distill_bert_nueral_network.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SahilCarterr/FinetuningLLM/blob/main/distill_bert_nueral_network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyJugZbngTEv"
      },
      "source": [
        "#### 1. Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbC3zl3b7OlS",
        "outputId": "9197ac7b-dc6c-4b28-c147-6e2e58e0b846"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mapping of unique strings to numbers: {'Dating and Relationships': 0, 'Random': 1, 'Movies and TV': 2, 'Music': 3, 'Technology and Internet': 4, 'Philosophy and Religion': 5, 'technology and Internet': 6, 'Law and Order': 7, 'Health and Wellness': 8, 'Food and Drinks': 9, 'Fashion and Makeup': 10, 'health and Wellness': 11, 'Gaming': 12, 'Sports': 13, 'fashion and Makeup': 14, 'sports': 15, 'Anime': 16, 'anime': 17, 'Finance': 18, 'Travel and Geography': 19, 'Pets and Animals': 20, 'gaming': 21, 'random': 22, 'music': 23}\n",
            "Data has been split and saved to train.csv and test.csv.\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/data_cleaned2jul_witutL.csv')\n",
        "unique_topics = df['topic'].unique()\n",
        "topic_mapping = {topic: i for i, topic in enumerate(unique_topics)}\n",
        "\n",
        "# Apply the mapping to the 'topic' column\n",
        "df['topic'] = df['topic'].map(topic_mapping)\n",
        "\n",
        "# Display the updated DataFrame and the mapping of unique strings to numbers\n",
        "print(\"Mapping of unique strings to numbers:\", topic_mapping)\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n",
        "\n",
        "# Save the train and test datasets to new CSV files\n",
        "train_data.to_csv('train.csv', index=False)\n",
        "test_data.to_csv('test.csv', index=False)\n",
        "\n",
        "print(\"Data has been split and saved to train.csv and test.csv.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLorMDtO7dem",
        "outputId": "0ec1c113-2e3a-4b02-b97a-e3d5dd561353"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip -q install pyarrow datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1IrCWAhHgTEx"
      },
      "source": [
        "##### 1.1 Converting to Pytorch Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255,
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            "208a740101c640a28428e94aaf5df895"
          ]
        },
        "id": "lObR_XSZ7eJT",
        "outputId": "56eb87db-f13c-4f65-e9b9-941802f50f8f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "163a89a28c4a412fb59a30c6ae33d03b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5cf60c0738064d728d756c75413fbe58"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['question', 'topic'],\n",
              "        num_rows: 1260\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['question', 'topic'],\n",
              "        num_rows: 315\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "data_files = {\"train\": \"/content/train.csv\", \"test\": \"/content/test.csv\"}\n",
        "dataset = load_dataset(\"csv\", data_files=data_files)\n",
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWqbuWxtgTEy"
      },
      "source": [
        "##### 1.2 Making a tokenized Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "OytqzQZg_gIx",
        "outputId": "5b09da94-3c9a-4a08-e683-48efbe44f27a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f3d005a89104ba5af145f07f7d8a29d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/465 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "081a3a53476b490fb1ed13b68e932cff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80f8e45d2a404ad5ae145257f2d02a6b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c7091eae0a314c30aa3af4133b868b2d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1260 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "67ba26fc9dfd4b73b0edb16e204503d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/315 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24e70daf79ea40d8a91f90718d88f43e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "checkpoint = \"distilbert-base-cased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    # Ensure inputs are strings, handle potential None or other non-string types\n",
        "    questions = [str(q) if q is not None else \"\" for q in examples['question']]\n",
        "    return tokenizer(questions,truncation=True)\n",
        "\n",
        "tokenized_datasets = dataset.map(tokenize_function, batched=True)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OmYk5VTJ_gGK",
        "outputId": "5967f0ee-70a8-4b25-830a-95489b914824"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['labels', 'input_ids', 'attention_mask']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "tokenized_datasets = tokenized_datasets.remove_columns([\"question\"])\n",
        "tokenized_datasets = tokenized_datasets.rename_column(\"topic\", \"labels\")\n",
        "tokenized_datasets.set_format(\"torch\")\n",
        "tokenized_datasets[\"train\"].column_names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGtm1IfJgTEz"
      },
      "source": [
        "##### 1.3 Converting Data to batches"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JQ_rE32D_gDs"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(\n",
        "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
        ")\n",
        "eval_dataloader = DataLoader(\n",
        "    tokenized_datasets[\"test\"], batch_size=8, collate_fn=data_collator\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bs0uPBCSgTEz"
      },
      "source": [
        "#### 2. Creating the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qU8m3Co58Fm3"
      },
      "outputs": [],
      "source": [
        "# Importing the libraries needed\n",
        "import pandas as pd\n",
        "import torch\n",
        "import transformers\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import DistilBertModel, DistilBertTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_V2IZdlJ8h7y"
      },
      "outputs": [],
      "source": [
        "\n",
        "from torch import cuda\n",
        "device = 'cuda' if cuda.is_available() else 'cpu'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cPUg2WIa8k8P"
      },
      "outputs": [],
      "source": [
        "# Defining some key variables that will be used later on in the training\n",
        "MAX_LEN = 512\n",
        "TRAIN_BATCH_SIZE = 4\n",
        "VALID_BATCH_SIZE = 2\n",
        "EPOCHS = 1\n",
        "LEARNING_RATE = 1e-05\n",
        "#tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_PbS5Wyc8_i6"
      },
      "outputs": [],
      "source": [
        "# Creating the customized model, by adding a drop out and a dense layer on top of distil bert to get the final output for the model.\n",
        "\n",
        "class DistillBERTClass(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(DistillBERTClass, self).__init__()\n",
        "        self.l1 = DistilBertModel.from_pretrained(\"distilbert-base-uncased\")\n",
        "        self.pre_classifier = torch.nn.Linear(768, 768)\n",
        "        self.dropout = torch.nn.Dropout(0.3)\n",
        "        self.classifier = torch.nn.Linear(768, 24)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask):\n",
        "        output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        hidden_state = output_1[0]\n",
        "        pooler = hidden_state[:, 0]\n",
        "        pooler = self.pre_classifier(pooler)\n",
        "        pooler = torch.nn.ReLU()(pooler)\n",
        "        pooler = self.dropout(pooler)\n",
        "        output = self.classifier(pooler)\n",
        "        return output"
      ]
    },
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            "text/plain": [
              "DistillBERTClass(\n",
              "  (l1): DistilBertModel(\n",
              "    (embeddings): Embeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer): Transformer(\n",
              "      (layer): ModuleList(\n",
              "        (0-5): 6 x TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
              "  (dropout): Dropout(p=0.3, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=24, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "model = DistillBERTClass()\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGXp_FnMgTE0"
      },
      "source": [
        "##### 2.1 Initializing the Loss and optimizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KQ-MpFZ89PHC"
      },
      "outputs": [],
      "source": [
        "# Creating the loss function and optimizer\n",
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(params =  model.parameters(), lr=LEARNING_RATE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5sD5aM0v98Ph"
      },
      "outputs": [],
      "source": [
        "# Function to calcuate the accuracy of the model\n",
        "\n",
        "def calcuate_accu(big_idx, targets):\n",
        "    n_correct = (big_idx==targets).sum().item()\n",
        "    return n_correct"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69iqijq9gTE0"
      },
      "source": [
        "#### 3 Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NR1T3Cie9-lF"
      },
      "outputs": [],
      "source": [
        "# Defining the training function on the 80% of the dataset for tuning the distilbert model\n",
        "\n",
        "def train(epoch):\n",
        "    tr_loss = 0\n",
        "    n_correct = 0\n",
        "    nb_tr_steps = 0\n",
        "    nb_tr_examples = 0\n",
        "    model.train()\n",
        "    for _,data in enumerate(train_dataloader, 0):\n",
        "        ids = data['input_ids'].to(device, dtype = torch.long)\n",
        "        mask = data['attention_mask'].to(device, dtype = torch.long)\n",
        "        targets = data['labels'].to(device, dtype = torch.long)\n",
        "\n",
        "        outputs = model(ids, mask)\n",
        "        loss = loss_function(outputs, targets)\n",
        "        tr_loss += loss.item()\n",
        "        big_val, big_idx = torch.max(outputs.data, dim=1)\n",
        "        n_correct += calcuate_accu(big_idx, targets)\n",
        "\n",
        "        nb_tr_steps += 1\n",
        "        nb_tr_examples+=targets.size(0)\n",
        "\n",
        "        if _%10==0:\n",
        "            loss_step = tr_loss/nb_tr_steps\n",
        "            accu_step = (n_correct*100)/nb_tr_examples\n",
        "            print(f\"Training Loss per 5000 steps: {loss_step}\")\n",
        "            print(f\"Training Accuracy per 5000 steps: {accu_step}\")\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        # # When using GPU\n",
        "        optimizer.step()\n",
        "\n",
        "    print(f'The Total Accuracy for Epoch {epoch}: {(n_correct*100)/nb_tr_examples}')\n",
        "    epoch_loss = tr_loss/nb_tr_steps\n",
        "    epoch_accu = (n_correct*100)/nb_tr_examples\n",
        "    print(f\"Training Loss Epoch: {epoch_loss}\")\n",
        "    print(f\"Training Accuracy Epoch: {epoch_accu}\")\n",
        "\n",
        "    return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9xBU1sb-CIu",
        "outputId": "199cbf28-6cd4-4623-b542-82d99898ccd1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 3.271740436553955\n",
            "Training Accuracy per 5000 steps: 0.0\n",
            "Training Loss per 5000 steps: 3.2032208876176314\n",
            "Training Accuracy per 5000 steps: 1.1363636363636365\n",
            "Training Loss per 5000 steps: 3.1238386631011963\n",
            "Training Accuracy per 5000 steps: 13.69047619047619\n",
            "Training Loss per 5000 steps: 3.0610107529547905\n",
            "Training Accuracy per 5000 steps: 18.951612903225808\n",
            "Training Loss per 5000 steps: 3.0148479822205334\n",
            "Training Accuracy per 5000 steps: 21.646341463414632\n",
            "Training Loss per 5000 steps: 2.935853457918354\n",
            "Training Accuracy per 5000 steps: 25.245098039215687\n",
            "Training Loss per 5000 steps: 2.8783816118709376\n",
            "Training Accuracy per 5000 steps: 27.25409836065574\n",
            "Training Loss per 5000 steps: 2.8408978337972934\n",
            "Training Accuracy per 5000 steps: 28.169014084507044\n",
            "Training Loss per 5000 steps: 2.801869833910907\n",
            "Training Accuracy per 5000 steps: 29.166666666666668\n",
            "Training Loss per 5000 steps: 2.7858931179885027\n",
            "Training Accuracy per 5000 steps: 29.395604395604394\n",
            "Training Loss per 5000 steps: 2.7394084033399526\n",
            "Training Accuracy per 5000 steps: 31.064356435643564\n",
            "Training Loss per 5000 steps: 2.7230803332887255\n",
            "Training Accuracy per 5000 steps: 31.644144144144143\n",
            "Training Loss per 5000 steps: 2.718118170076165\n",
            "Training Accuracy per 5000 steps: 31.611570247933884\n",
            "Training Loss per 5000 steps: 2.710607838994674\n",
            "Training Accuracy per 5000 steps: 31.48854961832061\n",
            "Training Loss per 5000 steps: 2.6944817668157266\n",
            "Training Accuracy per 5000 steps: 32.09219858156028\n",
            "Training Loss per 5000 steps: 2.6856716531791434\n",
            "Training Accuracy per 5000 steps: 32.11920529801324\n",
            "The Total Accuracy for Epoch 0: 32.06349206349206\n",
            "Training Loss Epoch: 2.684575003159197\n",
            "Training Accuracy Epoch: 32.06349206349206\n",
            "Training Loss per 5000 steps: 2.3400814533233643\n",
            "Training Accuracy per 5000 steps: 37.5\n",
            "Training Loss per 5000 steps: 2.4980995655059814\n",
            "Training Accuracy per 5000 steps: 34.09090909090909\n",
            "Training Loss per 5000 steps: 2.452269815263294\n",
            "Training Accuracy per 5000 steps: 35.714285714285715\n",
            "Training Loss per 5000 steps: 2.409626591590143\n",
            "Training Accuracy per 5000 steps: 38.306451612903224\n",
            "Training Loss per 5000 steps: 2.4132380747213595\n",
            "Training Accuracy per 5000 steps: 38.109756097560975\n",
            "Training Loss per 5000 steps: 2.432497821602167\n",
            "Training Accuracy per 5000 steps: 37.254901960784316\n",
            "Training Loss per 5000 steps: 2.4406363983623316\n",
            "Training Accuracy per 5000 steps: 37.09016393442623\n",
            "Training Loss per 5000 steps: 2.4891372751182232\n",
            "Training Accuracy per 5000 steps: 35.03521126760563\n",
            "Training Loss per 5000 steps: 2.482903907328476\n",
            "Training Accuracy per 5000 steps: 35.648148148148145\n",
            "Training Loss per 5000 steps: 2.4898336221883586\n",
            "Training Accuracy per 5000 steps: 35.714285714285715\n",
            "Training Loss per 5000 steps: 2.4885112342267934\n",
            "Training Accuracy per 5000 steps: 35.51980198019802\n",
            "Training Loss per 5000 steps: 2.4945576169469335\n",
            "Training Accuracy per 5000 steps: 35.36036036036036\n",
            "Training Loss per 5000 steps: 2.490611253691114\n",
            "Training Accuracy per 5000 steps: 35.43388429752066\n",
            "Training Loss per 5000 steps: 2.49423626270003\n",
            "Training Accuracy per 5000 steps: 35.20992366412214\n",
            "Training Loss per 5000 steps: 2.492117752420141\n",
            "Training Accuracy per 5000 steps: 35.283687943262414\n",
            "Training Loss per 5000 steps: 2.492474046763995\n",
            "Training Accuracy per 5000 steps: 35.182119205298015\n",
            "The Total Accuracy for Epoch 1: 35.0\n",
            "Training Loss Epoch: 2.496506777745259\n",
            "Training Accuracy Epoch: 35.0\n"
          ]
        }
      ],
      "source": [
        "EPOCHS=2\n",
        "for epoch in range(EPOCHS):\n",
        "    train(epoch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2EL1pusagTE1"
      },
      "source": [
        "#### 4. Evaluating Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4cyoLbgk-Fya"
      },
      "outputs": [],
      "source": [
        "def valid(model, testing_loader):\n",
        "    model.eval()\n",
        "    n_correct = 0; n_wrong = 0; total = 0\n",
        "    tr_loss = 0  # Initialize tr_loss here\n",
        "    nb_tr_steps = 0\n",
        "    nb_tr_examples = 0 # Initialize these variables as well\n",
        "    with torch.no_grad():\n",
        "        for _, data in enumerate(eval_dataloader, 0):\n",
        "            ids = data['input_ids'].to(device, dtype = torch.long)\n",
        "            mask = data['attention_mask'].to(device, dtype = torch.long)\n",
        "            targets = data['labels'].to(device, dtype = torch.long)\n",
        "            outputs = model(ids, mask).squeeze()\n",
        "            loss = loss_function(outputs, targets)\n",
        "            tr_loss += loss.item()\n",
        "            big_val, big_idx = torch.max(outputs.data, dim=1)\n",
        "            n_correct += calcuate_accu(big_idx, targets)\n",
        "\n",
        "            nb_tr_steps += 1\n",
        "            nb_tr_examples+=targets.size(0)\n",
        "\n",
        "            if _%10==0:\n",
        "                loss_step = tr_loss/nb_tr_steps\n",
        "                accu_step = (n_correct*100)/nb_tr_examples\n",
        "                print(f\"Validation Loss per 10 steps: {loss_step}\")\n",
        "                print(f\"Validation Accuracy per 10 steps: {accu_step}\")\n",
        "    epoch_loss = tr_loss/nb_tr_steps\n",
        "    epoch_accu = (n_correct*100)/nb_tr_examples\n",
        "    print(f\"Validation Loss Epoch: {epoch_loss}\")\n",
        "    print(f\"Validation Accuracy Epoch: {epoch_accu}\")\n",
        "\n",
        "    return epoch_accu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P1yeQfTGBM4D",
        "outputId": "7f542584-ea71-48e2-d838-b97ac61d5f35"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Loss per 10 steps: 2.044330596923828\n",
            "Validation Accuracy per 10 steps: 50.0\n",
            "Validation Loss per 10 steps: 2.3705007162961094\n",
            "Validation Accuracy per 10 steps: 40.90909090909091\n",
            "Validation Loss per 10 steps: 2.3594733760470437\n",
            "Validation Accuracy per 10 steps: 41.666666666666664\n",
            "Validation Loss per 10 steps: 2.355185789446677\n",
            "Validation Accuracy per 10 steps: 41.935483870967744\n",
            "Validation Loss Epoch: 2.3509431004524233\n",
            "Validation Accuracy Epoch: 40.317460317460316\n",
            "Accuracy on test data = 40.32%\n"
          ]
        }
      ],
      "source": [
        "acc = valid(model, eval_dataloader)\n",
        "print(\"Accuracy on test data = %0.2f%%\" % acc)"
      ]
    }
  ],
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