bart_large_mnli_Finetuned.ipynb

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        "<a href=\"https://colab.research.google.com/github/SahilCarterr/FinetuningLLM/blob/main/bart_large_mnli_Finetuned.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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      "source": [
        "#### 1. Data Preprocessing"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "V8Q9827qOFSL"
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      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q-3gyjiwO8w3"
      },
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      "source": [
        "df = pd.read_csv('/content/data_cleaned2jul_witutL.csv')"
      ]
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            "text/plain": [
              "                                            question                     topic\n",
              "0  once your ex was your lover will you give them...  Dating and Relationships\n",
              "1                                                any                    Random\n",
              "2               what type of guys do u hate on hunch  Dating and Relationships\n",
              "3                     karnapishachini ko kya chahiye             Movies and TV\n",
              "4                      feeling hai woexpress nhi kar  Dating and Relationships"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
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        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfbxGZg5i50_"
      },
      "source": [
        "##### 1.1 Mapping topics to numbers"
      ]
    },
    {
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        },
        "id": "13SbVYbOQEZn",
        "outputId": "f225755f-f3d2-46de-9678-8c39ff430c7d"
      },
      "outputs": [
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          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "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)"
      ]
    },
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              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7d0dfcd2-d499-433d-85ef-e9a1276fee4e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7d0dfcd2-d499-433d-85ef-e9a1276fee4e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7d0dfcd2-d499-433d-85ef-e9a1276fee4e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                            question  topic\n",
              "0  once your ex was your lover will you give them...      0\n",
              "1                                                any      1\n",
              "2               what type of guys do u hate on hunch      0\n",
              "3                     karnapishachini ko kya chahiye      2\n",
              "4                      feeling hai woexpress nhi kar      0"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7i52i88ei51D"
      },
      "source": [
        "##### 1.2 Splitting the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pRiK7zdOPCPK",
        "outputId": "735007e9-f6f6-4719-cf6e-ca48f570bd88"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data has been split and saved to train.csv and test.csv.\n"
          ]
        }
      ],
      "source": [
        "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": "kkKpoCVJPcnF",
        "outputId": "afb2e413-0152-4965-a709-67d515d6817f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m10.3 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[31m15.4 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[31m15.6 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[31m9.1 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[31m23.9 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[31m18.6 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": "JCXXq2NFi51G"
      },
      "source": [
        "##### 1.3 Converting to Pytorch dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266,
          "referenced_widgets": [
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        },
        "id": "gMY2cQFsPWWN",
        "outputId": "669324d6-8c3a-4882-c3ce-ddfaf284f0cc"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c7f92b2ab3b74aafa2f196234bb90a2a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e242d34cc58446609cf9416ec48be5cd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "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",
              "})"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": "LQ6LX45Ai51I"
      },
      "source": [
        "#### 2. Loading Model and Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393,
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        },
        "id": "ezyAvpVQPbDh",
        "outputId": "1aa965b3-1289-4a67-a838-3a432ad10e42"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c79174e112b1455484bc8018853aa0a9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "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"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "53eb659cde94462881d699099f756651",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "480d2e2a6bc243a58671198b021e1ff5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "a3eeeda624bf43ccb2f9cab3dd9c973b",
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            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a9a6490e63404d208b9662511d7e7c1b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a9f4695fe34246308bd6f9f12a519103",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1260 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1f86c633004c4f40899787a3fccacc61",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/315 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "checkpoint = \"facebook/bart-large-mnli\"\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": "markdown",
      "metadata": {
        "id": "Xg5wT4vHi51K"
      },
      "source": [
        "##### 2.1 Using Tokenized Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwg52RKKQw6g",
        "outputId": "00fd0af7-7e3c-4e17-e653-a1a6ae71dedc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['labels', 'input_ids', 'attention_mask']"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": "code",
      "execution_count": null,
      "metadata": {
        "id": "89S3f5OKRBFz"
      },
      "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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2OwWcHhRE0x",
        "outputId": "ba09a4ab-c3bb-4708-a892-6f0da3eaac46"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'labels': torch.Size([8]),\n",
              " 'input_ids': torch.Size([8, 16]),\n",
              " 'attention_mask': torch.Size([8, 16])}"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for batch in train_dataloader:\n",
        "    break\n",
        "{k: v.shape for k, v in batch.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5xM2EeEi51N"
      },
      "source": [
        "##### 2.2 Loading Model from Checkpoint"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135,
          "referenced_widgets": [
            "354e5901237f43e099c4a8c85aaa2051",
            "2ef542373a7c4d88aa0636a86db9ef1f",
            "96313df1d04542a3871979a2094790e8",
            "7f7a60202bc2472e912e61c21ab5f806",
            "5c9185e26e664af299e9d7c64d8e809c",
            "aec7f0153b0543688e8f7e70338cadc9",
            "c9559831945a4322bf075df9fd243f3e",
            "72a5305e45c744cba62e9bea01da32fd",
            "a3b3b5bfca094403b3dcf8203713a920",
            "19773e7ead954a88ace09f7ec352aa6e",
            "afd3d2cfaba347eca74fe4be5d90b2ae"
          ]
        },
        "id": "-Qg3EPbdRHOx",
        "outputId": "770bea85-4bd4-4880-fea3-346a56a1bb34"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "354e5901237f43e099c4a8c85aaa2051",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of BartForSequenceClassification were not initialized from the model checkpoint at facebook/bart-large-mnli and are newly initialized because the shapes did not match:\n",
            "- classification_head.out_proj.bias: found shape torch.Size([3]) in the checkpoint and torch.Size([24]) in the model instantiated\n",
            "- classification_head.out_proj.weight: found shape torch.Size([3, 1024]) in the checkpoint and torch.Size([24, 1024]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=24,ignore_mismatched_sizes=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fWd2V4Axi51P"
      },
      "source": [
        "##### 2.3 Checking Model output shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzwOlhOORKUj",
        "outputId": "dbcc96a1-b284-49b0-f5e6-44b09730cef1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor(3.0905, grad_fn=<NllLossBackward0>) torch.Size([8, 24])\n"
          ]
        }
      ],
      "source": [
        "outputs = model(**batch)\n",
        "print(outputs.loss, outputs.logits.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K671Y1MBi51Q"
      },
      "source": [
        "##### 2.2 Initializing Optimizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_o0VoIzRPvs",
        "outputId": "c358c086-9a2e-40ea-f61e-6b333d0e6d1a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:588: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import AdamW\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHy0dZUai51S"
      },
      "source": [
        "##### 2.3 Getting training steps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3FzGxdsRaL1",
        "outputId": "b0128440-8400-4cb2-a9b5-021cde28d924"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "474\n"
          ]
        }
      ],
      "source": [
        "from transformers import get_scheduler\n",
        "\n",
        "num_epochs = 3\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps,\n",
        ")\n",
        "print(num_training_steps)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UM-lQWF7i51T"
      },
      "source": [
        "#### 3. Training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rx140H6JRcmA",
        "outputId": "b4585a50-2754-4545-cf2a-a5ee36d18486"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69,
          "referenced_widgets": [
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            "121d640744fc4425b2bdbf90d0ebc538",
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            "9f5d445e12a14edcad5879f9c490ab76",
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            "9fc8b1c5fa314b53b2243054753bc243",
            "0d71bdf11b614e27acd25d005fae427b"
          ]
        },
        "id": "z6ACIG1uRfA_",
        "outputId": "d0cd06f2-997f-4bd3-e469-0fb49b292723"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e5c5c94332ad464fbded08defc8572f8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/474 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    for batch in train_dataloader:\n",
        "        batch = {k: v.to(device) for k, v in batch.items()}\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlT10YO_S5Lt",
        "outputId": "5e6abb13-7cdd-482c-b833-e599a632452e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━\u001b[0m \u001b[32m81.9/84.1 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install evaluate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rY41oc8Li51X"
      },
      "source": [
        "#### 4. Evaluate the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLB_5kOJRqZo",
        "outputId": "a6601a79-0ff6-496e-8f82-0a950bf71e84"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.6730\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "model.eval()\n",
        "all_predictions = []\n",
        "all_labels = []\n",
        "\n",
        "for batch in eval_dataloader:\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**batch)\n",
        "\n",
        "    logits = outputs.logits\n",
        "    predictions = torch.argmax(logits, dim=-1)\n",
        "\n",
        "    # Append predictions and labels\n",
        "    all_predictions.extend(predictions.cpu().numpy())\n",
        "    all_labels.extend(batch['labels'].cpu().numpy())\n",
        "\n",
        "# Convert lists to tensors\n",
        "all_predictions = torch.tensor(all_predictions)\n",
        "all_labels = torch.tensor(all_labels)\n",
        "\n",
        "# Compute accuracy\n",
        "accuracy = (all_predictions == all_labels).float().mean().item()\n",
        "\n",
        "print(f'Accuracy: {accuracy:.4f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yj27ywueTYby",
        "outputId": "e6a3058a-6bba-4828-ee4d-005c8858b983"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.6730\n",
            "Precision: 0.6650\n",
            "Recall: 0.6730\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import precision_score, recall_score\n",
        "\n",
        "# Compute accuracy\n",
        "accuracy = (all_predictions == all_labels).float().mean().item()\n",
        "\n",
        "# Compute precision and recall\n",
        "precision = precision_score(all_labels, all_predictions, average='weighted')\n",
        "recall = recall_score(all_labels, all_predictions, average='weighted')\n",
        "\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "print(f'Precision: {precision:.4f}')\n",
        "print(f'Recall: {recall:.4f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YdRZEnvimjyy"
      },
      "outputs": [],
      "source": []
    }
  ],
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    "accelerator": "GPU",
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