Sentiment__analysis_usingNBow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Nz5FpcE6K66",
        "outputId": "ca8a9f8e-1246-4e54-bf3f-d83d31495cd8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.18.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.13.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n",
            "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (14.0.2)\n",
            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n",
            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.2)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
            "Requirement already satisfied: fsspec[http]<=2024.2.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.3)\n",
            "Requirement already satisfied: huggingface-hub>=0.19.4 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.20.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.4->datasets) (4.10.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2024.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "OBwq4WcT3yJb"
      },
      "outputs": [],
      "source": [
        "import collections\n",
        "\n",
        "import datasets\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchtext\n",
        "import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seed = 1234\n",
        "\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "torch.cuda.manual_seed(seed)\n",
        "torch.backends.cudnn.deterministic = True"
      ],
      "metadata": {
        "id": "driExK4o6Q6_"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, test_data = datasets.load_dataset(\"imdb\", split=[\"train\", \"test\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vv2a90Cd7ho1",
        "outputId": "5372c03a-4966-48b0-a680-2c474b8dabf3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data , test_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmoiqGGO7rws",
        "outputId": "41160635-203d-4476-c818-79fcf28773c1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset({\n",
              "     features: ['text', 'label'],\n",
              "     num_rows: 25000\n",
              " }),\n",
              " Dataset({\n",
              "     features: ['text', 'label'],\n",
              "     num_rows: 25000\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BagHIi47rsv",
        "outputId": "8e107940-70b0-4d07-a678-a0fc80541381"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['neg', 'pos'], id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###TOKENIZATION\n"
      ],
      "metadata": {
        "id": "h4YVN6fW75jh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = torchtext.data.utils.get_tokenizer(\"basic_english\")"
      ],
      "metadata": {
        "id": "bIW-DtL77rqZ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_example(example , tokenizer , max_length):\n",
        "  tokens = tokenizer(example['text'])[:max_length]\n",
        "  return {\"tokens\": tokens}"
      ],
      "metadata": {
        "id": "EJU7frrk7rnw"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 256\n",
        "train_data = train_data.map(\n",
        "    tokenize_example , fn_kwargs= {\"tokenizer\" : tokenizer , \"max_length\" : max_length}\n",
        ")\n",
        "\n",
        "test_data = test_data.map(\n",
        "    tokenize_example , fn_kwargs = {\"tokenizer\": tokenizer ,\"max_length\": max_length}\n",
        ")"
      ],
      "metadata": {
        "id": "3CNvP5Il7rlO"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBxzyABK7rii",
        "outputId": "6f5f954a-72af-40c5-e9b6-8a52bdf1a1c8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label', 'tokens'],\n",
              "    num_rows: 25000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Creating Validation Data\n"
      ],
      "metadata": {
        "id": "jtNpD39HB319"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_size = 0.20\n",
        "train_valid_data = train_data.train_test_split(test_size  = test_size)\n",
        "train_data = train_valid_data[\"train\"]\n",
        "valid_data = train_valid_data[\"test\"]\n"
      ],
      "metadata": {
        "id": "3PqAKyNZ7rgZ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Creating Vocabulary\n"
      ],
      "metadata": {
        "id": "NJtDly5Xni75"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "min_freq = 4\n",
        "special_tokens = [\"<unk>\" , \"<pad>\" , \"<cls>\" , \"<sep>\"]\n",
        "\n",
        "vocab = torchtext.vocab.build_vocab_from_iterator(\n",
        "    train_data[\"tokens\"],\n",
        "    min_freq = min_freq,\n",
        "    specials = special_tokens\n",
        ")"
      ],
      "metadata": {
        "id": "grugmv5ZniaA"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab.get_itos()[:15]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4krzCwk7reY",
        "outputId": "650b281e-57d5-49b8-cec6-fa0819d59629"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<unk>',\n",
              " '<pad>',\n",
              " '<cls>',\n",
              " '<sep>',\n",
              " 'the',\n",
              " '.',\n",
              " ',',\n",
              " 'a',\n",
              " 'and',\n",
              " 'of',\n",
              " 'to',\n",
              " \"'\",\n",
              " 'is',\n",
              " 'it',\n",
              " 'i']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unk_index = vocab[\"<unk>\"]\n",
        "pad_index = vocab[\"<pad>\"]\n",
        "cls_index = vocab[\"<cls>\"]\n",
        "sep_index = vocab[\"<sep>\"]\n"
      ],
      "metadata": {
        "id": "x_LhxkdPo3MC"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#if some tokens are not present in vocab it will give index\n",
        "\n",
        "vocab.set_default_index(cls_index)\n"
      ],
      "metadata": {
        "id": "nsmopQw3qLPe"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab[\"somw\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWmTBdDuqdX5",
        "outputId": "67186e34-c4df-4318-d51a-5ecf583a93fb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocab.lookup_indices([\"hello\", \"world\", \"some_token\", \"<pad>\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cwXbRcvqtzs",
        "outputId": "748c2846-7ee7-4c28-a19c-a4b5035d13a4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5459, 187, 2, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Numericalizing Data"
      ],
      "metadata": {
        "id": "Ae_Y2jVWswIb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def numericalize_data(example , vocab):\n",
        "  ids = vocab.lookup_indices(example[\"tokens\"])\n",
        "  return {\"ids\" : ids}"
      ],
      "metadata": {
        "id": "Y6DenCe2suwd"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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})"
      ],
      "metadata": {
        "id": "sR4UTx_UtV9F"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# transforming the ids and label from integers into PyTorch tensors, which we do using the with_format method\n",
        "\n",
        "train_data = train_data.with_format(type = \"torch\" , columns= [\"ids\" , \"label\"])\n",
        "valid_data = valid_data.with_format(type=\"torch\", columns=[\"ids\", \"label\"])\n",
        "test_data = test_data.with_format(type=\"torch\", columns=[\"ids\", \"label\"])"
      ],
      "metadata": {
        "id": "9Q3TdyNouKHi"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[0][\"ids\"][:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_0jXsV7HurZd",
        "outputId": "fee116f5-9246-4152-f8ba-a90384e3e540"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 118,  718,   14,   11,  141,  302, 2863,   17,   96,   84])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[0].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkdoO19mu10H",
        "outputId": "158e83b1-f4d5-4b97-a5ae-cac24bfb25a4"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['label', 'ids'])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_Collate_fn(pad_index):\n",
        "  def collate_fn(batch):\n",
        "    batch_ids = [i[\"ids\"] for i in batch]\n",
        "    batch_ids = nn.utils.rnn.pad_sequence(\n",
        "        batch_ids , padding_value = pad_index , batch_first= True\n",
        "    )\n",
        "\n",
        "    batch_label = [i[\"label\"] for i in batch]\n",
        "    batch_label = torch.stack(batch_label)\n",
        "    batch = {\"ids\": batch_ids , \"label\": batch_label}\n",
        "    return batch\n",
        "\n",
        "  return collate_fn"
      ],
      "metadata": {
        "id": "tcmWJzZqwPng"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_data_loader(dataset , batch_size , pad_index , shuffle = False):\n",
        "  collate_fn = get_Collate_fn(pad_index)\n",
        "  data_loader = torch.utils.data.DataLoader(\n",
        "      dataset = dataset,\n",
        "      batch_size = batch_size ,\n",
        "      collate_fn = collate_fn,\n",
        "      shuffle = shuffle\n",
        "  )\n",
        "  return data_loader"
      ],
      "metadata": {
        "id": "gEddDimgwPj1"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 512\n",
        "\n",
        "train_data_loader = get_data_loader(train_data , batch_size , pad_index , shuffle = True)\n",
        "valid_data_loader = get_data_loader(valid_data , batch_size , pad_index)\n",
        "test_data_loader = get_data_loader(test_data, batch_size , pad_index)"
      ],
      "metadata": {
        "id": "8Xu2qv-NwPgl"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Building Model\n"
      ],
      "metadata": {
        "id": "tlkcBD1PzgFX"
      }
    },
    {
      "cell_type": "code",
      "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 , ids):\n",
        "    embebbed = self.embedding(ids)\n",
        "    pooled = embebbed.mean(dim = 1)\n",
        "    predicted = self.fc(pooled)\n",
        "\n",
        "    return predicted"
      ],
      "metadata": {
        "id": "yX-0YlDDzfwA"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_size = len(vocab)\n",
        "embedding_dim  = 300\n",
        "\n",
        "output_dim = len(train_data.unique(\"label\"))\n",
        "\n",
        "model = NBoW(vocab_size , embedding_dim , output_dim , pad_index)"
      ],
      "metadata": {
        "id": "tN1hKC9IwPd6"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xUaZuif-wPbf",
        "outputId": "65e430f9-1238-4d3b-a060-6f03981817e1"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model has  7,651,202 trainable parameters\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vectors = torchtext.vocab.GloVe()"
      ],
      "metadata": {
        "id": "wN7NikyFwPZW"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pretrained_embedding = vectors.get_vecs_by_tokens(vocab.get_itos())"
      ],
      "metadata": {
        "id": "ssJBc91PwPXN"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pretrained_embedding.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1eLYFRyo_mqw",
        "outputId": "cb7baa37-07ff-4dba-c4e5-8f8078dcab8c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([25502, 300])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.embedding.weight"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ages9D_rwPUx",
        "outputId": "5966daba-5385-41ce-ac80-4a72f92469f8"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Parameter containing:\n",
              "tensor([[-0.1117, -0.4966,  0.1631,  ..., -0.5592, -0.4480, -0.6476],\n",
              "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
              "        [ 0.7882, -1.6625, -0.7064,  ..., -1.5841, -0.3711, -1.2338],\n",
              "        ...,\n",
              "        [ 0.5408, -2.4592,  1.2055,  ...,  1.5511,  0.3961,  1.3328],\n",
              "        [-0.3017,  0.9250,  1.2910,  ...,  1.3969,  0.9476,  0.5435],\n",
              "        [ 0.5138, -0.4632, -0.2360,  ...,  1.1965,  0.6821, -0.1939]],\n",
              "       requires_grad=True)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretrained_embedding"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9iHjBVhh_111",
        "outputId": "2670ec5f-4d97-4a50-9243-c16e807f20e3"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
              "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
              "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
              "        ...,\n",
              "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
              "        [-0.1018,  0.4327,  0.2571,  ...,  0.3643,  0.3099,  0.1195],\n",
              "        [ 1.1741, -0.4386,  0.3310,  ...,  0.3193, -0.2292, -0.0887]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.embedding.weight.data = pretrained_embedding"
      ],
      "metadata": {
        "id": "cYTjfn5t_1xH"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.embedding.weight"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwnzKwKt_1uk",
        "outputId": "b1f679f9-242a-4613-a5c7-b8ff409ceabf"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Parameter containing:\n",
              "tensor([[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
              "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
              "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
              "        ...,\n",
              "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
              "        [-0.1018,  0.4327,  0.2571,  ...,  0.3643,  0.3099,  0.1195],\n",
              "        [ 1.1741, -0.4386,  0.3310,  ...,  0.3193, -0.2292, -0.0887]],\n",
              "       requires_grad=True)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = optim.Adam(model.parameters())"
      ],
      "metadata": {
        "id": "3uL8OZya_1sJ"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "iEsZUbEf_1pj"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6_LprJ8_1nG",
        "outputId": "1ca040ce-0258-43e2-8472-8de05d4bc543"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = model.to(device)\n",
        "criterion = criterion.to(device)"
      ],
      "metadata": {
        "id": "99ZVJo7R_1kS"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_accuracy(prediction , label):\n",
        "  batch_size , _ = prediction.shape\n",
        "  predicted_classes = prediction.argmax(dim = -1)\n",
        "  correct_predictions = predicted_classes.eq(label).sum()\n",
        "  accuracy = correct_predictions/ batch_size\n",
        "  return accuracy"
      ],
      "metadata": {
        "id": "QOLOcUHsFGdz"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(data_loader , model , criterion , optimizer , device):\n",
        "  model.train()\n",
        "  epoch_losses = []\n",
        "  epoch_accs = []\n",
        "  for batch in tqdm.tqdm(data_loader , desc = \"trainning..\"):\n",
        "    ids = batch[\"ids\"].to(device)\n",
        "    label = batch[\"label\"].to(device)\n",
        "    prediction = model(ids)\n",
        "    loss = criterion(prediction , label)\n",
        "    accuracy = get_accuracy(prediction , label)\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    epoch_losses.append(loss.item())\n",
        "    epoch_accs.append(accuracy.item())\n",
        "\n",
        "  return np.mean(epoch_losses) , np.mean(epoch_accs)\n"
      ],
      "metadata": {
        "id": "urat_Gki_1hh"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate(data_loader, model , criterion,device):\n",
        "  model.eval()\n",
        "  epoch_losses = []\n",
        "  epoch_accs= []\n",
        "  with torch.no_grad():\n",
        "    for batch in tqdm.tqdm(data_loader , desc = \"evaluating..\"):\n",
        "      ids = batch[\"ids\"].to(device)\n",
        "      label = batch[\"label\"].to(device)\n",
        "      prediction = model(ids)\n",
        "      loss = criterion(prediction , label)\n",
        "      accuracy = get_accuracy(prediction , label)\n",
        "      epoch_losses.append(loss.item())\n",
        "      epoch_accs.append(accuracy.item())\n",
        "\n",
        "  return np.mean(epoch_losses), np.mean(epoch_accs)"
      ],
      "metadata": {
        "id": "Z577Vto6_1e9"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_epochs = 15\n",
        "best_valid_loss = float(\"inf\")\n",
        "\n",
        "metrics = collections.defaultdict(list)\n",
        "\n",
        "for epoch in range(n_epochs):\n",
        "  train_loss, train_acc = train(\n",
        "      train_data_loader , model, criterion , optimizer , device\n",
        "  )\n",
        "  valid_loss , valid_acc = evaluate(valid_data_loader , model , criterion , device)\n",
        "  metrics[\"train_losses\"].append(train_loss)\n",
        "  metrics[\"train_accuracy\"].append(train_acc)\n",
        "  metrics[\"valid_losses\"].append(valid_loss)\n",
        "  metrics[\"valid_accuracy\"].append(valid_acc)\n",
        "\n",
        "  if valid_loss < best_valid_loss:\n",
        "    best_valid_loss = valid_loss\n",
        "    torch.save(model.state_dict() , \"nbow.pt\")\n",
        "\n",
        "  print(f\"epoch: {epoch}\")\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}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0r9jPH50_1ca",
        "outputId": "0b992a2c-0b78-4250-c413-20419fb2f80f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.57it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:02<00:00,  4.28it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 0\n",
            "train_loss: 0.630, train_acc: 0.737\n",
            "valid_loss: 0.598, valid_acc: 0.764\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.53it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.25it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 1\n",
            "train_loss: 0.559, train_acc: 0.792\n",
            "valid_loss: 0.527, valid_acc: 0.800\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.60it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 2\n",
            "train_loss: 0.485, train_acc: 0.830\n",
            "valid_loss: 0.464, valid_acc: 0.827\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:16<00:00,  2.49it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.14it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 3\n",
            "train_loss: 0.417, train_acc: 0.858\n",
            "valid_loss: 0.413, valid_acc: 0.851\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.57it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.20it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 4\n",
            "train_loss: 0.367, train_acc: 0.875\n",
            "valid_loss: 0.380, valid_acc: 0.859\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:16<00:00,  2.40it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.16it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 5\n",
            "train_loss: 0.326, train_acc: 0.890\n",
            "valid_loss: 0.353, valid_acc: 0.868\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.61it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.34it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 6\n",
            "train_loss: 0.295, train_acc: 0.900\n",
            "valid_loss: 0.334, valid_acc: 0.875\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.50it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  5.63it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 7\n",
            "train_loss: 0.270, train_acc: 0.910\n",
            "valid_loss: 0.320, valid_acc: 0.880\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.59it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 8\n",
            "train_loss: 0.252, train_acc: 0.917\n",
            "valid_loss: 0.310, valid_acc: 0.881\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.58it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.08it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 9\n",
            "train_loss: 0.230, train_acc: 0.925\n",
            "valid_loss: 0.302, valid_acc: 0.882\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:16<00:00,  2.49it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  5.53it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 10\n",
            "train_loss: 0.215, train_acc: 0.932\n",
            "valid_loss: 0.296, valid_acc: 0.885\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.57it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.10it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 11\n",
            "train_loss: 0.200, train_acc: 0.937\n",
            "valid_loss: 0.293, valid_acc: 0.884\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.55it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:02<00:00,  4.58it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 12\n",
            "train_loss: 0.185, train_acc: 0.944\n",
            "valid_loss: 0.286, valid_acc: 0.888\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.57it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.17it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 13\n",
            "train_loss: 0.172, train_acc: 0.949\n",
            "valid_loss: 0.286, valid_acc: 0.888\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "trainning..: 100%|██████████| 40/40 [00:15<00:00,  2.59it/s]\n",
            "evaluating..: 100%|██████████| 10/10 [00:01<00:00,  6.03it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 14\n",
            "train_loss: 0.164, train_acc: 0.952\n",
            "valid_loss: 0.283, valid_acc: 0.889\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10, 6))\n",
        "ax = fig.add_subplot(1, 1, 1)\n",
        "ax.plot(metrics[\"train_losses\"], label=\"train loss\")\n",
        "ax.plot(metrics[\"valid_losses\"], label=\"valid loss\")\n",
        "ax.set_xlabel(\"epoch\")\n",
        "ax.set_ylabel(\"loss\")\n",
        "ax.set_xticks(range(n_epochs))\n",
        "ax.legend()\n",
        "ax.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "pLEUEpFj_1Z7",
        "outputId": "35bc60da-f127-4c1b-829e-91de6aded8e8"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10, 6))\n",
        "ax = fig.add_subplot(1, 1, 1)\n",
        "ax.plot(metrics[\"train_accuracy\"], label=\"train accuracy\")\n",
        "ax.plot(metrics[\"valid_accuracy\"], label=\"valid accuracy\")\n",
        "ax.set_xlabel(\"epoch\")\n",
        "ax.set_ylabel(\"loss\")\n",
        "ax.set_xticks(range(n_epochs))\n",
        "ax.legend()\n",
        "ax.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "4fiDJNyi_1X5",
        "outputId": "00488e8e-9359-41cc-e10d-7d675f024d90"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}