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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lisabecker/nlp-fundamentals/blob/main/0404_finetuning_transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Module 4 Use Case - 👍 or 👎? Classifying Movie Reviews with Transformers\n",
        "\n",
        "In this exercise, we will finetune a transformer model `distilbert` on the [IMDB movie review dataset](https://huggingface.co/datasets/imdb) to classify if a movie review is either positive or negative. In the end, we shall try feeding the trained model with our own reviews about recent movies to check if it is able to classify them according to our general sentiment about the movie.\n",
        "\n",
        "What is `distilbert`? It's a \"distilled\" version of BERT. Its main benefits include:\n",
        "\n",
        "- **Reduced Size**: DistilBERT has 40% fewer parameters than BERT-base, making it much lighter and faster for training and inference.\n",
        "- **Preserved Performance**: Despite its reduced size, DistilBERT retains up to 97% of BERT's performance on various benchmark NLP tasks.\n",
        "- **Flexibility and Efficiency**: Its smaller size makes it more suitable for real-world applications, including on mobile devices or other environments with limited computational resources."
      ],
      "metadata": {
        "id": "8AyCo8sqewqG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##1. Set up the environment"
      ],
      "metadata": {
        "id": "NmB3KBRUGUrA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Installing all packages will take a few minutes\n",
        "!pip install --q transformers==4.36.2 datasets==2.16.1 torch==2.1.2\n",
        "print(\"\\nPackages installed.\")"
      ],
      "metadata": {
        "id": "ylp08rjzGToS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a49b25a4-4045-4118-a7df-c5f37c6f9032"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.8/209.8 MB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.5/20.5 MB\u001b[0m \u001b[31m81.0 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[31m18.8 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",
            "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m\n",
            "Packages installed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2. Load the dataset\n",
        "\n",
        "Huggingface 🤗 provides a datasets library which not only contains most of the  popular open-source NLP datasets, but also allows users to upload their own and share it with the world. For this exercise, we will use the `IMDB Movie Reviews` dataset which is popular for sentiment analysis tasks."
      ],
      "metadata": {
        "id": "qI2E0deXHQhs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"imdb\")\n",
        "print(\"IMDB dataset downloaded.\")"
      ],
      "metadata": {
        "id": "l4Dyp4UdHPsF",
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        "outputId": "ba970fa4-cb3c-4f77-c801-9440850314ca"
      },
      "execution_count": null,
      "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/7.81k [00:00<?, ?B/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/21.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11bf71d6e5814264ac8fe1d05d27c25b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cc76f66d33c4b9785ac2ad82f898a1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/42.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "352fcb8a53564f5589452e6be5373412"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "406ee038de854b318b7ea82fdafa80ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9b7ae95f65fc4fb582545202ffd658bd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97421bbbb81a4c20a776c1c6f6670941"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IMDB dataset downloaded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3. Inspect the dataset\n",
        "\n",
        "Let's inspect the downloaded dataset.  We see that the dataset contains two fields: `text` and `label`. The `text` field contains the review and the `label` field contains the classification of the review as positive or negative. `label` can contain one of two values `0` and `1`. The former indicates a *negative* review while the latter, *positive*."
      ],
      "metadata": {
        "id": "Jhi09IkuIntu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's inspect the dataset\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vtZ7km1hHnN5",
        "outputId": "efd802a2-6af7-4339-bb1b-8136f0c2eb3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    unsupervised: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 50000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4. Process the dataset\n",
        "\n",
        "Transformers generally deal with tokens and not raw characters. Hence, to train a model, we must set up utility functions to tokenize the given text. A tokenizer splits a given text into words and replaces each word with a number (input_id). Each sample is padded to the longest sample or truncated to the maximum length that a model can handle."
      ],
      "metadata": {
        "id": "JyTN1ZQDfE5n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
        "\n",
        "def tokenize_function(samples):\n",
        "    return tokenizer(samples[\"text\"], padding=\"max_length\", truncation=True, return_tensors='pt')\n",
        "\n",
        "# Tokenizing the entire dataset will take a few minutes\n",
        "tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=[\"text\"])\n",
        "\n",
        "# Use the set_format() function to set the dataset format to be compatible with PyTorch:\n",
        "tokenized_datasets.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"label\"])\n",
        "\n",
        "print(\"Dataset tokenized.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258,
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        "id": "CUQrZDQkcUBq",
        "outputId": "cb9a31bf-6bcf-49f1-9020-6aee121324f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2d59b7147d044325a2968acebe16a519"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cfadee7d198d41b493e9b14172bdd55a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "790eca63d4b0488ba08358bb910638aa"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset tokenized.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What the dataset looks like after tokenization\n",
        "tokenized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GPV7hkjczR5L",
        "outputId": "d64106a5-19cb-4aa3-b115-bf7f62a78b36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['label', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['label', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    unsupervised: Dataset({\n",
              "        features: ['label', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 50000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A tokenized sample\n",
        "print(\"Label:\", {tokenized_datasets[\"train\"][\"label\"][0]})\n",
        "print(\"Input IDs:\", {tokenized_datasets[\"train\"][\"input_ids\"][0]})\n",
        "print(\"Attention Mask:\", {tokenized_datasets[\"train\"][\"attention_mask\"][0]})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGDlDOr_vM5F",
        "outputId": "61cb4aa3-1bcd-456d-dbe1-ec23b74b0656"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: {tensor(0)}\n",
            "Input IDs: {tensor([  101,  1045, 12524,  1045,  2572,  8025,  1011,  3756,  2013,  2026,\n",
            "         2678,  3573,  2138,  1997,  2035,  1996,  6704,  2008,  5129,  2009,\n",
            "         2043,  2009,  2001,  2034,  2207,  1999,  3476,  1012,  1045,  2036,\n",
            "         2657,  2008,  2012,  2034,  2009,  2001,  8243,  2011,  1057,  1012,\n",
            "         1055,  1012,  8205,  2065,  2009,  2412,  2699,  2000,  4607,  2023,\n",
            "         2406,  1010,  3568,  2108,  1037,  5470,  1997,  3152,  2641,  1000,\n",
            "         6801,  1000,  1045,  2428,  2018,  2000,  2156,  2023,  2005,  2870,\n",
            "         1012,  1026,  7987,  1013,  1028,  1026,  7987,  1013,  1028,  1996,\n",
            "         5436,  2003,  8857,  2105,  1037,  2402,  4467,  3689,  3076,  2315,\n",
            "        14229,  2040,  4122,  2000,  4553,  2673,  2016,  2064,  2055,  2166,\n",
            "         1012,  1999,  3327,  2016,  4122,  2000,  3579,  2014,  3086,  2015,\n",
            "         2000,  2437,  2070,  4066,  1997,  4516,  2006,  2054,  1996,  2779,\n",
            "        25430, 14728,  2245,  2055,  3056,  2576,  3314,  2107,  2004,  1996,\n",
            "         5148,  2162,  1998,  2679,  3314,  1999,  1996,  2142,  2163,  1012,\n",
            "         1999,  2090,  4851,  8801,  1998,  6623,  7939,  4697,  3619,  1997,\n",
            "         8947,  2055,  2037, 10740,  2006,  4331,  1010,  2016,  2038,  3348,\n",
            "         2007,  2014,  3689,  3836,  1010, 19846,  1010,  1998,  2496,  2273,\n",
            "         1012,  1026,  7987,  1013,  1028,  1026,  7987,  1013,  1028,  2054,\n",
            "         8563,  2033,  2055,  1045,  2572,  8025,  1011,  3756,  2003,  2008,\n",
            "         2871,  2086,  3283,  1010,  2023,  2001,  2641, 26932,  1012,  2428,\n",
            "         1010,  1996,  3348,  1998, 16371, 25469,  5019,  2024,  2261,  1998,\n",
            "         2521,  2090,  1010,  2130,  2059,  2009,  1005,  1055,  2025,  2915,\n",
            "         2066,  2070, 10036,  2135,  2081, 22555,  2080,  1012,  2096,  2026,\n",
            "         2406,  3549,  2568,  2424,  2009, 16880,  1010,  1999,  4507,  3348,\n",
            "         1998, 16371, 25469,  2024,  1037,  2350, 18785,  1999,  4467,  5988,\n",
            "         1012,  2130, 13749,  7849, 24544,  1010, 15835,  2037,  3437,  2000,\n",
            "         2204,  2214,  2879,  2198,  4811,  1010,  2018,  3348,  5019,  1999,\n",
            "         2010,  3152,  1012,  1026,  7987,  1013,  1028,  1026,  7987,  1013,\n",
            "         1028,  1045,  2079,  4012,  3549,  2094,  1996, 16587,  2005,  1996,\n",
            "         2755,  2008,  2151,  3348,  3491,  1999,  1996,  2143,  2003,  3491,\n",
            "         2005,  6018,  5682,  2738,  2084,  2074,  2000,  5213,  2111,  1998,\n",
            "         2191,  2769,  2000,  2022,  3491,  1999, 26932, 12370,  1999,  2637,\n",
            "         1012,  1045,  2572,  8025,  1011,  3756,  2003,  1037,  2204,  2143,\n",
            "         2005,  3087,  5782,  2000,  2817,  1996,  6240,  1998, 14629,  1006,\n",
            "         2053, 26136,  3832,  1007,  1997,  4467,  5988,  1012,  2021,  2428,\n",
            "         1010,  2023,  2143,  2987,  1005,  1056,  2031,  2172,  1997,  1037,\n",
            "         5436,  1012,   102,     0,     0,     0,     0,     0,     0,     0,\n",
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            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0])}\n",
            "Attention Mask: {tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##5. Create data loaders\n",
        "\n",
        "Data loaders are used to create an iterator, which can be used to load data in batches during the training loop. They can also perform various other tasks such as type conversion, collation, etc."
      ],
      "metadata": {
        "id": "H6j-S4q2irTJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Randomly pick out a subset of samples from out dataset for quicker training\n",
        "train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42).select(range(10000))\n",
        "test_dataset = tokenized_datasets[\"test\"].shuffle(seed=42).select(range(1000))\n",
        "\n",
        "# Create an \"iterator\" object to load data in batches\n",
        "# A batch is a subset of the data that the model uses for training at once\n",
        "train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=16, num_workers=2)\n",
        "eval_dataloader = DataLoader(test_dataset, batch_size=16, num_workers=2)"
      ],
      "metadata": {
        "id": "22acadZlfsP0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_dataloader.dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JpLxO-S40-2Y",
        "outputId": "3d0352c7-4284-4fd5-c3c0-39f388e78a25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['label', 'input_ids', 'attention_mask'],\n",
            "    num_rows: 10000\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##6. Load pretrained model and set training parameters\n",
        "\n",
        "We use the pretrained `distilbert-base-uncased` model, which is a smaller, faster version of BERT, a popular transformer model.\n",
        "\n",
        "**AutoModelForSequenceClassification** customizes this model for the task of sequence classification (here, classifying movie reviews into positive or negative).\n",
        "\n",
        "\n",
        "**Optimizer Setup:** AdamW is an optimization algorithm used to minimize the loss during training. The learning rate lr=5e-5 is set, which determines how much the model updates its weights in response to the estimated error each time the model weights are updated.\n",
        "\n",
        "**Training Parameters:** We're setting the number of epochs (num_epochs) as 3, meaning the entire training dataset will be passed through the model three times. num_training_steps is the total number of training steps the model will undergo, calculated by multiplying the number of epochs by the number of batches in the training data.\n",
        "\n",
        "**Learning Rate Scheduler:** The scheduler adjusts the learning rate over the course of training, here using a linear schedule with no warmup steps."
      ],
      "metadata": {
        "id": "1ZI-7ez3mFfY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AdamW\n",
        "from transformers import get_scheduler\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "# Load pretrained model and set training parameters\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2)\n",
        "\n",
        "# Set parameters for our training schedule\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "\n",
        "# How many times our model will see the entire training set\n",
        "num_epochs = 3\n",
        "\n",
        "# How frequently the model's weights are updated (= once for each batch of data)\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "\n",
        "# Create a training schedule to tell the model how to finetune\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138,
          "referenced_widgets": [
            "406d379406984b6c83afe0b0ce03403c",
            "b0863e2722b04f9d87b671d961c1c670",
            "9e4780881e3b4d0ebea701e86905b894",
            "ae01ac1fe5464f37b625cac3ac245faa",
            "7fc738de05a14834a28fc702351d7a13",
            "4b43880789d9449cb1a7262577734ad8",
            "1ddcaba9e62045cfb8f2ac7705ca2ac8",
            "08724f467e5c4c6c9783d0868c84064e",
            "20a568efd2154580816f701c2385e412",
            "f8f0786347bc4afaa1c488a38454dbf5",
            "4569fc340ad24265b2a8645ad7dd325d"
          ]
        },
        "id": "Zej0YHoPl_R4",
        "outputId": "f5c10803-b78b-4615-82de-92107cc149d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "406d379406984b6c83afe0b0ce03403c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:429: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect at the model details\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "code",
        "id": "cNpzcgBP2H-f",
        "outputId": "6663d15a-a445-4b79-8496-ae11bb98fd29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DistilBertForSequenceClassification(\n",
              "  (distilbert): 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",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              "  (dropout): Dropout(p=0.2, inplace=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://github.com/lisabecker/nlp-fundamentals/blob/main/graphics/transformer_architecture.png?raw=true\" width=\"40%\">"
      ],
      "metadata": {
        "id": "0sgoaML6bQKN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##7. Run Training loop\n",
        "**Setting Up the Device:** The model training will be performed on a GPU if available (faster) or on a CPU if a GPU is not available.\n",
        "\n",
        "**Training Process:** For each epoch, the training data is passed through the model in batches. The model's output includes the loss, which measures the difference between the predicted and actual values.\n",
        "\n",
        "**Backpropagation and Optimization:** The loss.backward() call performs backpropagation to calculate gradients, and optimizer.step() updates the model's weights based on these gradients. The learning rate is updated at each step using lr_scheduler.step(), and optimizer.zero_grad() clears old gradients, ensuring that only the most recent gradients are used in the next step.\n",
        "\n",
        "**Progress Tracking:** A progress bar is displayed using tqdm, showing the number of steps completed out of the total."
      ],
      "metadata": {
        "id": "dVHDqtVLnlVg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Use GPU if available - you can change it under \"Runtime\" -> \"Change runtime type\"\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "# Number of steps after which to log training stats\n",
        "log_steps = 50\n",
        "training_step_count = 0\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    print(f\"\\nEpoch {epoch}\")\n",
        "    for batch in train_dataloader:\n",
        "        # The training data is passed through the model\n",
        "        batch = {k: v.to(device) for k, v in batch.items()}\n",
        "        outputs = model(input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"], labels=batch[\"label\"])\n",
        "\n",
        "        # Perform backpropagation to calculate new weights\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "\n",
        "        # Update the training steps based on the loss\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)\n",
        "\n",
        "        # Calculate the average loss for this epoch\n",
        "        training_step_count += 1\n",
        "        if training_step_count % log_steps == 0:\n",
        "            print(f\"Training loss at step {training_step_count}: {loss.item()}\")\n",
        "\n",
        "# Save finetuned model\n",
        "model.save_pretrained(\"distilbert-uncased-imdb-finetune\", from_pt=True)\n",
        "print(\"\\nModel finetuned and saved to disk.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830,
          "referenced_widgets": [
            "3cbab58f027e45ba9ae527ab7a90f8f3",
            "92ab07dcb318477c9c7035d6d6f4e431",
            "f007423d3f01497cbbe69489713a7442",
            "01a2d190bc1243f7ae7ba1045e2c1011",
            "9c96c6af878344baae4926fe51adbea4",
            "b603187279d34826bdd7345e7c7ddc9b",
            "1933fd06bcab42e4a14b3d837b873958",
            "5a6857e8330a49f79fd2c1e4c4bafba2",
            "2774862486c848d9a8b2956279547ab7",
            "789b2d1c6cd34c0595336049c2281b65",
            "755d9fe843894bc88c4a6b89768a1fd6"
          ]
        },
        "id": "76NQqsx-nguO",
        "outputId": "03c81b68-08e2-49a8-d7d8-b3bc0dac2087"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1875 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3cbab58f027e45ba9ae527ab7a90f8f3"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 0\n",
            "Training loss at step 50: 0.29767802357673645\n",
            "Training loss at step 100: 0.174493670463562\n",
            "Training loss at step 150: 0.11936938762664795\n",
            "Training loss at step 200: 0.2581367790699005\n",
            "Training loss at step 250: 0.3692319989204407\n",
            "Training loss at step 300: 0.27673476934432983\n",
            "Training loss at step 350: 0.5609225630760193\n",
            "Training loss at step 400: 0.4167221486568451\n",
            "Training loss at step 450: 0.3462904393672943\n",
            "Training loss at step 500: 0.3550032377243042\n",
            "Training loss at step 550: 0.2580909729003906\n",
            "Training loss at step 600: 0.11381731182336807\n",
            "\n",
            "Epoch 1\n",
            "Training loss at step 650: 0.024932971224188805\n",
            "Training loss at step 700: 0.13669981062412262\n",
            "Training loss at step 750: 0.06672060489654541\n",
            "Training loss at step 800: 0.09782260656356812\n",
            "Training loss at step 850: 0.03975396230816841\n",
            "Training loss at step 900: 0.2670494318008423\n",
            "Training loss at step 950: 0.052920956164598465\n",
            "Training loss at step 1000: 0.0629207193851471\n",
            "Training loss at step 1050: 0.12790869176387787\n",
            "Training loss at step 1100: 0.06985287368297577\n",
            "Training loss at step 1150: 0.03696947172284126\n",
            "Training loss at step 1200: 0.06493562459945679\n",
            "Training loss at step 1250: 0.10297051072120667\n",
            "\n",
            "Epoch 2\n",
            "Training loss at step 1300: 0.0054493192583322525\n",
            "Training loss at step 1350: 0.26297202706336975\n",
            "Training loss at step 1400: 0.019007213413715363\n",
            "Training loss at step 1450: 0.06415711343288422\n",
            "Training loss at step 1500: 0.012543990276753902\n",
            "Training loss at step 1550: 0.006765526719391346\n",
            "Training loss at step 1600: 0.018053581938147545\n",
            "Training loss at step 1650: 0.004664943553507328\n",
            "Training loss at step 1700: 0.0030053001828491688\n",
            "Training loss at step 1750: 0.06778151541948318\n",
            "Training loss at step 1800: 0.008168407715857029\n",
            "Training loss at step 1850: 0.00652000168338418\n",
            "\n",
            "Model finetuned and saved to disk.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##8. Evaluate model on test set\n",
        "Let's test the model on a new review"
      ],
      "metadata": {
        "id": "gGGMOVZh5QTE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Map the label ID to the actual class for better readability\n",
        "label_names = dataset[\"train\"].features[\"label\"].names\n",
        "def get_label_name(label_id):\n",
        "    return label_names[label_id]"
      ],
      "metadata": {
        "id": "pnf5oft8eMKW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample = {\"text\": \"I really enjoyed this one! It had a lot of funny moments.\"}\n",
        "\n",
        "tokenized_sample = tokenize_function(sample).to(device)\n",
        "output = model(input_ids=tokenized_sample[\"input_ids\"], attention_mask=tokenized_sample[\"attention_mask\"])\n",
        "prediction = torch.argmax(output.logits, dim=-1)\n",
        "\n",
        "print(f\"<{sample['text']}> is a {get_label_name(prediction)} review.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYjKJe-wDkK2",
        "outputId": "1fe94ad2-b6ab-499e-d140-d83a100929d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<I really enjoyed this one! It had a lot of funny moments.> is a pos review.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Evaluation Mode:** model.eval() is used to set the model to evaluation mode, disabling specific layers and behaviors like dropout layers used in training.\n",
        "\n",
        "**Generating Predictions:** The model generates predictions for the test set. torch.no_grad() tells PyTorch not to calculate gradients, which is necessary during evaluation.\n",
        "\n",
        "**Calculating Accuracy:** Predictions are compared with actual labels (references) to calculate the model's accuracy. This is the proportion of predictions the model got right, giving a straightforward metric to evaluate the model's performance."
      ],
      "metadata": {
        "id": "zNbwRn8ovmKO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
        "import numpy as np\n",
        "\n",
        "def evaluate_model(model, dataloader, device):\n",
        "    model.eval()  # Set the model to evaluation mode\n",
        "\n",
        "    predictions = []\n",
        "    references = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        # Predict the labels for all samples in all batches\n",
        "        for batch in dataloader:\n",
        "            batch = {k: v.to(device) for k, v in batch.items()}\n",
        "            outputs = model(input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"])\n",
        "\n",
        "            logits = outputs.logits\n",
        "            batch_predictions = torch.argmax(logits, dim=-1).tolist()\n",
        "            batch_references = batch[\"label\"].tolist()\n",
        "\n",
        "            # Add the predictions and true labels\n",
        "            predictions.extend(batch_predictions)\n",
        "            references.extend(batch_references)\n",
        "\n",
        "    # Calculate metrics\n",
        "    accuracy = accuracy_score(references, predictions)\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(references, predictions, average='binary')\n",
        "\n",
        "    return accuracy, precision, recall, f1\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy, precision, recall, f1 = evaluate_model(model, eval_dataloader, device)\n",
        "\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(f\"F1 Score: {f1:.4f}\")"
      ],
      "metadata": {
        "id": "e0i9Ra1x9vnF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2020632c-cbc0-4727-c46e-9205242021e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9130\n",
            "Precision: 0.8955\n",
            "Recall: 0.9303\n",
            "F1 Score: 0.9126\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pDYcPHs5gRyU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}