{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notebook bazuje na \n",
    "# https://github.com/bentrevett/pytorch-sentiment-analysis/blob/master/3%20-%20Faster%20Sentiment%20Analysis.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.8.3\n",
      "  latest version: 4.9.2\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.8.3\n",
      "  latest version: 4.9.2\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "Requirement already satisfied: en_core_web_sm==2.3.1 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz#egg=en_core_web_sm==2.3.1 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (2.3.1)\n",
      "Requirement already satisfied: spacy<2.4.0,>=2.3.0 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from en_core_web_sm==2.3.1) (2.3.2)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.0.4)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (3.0.2)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.25.0)\n",
      "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.8.0)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.19.2)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.5)\n",
      "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.5)\n",
      "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.0)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (4.54.1)\n",
      "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.9.6)\n",
      "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.4.1)\n",
      "Requirement already satisfied: setuptools in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (50.3.1.post20201107)\n",
      "Requirement already satisfied: thinc==7.4.1 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (7.4.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2020.12.5)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.10)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.25.11)\n",
      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the model via spacy.load('en_core_web_sm')\n",
      "\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
      "/home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages/en_core_web_sm -->\n",
      "/home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages/spacy/data/en\n",
      "You can now load the model via spacy.load('en')\n"
     ]
    }
   ],
   "source": [
    "!conda install torchtext -c pytorch -y\n",
    "!conda install spacy -y\n",
    "!python -m spacy download en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages/torchtext/data/field.py:150: UserWarning: Field class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\n",
      "  warnings.warn('{} class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.'.format(self.__class__.__name__), UserWarning)\n",
      "/home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages/torchtext/data/field.py:150: UserWarning: LabelField class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\n",
      "  warnings.warn('{} class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.'.format(self.__class__.__name__), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torchtext import data\n",
    "from torchtext import datasets\n",
    "\n",
    "SEED = 1234\n",
    "\n",
    "torch.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True\n",
    "\n",
    "TEXT = data.Field(tokenize = 'spacy')\n",
    "LABEL = data.LabelField(dtype = torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages/torchtext/data/example.py:78: UserWarning: Example class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\n",
      "  warnings.warn('Example class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.', UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)\n",
    "\n",
    "train_data, valid_data = train_data.split(random_state = random.seed(SEED))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 17500\n",
      "Number of validation examples: 7500\n",
      "Number of testing examples: 25000\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of training examples: {len(train_data)}')\n",
    "print(f'Number of validation examples: {len(valid_data)}')\n",
    "print(f'Number of testing examples: {len(test_data)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'text': ['Why', 'do', 'people', 'who', 'do', 'not', 'know', 'what', 'a', 'particular', 'time', 'in', 'the', 'past', 'was', 'like', 'feel', 'the', 'need', 'to', 'try', 'to', 'define', 'that', 'time', 'for', 'others', '?', 'Replace', 'Woodstock', 'with', 'the', 'Civil', 'War', 'and', 'the', 'Apollo', 'moon', '-', 'landing', 'with', 'the', 'Titanic', 'sinking', 'and', 'you', \"'ve\", 'got', 'as', 'realistic', 'a', 'flick', 'as', 'this', 'formulaic', 'soap', 'opera', 'populated', 'entirely', 'by', 'low', '-', 'life', 'trash', '.', 'Is', 'this', 'what', 'kids', 'who', 'were', 'too', 'young', 'to', 'be', 'allowed', 'to', 'go', 'to', 'Woodstock', 'and', 'who', 'failed', 'grade', 'school', 'composition', 'do', '?', '\"', 'I', \"'ll\", 'show', 'those', 'old', 'meanies', ',', 'I', \"'ll\", 'put', 'out', 'my', 'own', 'movie', 'and', 'prove', 'that', 'you', 'do', \"n't\", 'have', 'to', 'know', 'nuttin', 'about', 'your', 'topic', 'to', 'still', 'make', 'money', '!', '\"', 'Yeah', ',', 'we', 'already', 'know', 'that', '.', 'The', 'one', 'thing', 'watching', 'this', 'film', 'did', 'for', 'me', 'was', 'to', 'give', 'me', 'a', 'little', 'insight', 'into', 'underclass', 'thinking', '.', 'The', 'next', 'time', 'I', 'see', 'a', 'slut', 'in', 'a', 'bar', 'who', 'looks', 'like', 'Diane', 'Lane', ',', 'I', \"'m\", 'running', 'the', 'other', 'way', '.', 'It', \"'s\", 'child', 'abuse', 'to', 'let', 'parents', 'that', 'worthless', 'raise', 'kids', '.', 'It', \"'s\", 'audience', 'abuse', 'to', 'simply', 'stick', 'Woodstock', 'and', 'the', 'moonlanding', 'into', 'a', 'flick', 'as', 'if', 'that', 'ipso', 'facto', 'means', 'the', 'film', 'portrays', '1969', '.'], 'label': 'neg'}\n"
     ]
    }
   ],
   "source": [
    "print(vars(train_data.examples[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "MAX_VOCAB_SIZE = 25_000\n",
    "\n",
    "TEXT.build_vocab(train_data, max_size = MAX_VOCAB_SIZE)\n",
    "\n",
    "LABEL.build_vocab(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique tokens in TEXT vocabulary: 25002\n",
      "Unique tokens in LABEL vocabulary: 2\n"
     ]
    }
   ],
   "source": [
    "print(f\"Unique tokens in TEXT vocabulary: {len(TEXT.vocab)}\")\n",
    "print(f\"Unique tokens in LABEL vocabulary: {len(LABEL.vocab)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('the', 203172), (',', 192039), ('.', 165889), ('a', 109265), ('and', 109192), ('of', 100241), ('to', 93511), ('is', 76322), ('in', 61299), ('I', 54013), ('it', 53609), ('that', 48928), ('\"', 44101), (\"'s\", 43213), ('this', 42383), ('-', 36691), ('/><br', 35471), ('was', 34989), ('as', 30252), ('with', 30012)]\n"
     ]
    }
   ],
   "source": [
    "print(TEXT.vocab.freqs.most_common(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['<unk>', '<pad>', 'the', ',', '.', 'a', 'and', 'of', 'to', 'is']\n"
     ]
    }
   ],
   "source": [
    "print(TEXT.vocab.itos[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(None, {'neg': 0, 'pos': 1})\n"
     ]
    }
   ],
   "source": [
    "print(LABEL.vocab.stoi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages/torchtext/data/iterator.py:48: UserWarning: BucketIterator class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\n",
      "  warnings.warn('{} class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.'.format(self.__class__.__name__), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 64\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(\n",
    "    (train_data, valid_data, test_data), \n",
    "    batch_size = BATCH_SIZE, \n",
    "    device = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class FastText(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, output_dim, pad_idx):\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_idx)\n",
    "        \n",
    "        self.fc = nn.Linear(embedding_dim, output_dim)\n",
    "        \n",
    "    def forward(self, text):\n",
    "        \n",
    "        #text = [sent len, batch size]\n",
    "        \n",
    "        embedded = self.embedding(text)\n",
    "                \n",
    "        #embedded = [sent len, batch size, emb dim]\n",
    "        \n",
    "        embedded = embedded.permute(1, 0, 2)\n",
    "        \n",
    "        #embedded = [batch size, sent len, emb dim]\n",
    "        \n",
    "        pooled = F.avg_pool2d(embedded, (embedded.shape[1], 1)).squeeze(1) \n",
    "        \n",
    "        #pooled = [batch size, embedding_dim]\n",
    "                \n",
    "        return torch.sigmoid(self.fc(pooled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_DIM = len(TEXT.vocab)\n",
    "EMBEDDING_DIM = 100\n",
    "OUTPUT_DIM = 1\n",
    "PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]\n",
    "\n",
    "model = FastText(INPUT_DIM, EMBEDDING_DIM, OUTPUT_DIM, PAD_IDX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 2,500,301 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "print(f'The model has {count_parameters(model):,} trainable parameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "UNK_IDX = TEXT.vocab.stoi[TEXT.unk_token]\n",
    "\n",
    "model.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM)\n",
    "model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "optimizer = optim.Adam(model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.BCELoss()\n",
    "\n",
    "model = model.to(device)\n",
    "criterion = criterion.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_accuracy(preds, y):\n",
    "    \"\"\"\n",
    "    Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8\n",
    "    \"\"\"\n",
    "\n",
    "    #round predictions to the closest integer\n",
    "    rounded_preds = torch.round(preds)\n",
    "    correct = (rounded_preds == y).float() #convert into float for division \n",
    "    acc = correct.sum() / len(correct)\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, iterator, optimizer, criterion):\n",
    "    \n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    for batch in iterator:\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        predictions = model(batch.text).squeeze(1)\n",
    "        \n",
    "        loss = criterion(predictions, batch.label)\n",
    "        \n",
    "        acc = binary_accuracy(predictions, batch.label)\n",
    "        \n",
    "        loss.backward()\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "        epoch_loss += loss.item()\n",
    "        epoch_acc += acc.item()\n",
    "        \n",
    "    return epoch_loss / len(iterator), epoch_acc / len(iterator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, iterator, criterion):\n",
    "    \n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    \n",
    "    model.eval()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "    \n",
    "        for batch in iterator:\n",
    "\n",
    "            predictions = model(batch.text).squeeze(1)\n",
    "            \n",
    "            loss = criterion(predictions, batch.label)\n",
    "            \n",
    "            acc = binary_accuracy(predictions, batch.label)\n",
    "\n",
    "            epoch_loss += loss.item()\n",
    "            epoch_acc += acc.item()\n",
    "        \n",
    "    return epoch_loss / len(iterator), epoch_acc / len(iterator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "def epoch_time(start_time, end_time):\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_mins = int(elapsed_time / 60)\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
    "    return elapsed_mins, elapsed_secs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages/torchtext/data/batch.py:23: UserWarning: Batch class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\n",
      "  warnings.warn('{} class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.'.format(self.__class__.__name__), UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 01 | Epoch Time: 0m 40s\n",
      "\tTrain Loss: 0.686 | Train Acc: 59.60%\n",
      "\t Val. Loss: 0.630 |  Val. Acc: 67.82%\n",
      "Epoch: 02 | Epoch Time: 0m 37s\n",
      "\tTrain Loss: 0.639 | Train Acc: 74.52%\n",
      "\t Val. Loss: 0.502 |  Val. Acc: 75.98%\n"
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 3\n",
    "\n",
    "best_valid_loss = float('inf')\n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "\n",
    "    start_time = time.time()\n",
    "    \n",
    "    train_loss, train_acc = train(model, train_iterator, optimizer, criterion)\n",
    "    valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)\n",
    "    \n",
    "    end_time = time.time()\n",
    "\n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "    \n",
    "    if valid_loss < best_valid_loss:\n",
    "        best_valid_loss = valid_loss\n",
    "        torch.save(model.state_dict(), 'model.pt')\n",
    "    \n",
    "    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_state_dict(torch.load('model.pt'))\n",
    "\n",
    "test_loss, test_acc = evaluate(model, test_iterator, criterion)\n",
    "\n",
    "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "nlp = spacy.load('en')\n",
    "\n",
    "def predict_sentiment(model, sentence):\n",
    "    model.eval()\n",
    "    tokenized = [tok.text for tok in nlp.tokenizer(sentence)]\n",
    "    indexed = [TEXT.vocab.stoi[t] for t in tokenized]\n",
    "    tensor = torch.LongTensor(indexed).to(device)\n",
    "    tensor = tensor.unsqueeze(1)\n",
    "    prediction = model(tensor)\n",
    "    return prediction.item()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example negative review..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_sentiment(model, \"This film is terrible\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example positive review..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_sentiment(model, \"This film is great\")"
   ]
  }
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