{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lIYdn1woOS1n",
    "outputId": "cece5524-0d94-4cc4-b260-23e2f0ecc744"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Y2upWg_Qvax1"
   },
   "outputs": [],
   "source": [
    "import functools\n",
    "\n",
    "import datasets\n",
    "\n",
    "import torchtext\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZIVeVqVUvdcK",
    "outputId": "db0dbf36-4a75-4d30-bcef-8cb52b7a5b30"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5304f46c35d4fe6985cf45389babfda",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.92k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afaae0e285b84b6caf5c65779b42f6ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.02 MiB, post-processed: Unknown size, total: 207.25 MiB) to /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ed6be629b54466f81d4bf603d23afef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/84.1M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset imdb downloaded and prepared to /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a. Subsequent calls will reuse this data.\n"
     ]
    }
   ],
   "source": [
    "train_data, test_data = datasets.load_dataset('imdb', split=['train', 'test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2f6USIQOvkV_",
    "outputId": "3444d1ad-e2eb-4766-92d8-ea4d85f7011f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Dataset({\n",
       "     features: ['label', 'text'],\n",
       "     num_rows: 25000\n",
       " }),\n",
       " Dataset({\n",
       "     features: ['label', 'text'],\n",
       "     num_rows: 25000\n",
       " }))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data, test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "3Av1UUtZxoSL"
   },
   "outputs": [],
   "source": [
    "tokenizer = torchtext.data.utils.get_tokenizer('basic_english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "ju4G0eKEx2RO"
   },
   "outputs": [],
   "source": [
    "def tokenize_data(example, tokenizer):\n",
    "    tokens = {'tokens': tokenizer(example['text'])}\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T6RecxI4xs7s",
    "outputId": "0c97c5cb-7bca-4664-98fa-5b665ab510c9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1f91bc27c104b679ee954c62e6b33f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25000 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aaa75c602f5d46b78a8c79a023f66db2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25000 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data = train_data.map(tokenize_data, fn_kwargs={'tokenizer': tokenizer})\n",
    "test_data = test_data.map(tokenize_data, fn_kwargs={'tokenizer': tokenizer})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-Foj4qesxqiz",
    "outputId": "09fc27eb-0f50-45df-aacb-debf53948cdc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Dataset({\n",
       "     features: ['label', 'text', 'tokens'],\n",
       "     num_rows: 25000\n",
       " }),\n",
       " Dataset({\n",
       "     features: ['label', 'text', 'tokens'],\n",
       "     num_rows: 25000\n",
       " }))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data, test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "OfzYwhN2wFcA"
   },
   "outputs": [],
   "source": [
    "train_valid_data = train_data.train_test_split(test_size=0.25)\n",
    "train_data = train_valid_data['train']\n",
    "valid_data = train_valid_data['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ovGkrOJkwZsC",
    "outputId": "b5ca62c6-96e5-4eca-8c0f-558fd087dee1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18750, 6250, 25000)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data), len(valid_data), len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "9VMeQG_FxUVO"
   },
   "outputs": [],
   "source": [
    "min_freq = 3\n",
    "special_tokens = ['<unk>', '<pad>']\n",
    "\n",
    "vocab = torchtext.vocab.build_vocab_from_iterator(train_data['tokens'],\n",
    "                                                  min_freq=min_freq,\n",
    "                                                  specials=special_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rbroBAClxXGB",
    "outputId": "91c5da92-7f97-4ad8-a946-6de1899e64a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35341"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3bKHqCxPyQSb",
    "outputId": "35b3c437-f0f8-43fb-8f10-b968c597d5b4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<unk>', '<pad>', 'the', '.', ',', 'and', 'a', 'of', 'to', \"'\"]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab.get_itos()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uStvd2szyUGR",
    "outputId": "d4c9d2a4-86a9-413f-9200-5f1a27ece925"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unk_index = vocab['<unk>']\n",
    "\n",
    "unk_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gd5R8NCJyws4",
    "outputId": "46666a6d-56ff-42e3-ebe2-6f87930270c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pad_index = vocab['<pad>']\n",
    "\n",
    "pad_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "_syj_YR8yp7B"
   },
   "outputs": [],
   "source": [
    "vocab.set_default_index(unk_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "ENlE1eAM0lHe"
   },
   "outputs": [],
   "source": [
    "def numericalize_data(example, vocab):\n",
    "    ids = {'ids': [vocab[token] for token in example['tokens']]}\n",
    "    return ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164,
     "referenced_widgets": [
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      "a26e53975a1142828b8425895847b47b",
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      "fdc8037ddba74cb7993f6f986e491894",
      "398dcca3803c46beb2854bd52d66e322",
      "f17e77bdd542441caf8d1d427c360601",
      "0cf052dd17f54df19b0e5de1891ec1a4",
      "32aa00f0d71b4655880a19ad08b9f02d",
      "3c20326b78dc4e458bacb85742002a9a",
      "96e6499f54c348628bc33ff48fd1ee6a",
      "d52ef97893144dffa64d1da2b8ea52e6",
      "d0832872ea074689b8c7e3b8b647e68d",
      "b7fc360206ca42f5b51eacb4f1de763d",
      "eeac1f30dad64508ba7f1a9473f390bd",
      "958e2685a018455b9ea02803ca96b464"
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    },
    "id": "ux_YLzDA069-",
    "outputId": "11d67399-ace5-49ed-8327-0f85de3386a6"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "416782d88fee4bed84a1a08c59f4e549",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/18750 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec6fef000aa447b794f102eda085ce84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/6250 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28feaa784dda446ba6001d82dfe9514f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25000 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data = train_data.map(numericalize_data, fn_kwargs={'vocab': vocab})\n",
    "valid_data = valid_data.map(numericalize_data, fn_kwargs={'vocab': vocab})\n",
    "test_data = test_data.map(numericalize_data, fn_kwargs={'vocab': vocab})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "GAXGqlXT1FD9"
   },
   "outputs": [],
   "source": [
    "train_data.set_format(type='torch', columns=['ids', 'label'])\n",
    "valid_data.set_format(type='torch', columns=['ids', 'label'])\n",
    "test_data.set_format(type='torch', columns=['ids', 'label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "qCmyoFKAvmnj"
   },
   "outputs": [],
   "source": [
    "class NBoW(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, output_dim, pad_index):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_index)\n",
    "        self.fc = nn.Linear(embedding_dim, output_dim)\n",
    "\n",
    "    def forward(self, text):\n",
    "        # text = [batch size, seq len]\n",
    "        embedded = self.embedding(text)\n",
    "        # embedded = [batch size, seq len, embedding dim]\n",
    "        pooled = embedded.mean(dim=1)\n",
    "        # pooled = [batch size, embedding dim]\n",
    "        prediction = self.fc(pooled)\n",
    "        # prediction = [batch size, output dim]\n",
    "        return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "lPym0qxrwC7e"
   },
   "outputs": [],
   "source": [
    "vocab_size = len(vocab)\n",
    "embedding_dim = 256\n",
    "output_dim = 2\n",
    "\n",
    "model = NBoW(vocab_size, embedding_dim, output_dim, pad_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Prvx6C3TFyI4",
    "outputId": "c9069fbe-ec6e-423d-ff59-820f934c6432"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".vector_cache/wiki.en.vec:   7%|▋         | 465M/6.60G [08:18<3:21:18, 508kB/s]    "
     ]
    }
   ],
   "source": [
    "vectors = torchtext.vocab.FastText()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e3gI343FIETN",
    "outputId": "44ed85d4-3e22-4bf0-e86e-0d7c01e33c35"
   },
   "outputs": [],
   "source": [
    "vectors = torchtext.vocab.GloVe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xLg8TKFCzAfL"
   },
   "outputs": [],
   "source": [
    "optimizer = optim.Adam(model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XJrIlwlfzQqY"
   },
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KbEREGWmzR7J",
    "outputId": "44ec5013-ab39-4f35-b425-250f7e4093ef"
   },
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IBtd-0I3zVTo"
   },
   "outputs": [],
   "source": [
    "model = model.to(device)\n",
    "criterion = criterion.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TNaDUz3M2QDM"
   },
   "outputs": [],
   "source": [
    "def collate(batch, pad_index):\n",
    "    batch_ids = [i['ids'] for i in batch]\n",
    "    batch_ids = nn.utils.rnn.pad_sequence(batch_ids, padding_value=pad_index, batch_first=True)\n",
    "    batch_labels = [i['label'] for i in batch]\n",
    "    batch_labels = torch.stack(batch_labels)\n",
    "    batch = {'ids': batch_ids,\n",
    "             'labels': batch_labels}\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LYsAzjrV0AnH"
   },
   "outputs": [],
   "source": [
    "batch_size = 512\n",
    "\n",
    "collate = functools.partial(collate, pad_index=pad_index)\n",
    "\n",
    "train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, collate_fn=collate)\n",
    "valid_dataloader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size, collate_fn=collate)\n",
    "test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, collate_fn=collate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mKeLtjK5zX41"
   },
   "outputs": [],
   "source": [
    "def train(dataloader, model, criterion, optimizer, device):\n",
    "\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    epoch_accuracy = 0\n",
    "\n",
    "    for batch in dataloader:\n",
    "        tokens = batch['ids'].to(device)\n",
    "        labels = batch['labels'].to(device)\n",
    "        predictions = model(tokens)\n",
    "        loss = criterion(predictions, labels)\n",
    "        accuracy = get_accuracy(predictions, labels)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "        epoch_accuracy += accuracy.item()\n",
    "\n",
    "    return epoch_loss / len(dataloader), epoch_accuracy / len(dataloader)"
   ]
  },
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    "def evaluate(dataloader, model, criterion, device):\n",
    "    \n",
    "    model.eval()\n",
    "    epoch_loss = 0\n",
    "    epoch_accuracy = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for batch in dataloader:\n",
    "            tokens = batch['ids'].to(device)\n",
    "            labels = batch['labels'].to(device)\n",
    "            predictions = model(tokens)\n",
    "            loss = criterion(predictions, labels)\n",
    "            accuracy = get_accuracy(predictions, labels)\n",
    "            epoch_loss += loss.item()\n",
    "            epoch_accuracy += accuracy.item()\n",
    "\n",
    "    return epoch_loss / len(dataloader), epoch_accuracy / len(dataloader)"
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    "def get_accuracy(predictions, labels):\n",
    "    batch_size = predictions.shape[0]\n",
    "    predicted_classes = predictions.argmax(1, keepdim=True)\n",
    "    correct_predictions = predicted_classes.eq(labels.view_as(predicted_classes)).sum()\n",
    "    accuracy = correct_predictions.float() / batch_size\n",
    "    return accuracy"
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    "n_epochs = 10\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "\n",
    "    train_loss, train_acc = train(train_dataloader, model, criterion, optimizer, device)\n",
    "    valid_loss, valid_acc = evaluate(valid_dataloader, model, criterion, device)\n",
    "\n",
    "    print(f'epoch: {epoch+1}')\n",
    "    print(f'train_loss: {train_loss:.3f}, train_acc: {train_acc:.3f}')\n",
    "    print(f'valid_loss: {valid_loss:.3f}, valid_acc: {valid_acc:.3f}')"
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