      "Epoch 6/10\n",
      "----------\n",
      "Train loss 0.10604079956789549 accuracy 0.9750525634805111\n",
      "Val   loss 1.4134813156845265 accuracy 0.7663755458515285\n",
      "\n",
      "Epoch 7/10\n",
      "----------\n",
      "Train loss 0.08070140504983812 accuracy 0.9821284166262333\n",
      "Val   loss 1.5673519185748288 accuracy 0.7627365356622999\n",
      "\n",
      "Epoch 8/10\n",
      "----------\n",
      "Train loss 0.05695506070209762 accuracy 0.9878699660359049\n",
      "Val   loss 1.649220861292845 accuracy 0.7787481804949055\n",
      "\n",
      "Epoch 9/10\n",
      "----------\n",
      "Train loss 0.04505807672863 accuracy 0.9905790069545528\n",
      "Val   loss 1.7127720948124607 accuracy 0.7729257641921398\n",
      "\n",
      "Epoch 10/10\n",
      "----------\n",
      "Train loss 0.03133775449837519 accuracy 0.9932476144266538\n",
      "Val   loss 1.7410798541400148 accuracy 0.7707423580786027\n",
      "\n",
      "CPU times: user 33min 2s, sys: 13.5 s, total: 33min 15s\n",
      "Wall time: 34min 8s\n"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 506,
       "width": 732
      }
     },
     "output_type": "display_data"
    }
   ],
   "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]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7983988355167395"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "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[\"text\"]\n",
    "      input_ids = d[\"input_ids\"].to(device)\n",
    "      attention_mask = d[\"attention_mask\"].to(device)\n",
    "      targets = d[\"sentiment\"].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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(\n",
    "  model,\n",
    "  test_data_loader\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.79      0.81      0.80       368\n",
      "     neutral       0.78      0.76      0.77       568\n",
      "    positive       0.83      0.84      0.83       438\n",
      "\n",
      "    accuracy                           0.80      1374\n",
      "   macro avg       0.80      0.80      0.80      1374\n",
      "weighted avg       0.80      0.80      0.80      1374\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred, target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 516,
       "width": 725
      }
     },
     "output_type": "display_data"
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "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",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Happy STAR WARS Day\n",
      "\n",
      "True sentiment: positive\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\".join(wrap(review_text)))\n",
    "print()\n",
    "print(f'True sentiment: {list(class_names)[prediction.tolist()[0]]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 489,
       "width": 779
      }
     },
     "output_type": "display_data"
    }
   ],
   "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]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_text = \"My ridiculous dog is amazing\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "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",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review text: My ridiculous dog is amazing\n",
      "Sentiment  : neutral\n"
     ]
    }
   ],
   "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  : {list(class_names)[prediction.tolist()[0]]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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