Fine-tuning Distilbert (PyTorch)

fine-tuning-a-masked-language-model-pytorch.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/Daethyra/abd3cc5a845a2f4d487e21c6a654ca12/copy-of-fine-tuning-a-masked-language-model-pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0om2eUilS1Tx"
      },
      "source": [
        "# Fine-tuning a masked language model (PyTorch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8cA3jDV8S1T1"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Yc9t_u52S1T1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cae6288f-161e-4917-e759-ade54141485b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.14.5-py3-none-any.whl (519 kB)\n",
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            "\u001b[?25hCollecting sentencepiece!=0.1.92,>=0.1.91 (from transformers[sentencepiece])\n",
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            "Installing collected packages: sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, tokenizers, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.16.4 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.14.0 transformers-4.34.0 xxhash-3.3.0\n",
            "Collecting accelerate\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.23.0\n",
            "\u001b[31mERROR: torch_xla-1.9-cp37-cp37m-linux_x86_64.whl is not a supported wheel on this platform.\u001b[0m\u001b[31m\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (3.0.2-1ubuntu0.2).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 18 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]\n",
        "!pip install accelerate\n",
        "# To run the training on TPU, you will need to uncomment the following line:\n",
        "!pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
        "!apt install git-lfs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWyW_lTTS1T3"
      },
      "source": [
        "You will need to setup git, adapt your email and name in the following cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "cMB9AVZcS1T3"
      },
      "outputs": [],
      "source": [
        "!git config --global user.email \"daethyra@pm.me\"\n",
        "!git config --global user.name \"Daethyra\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X06YqWKnS1T4"
      },
      "source": [
        "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "7AkqR0c9S1T4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "0ca5e500fb0643b4a53be765921d8234",
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            "1fa8558ab78d471ea3820bcdbe7f43e4"
          ]
        },
        "outputId": "633d68d3-149f-4ce3-866e-af5d3d9eae10"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "25baee739fa04eef82e0376d11eab9cb"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "WY70ZipVS1T5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "6f38eba1aed64902aac861c2762a74fa",
            "1e01eff2d83e4817a937380446eb0904",
            "980bb86d11904c3d9b04a9c25bcabf0f",
            "b3783982096c4269ba86b59994de2611",
            "7eaa2f7325764f45981f615dbebdc739",
            "41ce3fcc0ed64f89a43d7efbfdc5c2eb"
          ]
        },
        "outputId": "4bc67429-df58-449d-ef6e-bc2bf8751cde"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf7c890e1e1b40e3814522aa90b6b84b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea45ee7d48364d4db0d3caae09c731cf"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoModelForMaskedLM\n",
        "\n",
        "model_checkpoint = \"distilbert-base-uncased\"\n",
        "model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "bTp92NkFS1T5",
        "outputId": "939ee152-c588-43ce-ea05-15b901e5648e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'>>> DistilBERT number of parameters: 67M'\n",
            "'>>> BERT number of parameters: 110M'\n"
          ]
        }
      ],
      "source": [
        "distilbert_num_parameters = model.num_parameters() / 1_000_000\n",
        "print(f\"'>>> DistilBERT number of parameters: {round(distilbert_num_parameters)}M'\")\n",
        "print(f\"'>>> BERT number of parameters: 110M'\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Dvb7hfS2S1T6"
      },
      "outputs": [],
      "source": [
        "text = \"This is a great [MASK].\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "4y9mk89jS1T6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "f3c789825f7f48ed860c3cb15995adca",
            "bd0e47cb912545a8bad33e7c96c66406",
            "d778a77b89f34fecab0726c2a5b38acc",
            "049ed1da66e942ffb114d5350ad87cf1",
            "cf8c8e9e8b2a4ccbaea65e592f7dbd7a",
            "d79420498e634d919c38f2af38559d50",
            "bd830af25d264f0eb720add5a61d53e0"
          ]
        },
        "outputId": "8f225cc5-5cb8-4519-8906-e84636dfb3fb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c3aa9652d5a3438ca1b31e2a860fdb45"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a039353486444c5a2faa223a9d260d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49a17ee377ee42d3a78a3b9b01c4208e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "7XijUOZzS1T7",
        "outputId": "e3e5bfc8-112a-45c3-db83-b0c981854951",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'>>> This is a great deal.'\n",
            "'>>> This is a great success.'\n",
            "'>>> This is a great adventure.'\n",
            "'>>> This is a great idea.'\n",
            "'>>> This is a great feat.'\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"pt\")\n",
        "token_logits = model(**inputs).logits\n",
        "# Find the location of [MASK] and extract its logits\n",
        "mask_token_index = torch.where(inputs[\"input_ids\"] == tokenizer.mask_token_id)[1]\n",
        "mask_token_logits = token_logits[0, mask_token_index, :]\n",
        "# Pick the [MASK] candidates with the highest logits\n",
        "top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()\n",
        "\n",
        "for token in top_5_tokens:\n",
        "    print(f\"'>>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}'\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "upj5M8uQS1T7",
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        "colab": {
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          "height": 484,
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.31k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "47a54d3154c4402ba8ad2d5bfebd9f52"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e210d17922cc478ab2bd55b9d2703adb"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/7.59k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ff34138ff3a4dcbbd1a08eb7bed7b2d"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/84.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "ba1393bf876547ae8b615f51e4d8236e"
            }
          },
          "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": "85455998d513496ebbb0bd73be5e752e"
            }
          },
          "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": "bb34d97b3ba340b8a3b2566e6f31ec1c"
            }
          },
          "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": "986597227d7a433bb3f3e12c529f6a91"
            }
          },
          "metadata": {}
        },
        {
          "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": 9
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "imdb_dataset = load_dataset(\"imdb\")\n",
        "imdb_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "14B0BkOTS1T7",
        "outputId": "a3726888-91ef-48b2-cc03-cb7928e6ce80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "'>>> Review: There is no relation at all between Fortier and Profiler but the fact that both are police series about violent crimes. Profiler looks crispy, Fortier looks classic. Profiler plots are quite simple. Fortier's plot are far more complicated... Fortier looks more like Prime Suspect, if we have to spot similarities... The main character is weak and weirdo, but have \"clairvoyance\". People like to compare, to judge, to evaluate. How about just enjoying? Funny thing too, people writing Fortier looks American but, on the other hand, arguing they prefer American series (!!!). Maybe it's the language, or the spirit, but I think this series is more English than American. By the way, the actors are really good and funny. The acting is not superficial at all...'\n",
            "'>>> Label: 1'\n",
            "\n",
            "'>>> Review: This movie is a great. The plot is very true to the book which is a classic written by Mark Twain. The movie starts of with a scene where Hank sings a song with a bunch of kids called \"when you stub your toe on the moon\" It reminds me of Sinatra's song High Hopes, it is fun and inspirational. The Music is great throughout and my favorite song is sung by the King, Hank (bing Crosby) and Sir \"Saggy\" Sagamore. OVerall a great family movie or even a great Date movie. This is a movie you can watch over and over again. The princess played by Rhonda Fleming is gorgeous. I love this movie!! If you liked Danny Kaye in the Court Jester then you will definitely like this movie.'\n",
            "'>>> Label: 1'\n",
            "\n",
            "'>>> Review: George P. Cosmatos' \"Rambo: First Blood Part II\" is pure wish-fulfillment. The United States clearly didn't win the war in Vietnam. They caused damage to this country beyond the imaginable and this movie continues the fairy story of the oh-so innocent soldiers. The only bad guys were the leaders of the nation, who made this war happen. The character of Rambo is perfect to notice this. He is extremely patriotic, bemoans that US-Americans didn't appreciate and celebrate the achievements of the single soldier, but has nothing but distrust for leading officers and politicians. Like every film that defends the war (e.g. \"We Were Soldiers\") also this one avoids the need to give a comprehensible reason for the engagement in South Asia. And for that matter also the reason for every single US-American soldier that was there. Instead, Rambo gets to take revenge for the wounds of a whole nation. It would have been better to work on how to deal with the memories, rather than suppressing them. \"Do we get to win this time?\" Yes, you do.'\n",
            "'>>> Label: 0'\n"
          ]
        }
      ],
      "source": [
        "sample = imdb_dataset[\"train\"].shuffle(seed=42).select(range(3))\n",
        "\n",
        "for row in sample:\n",
        "    print(f\"\\n'>>> Review: {row['text']}'\")\n",
        "    print(f\"'>>> Label: {row['label']}'\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
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            "3e07426c822a4e98a5f6d6453cd74e2f",
            "ec9f74d233f842b988db378e0d2ffea7",
            "7da4760d77c343f6b39318f25fb279f4",
            "9421fe07b0a4408ab7984d338af6b88c",
            "38603ca2b5c548938b2dc4b0432d2d20",
            "b5eead3fd4d840da9ed61d11f17ec7ea",
            "17ab9837eadf4eddb03c983335422465",
            "f8cc6c1b54804037a2bbfa2b9206d546",
            "9af295b6dea14e3c85f8d7c512fa6b3a",
            "52454496d8694b42bcfd7c0433d898f4"
          ]
        }
      },
      "outputs": [
        {
          "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": "a77691c0275145fb9a844d1756219fe7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (720 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        },
        {
          "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": "2bb96312572f4ea7a7cf569f78c214c0"
            }
          },
          "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": "a77895cfbe824da2ac2c1253cc106b70"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'word_ids'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'word_ids'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    unsupervised: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'word_ids'],\n",
              "        num_rows: 50000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "def tokenize_function(examples):\n",
        "    result = tokenizer(examples[\"text\"])\n",
        "    if tokenizer.is_fast:\n",
        "        result[\"word_ids\"] = [result.word_ids(i) for i in range(len(result[\"input_ids\"]))]\n",
        "    return result\n",
        "\n",
        "\n",
        "# Use batched=True to activate fast multithreading!\n",
        "tokenized_datasets = imdb_dataset.map(\n",
        "    tokenize_function, batched=True, remove_columns=[\"text\", \"label\"]\n",
        ")\n",
        "tokenized_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "AFe6sArUS1T8",
        "outputId": "af510535-2256-47bf-a479-64bea0ed20c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "512"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "tokenizer.model_max_length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "7G7zqj7PS1T8"
      },
      "outputs": [],
      "source": [
        "chunk_size = 128"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "H9ONhlOmS1T8",
        "outputId": "1c898248-50f2-47e6-d2cb-1aa62c157b94",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'>>> Review 0 length: 363'\n",
            "'>>> Review 1 length: 304'\n",
            "'>>> Review 2 length: 133'\n"
          ]
        }
      ],
      "source": [
        "# Slicing produces a list of lists for each feature\n",
        "tokenized_samples = tokenized_datasets[\"train\"][:3]\n",
        "\n",
        "for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n",
        "    print(f\"'>>> Review {idx} length: {len(sample)}'\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "UknlEQeQS1T8",
        "outputId": "9de54acb-04f2-4356-8bbe-83efe38526e3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'>>> Concatenated reviews length: 800'\n"
          ]
        }
      ],
      "source": [
        "concatenated_examples = {\n",
        "    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n",
        "}\n",
        "total_length = len(concatenated_examples[\"input_ids\"])\n",
        "print(f\"'>>> Concatenated reviews length: {total_length}'\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "-GiDb7VGS1T8",
        "outputId": "e8d6aa49-d9e4-4415-a97a-c56f04be7e31",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'>>> Chunk length: 128'\n",
            "'>>> Chunk length: 128'\n",
            "'>>> Chunk length: 128'\n",
            "'>>> Chunk length: 128'\n",
            "'>>> Chunk length: 128'\n",
            "'>>> Chunk length: 128'\n",
            "'>>> Chunk length: 32'\n"
          ]
        }
      ],
      "source": [
        "chunks = {\n",
        "    k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
        "    for k, t in concatenated_examples.items()\n",
        "}\n",
        "\n",
        "for chunk in chunks[\"input_ids\"]:\n",
        "    print(f\"'>>> Chunk length: {len(chunk)}'\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "kVkx-86SS1T9"
      },
      "outputs": [],
      "source": [
        "def group_texts(examples):\n",
        "    # Concatenate all texts\n",
        "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
        "    # Compute length of concatenated texts\n",
        "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
        "    # We drop the last chunk if it's smaller than chunk_size\n",
        "    total_length = (total_length // chunk_size) * chunk_size\n",
        "    # Split by chunks of max_len\n",
        "    result = {\n",
        "        k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
        "        for k, t in concatenated_examples.items()\n",
        "    }\n",
        "    # Create a new labels column\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "9xW7x2wPS1T9",
        "outputId": "d2e72f51-0c3c-4b2f-e682-b7e78bee62ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
            "4c471e7c12fb49fabf7c95d186be7707",
            "786d5f758377428ca9d66ffee671917d",
            "7518cafedac0461faa63141578e363d2",
            "873f90fc973f493babb918db91541043",
            "3a9e6b8132e6412f96cad906ab15597d",
            "c24a7ea87179443cb89a6590d35dc168",
            "2ad2201d43c345fca48606bb6e5f7369",
            "b36c117d72724374821e051d2069491e",
            "021975c99eb1495c8002d1c8c2457cab",
            "c367d68e963b49268e7a80db4f159aa3",
            "ced7afbf90844b77ade8af9ef1d0e6b2",
            "aa4aa2168320458e9cede50e94664bdf",
            "076618b8be8246348b3c43be63ab5381",
            "7aeb1df28683414c93a1634575ea09ca",
            "401e4c64bc004a3688d917b9eeba4367",
            "9c08b2a8581444b5a004a93c52a619cd",
            "75ad35de906c464a90d484e63a8ee494",
            "2e07aaac70294aa59ff2bc58ff1d0af7",
            "ecdbb50480514c03b0180363980b68ab",
            "6ef6d3b7ece440869f815c262012a7da",
            "5c384039746b46d2af1ef494c47b2bbb",
            "6663da039a39448ea0c5538e9c4364a0",
            "b51f92cfef63478586326dfe717d3f59",
            "82901ddbc7a847d2a8bfbdede84f8f56",
            "5483ad39c3434dd5978d9e563457206e",
            "e6e7504d398346fc8636db6c692eb640",
            "8cf33a131c814cf08c0a94b71c5fb584",
            "8bb3dbc569c84fc5956aa10d6f6b7a6b",
            "d1fe4937dc0e415b8563d2cfeff00c85",
            "26744129616940e2b022bbb97eef4137",
            "a115aff58b6e436b86d6d6bf924b0f22",
            "2a99b7d1eed041608241d262ba475c32",
            "e971c9a93ec44323ac9df8ca4e3a41ec"
          ]
        }
      },
      "outputs": [
        {
          "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": "4c471e7c12fb49fabf7c95d186be7707"
            }
          },
          "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": "aa4aa2168320458e9cede50e94664bdf"
            }
          },
          "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": "b51f92cfef63478586326dfe717d3f59"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'word_ids', 'labels'],\n",
              "        num_rows: 61291\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'word_ids', 'labels'],\n",
              "        num_rows: 59904\n",
              "    })\n",
              "    unsupervised: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'word_ids', 'labels'],\n",
              "        num_rows: 122957\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n",
        "lm_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "F_t0be9IS1T9",
        "outputId": "f1a689c2-de51-48e7-e592-6a2e11581427",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"as the vietnam war and race issues in the united states. in between asking politicians and ordinary denizens of stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men. < br / > < br / > what kills me about i am curious - yellow is that 40 years ago, this was considered pornographic. really, the sex and nudity scenes are few and far between, even then it's not shot like some cheaply made porno. while my countrymen mind find it shocking, in reality sex and nudity are a major staple in swedish cinema. even ingmar bergman,\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "H-czGwnxS1T9"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForLanguageModeling\n",
        "\n",
        "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "5MwOxRnPS1T9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a8622fb-9a85-46eb-93d3-c3d62dc0af0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "'>>> [CLS] merlin rented [MASK] am [MASK] - yellow from my video store because of all the controversy [MASK] surrounded it when it was first released in [MASK]. i [MASK] heard that at first it was seized by [MASK] [MASK] s. customs if it ever [MASK] to enter this country [MASK] therefore being a fan of films considered \" controversial \" i really had to see [MASK] for myself. < [MASK] / > < br / > the [MASK] is centered [MASK] [MASK] young swedish drama student named lena who wants to learn everything she can about life. in particular she wants to focus her attentions to making some sort of documentary on [MASK] the average swede thought about certain political issues such'\n",
            "\n",
            "'>>> [MASK] the vietnam war and race issues in the united states. [MASK] between asking politicians and ordinary [MASK]izens of stockholm about their opinions on politics, she has sex with her drama teacher, [MASK], and married men. < br / > < br / > what kills me about i am [MASK] - yellow is that 40 years [MASK], this [MASK] considered pornographic. really, the sex and nudity scenes [MASK] few and far between, even then it's not shot like some cheaply made [MASK]o. while my [MASK]men [MASK] find it shocking, in reality sex and nudity are a major staple in swedish [MASK]. even ing [MASK] bergman,'\n"
          ]
        }
      ],
      "source": [
        "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
        "for sample in samples:\n",
        "    _ = sample.pop(\"word_ids\")\n",
        "\n",
        "for chunk in data_collator(samples)[\"input_ids\"]:\n",
        "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "ZGXBOtZkS1T9"
      },
      "outputs": [],
      "source": [
        "import collections\n",
        "import numpy as np\n",
        "\n",
        "from transformers import default_data_collator\n",
        "\n",
        "wwm_probability = 0.2\n",
        "\n",
        "\n",
        "def whole_word_masking_data_collator(features):\n",
        "    for feature in features:\n",
        "        word_ids = feature.pop(\"word_ids\")\n",
        "\n",
        "        # Create a map between words and corresponding token indices\n",
        "        mapping = collections.defaultdict(list)\n",
        "        current_word_index = -1\n",
        "        current_word = None\n",
        "        for idx, word_id in enumerate(word_ids):\n",
        "            if word_id is not None:\n",
        "                if word_id != current_word:\n",
        "                    current_word = word_id\n",
        "                    current_word_index += 1\n",
        "                mapping[current_word_index].append(idx)\n",
        "\n",
        "        # Randomly mask words\n",
        "        mask = np.random.binomial(1, wwm_probability, (len(mapping),))\n",
        "        input_ids = feature[\"input_ids\"]\n",
        "        labels = feature[\"labels\"]\n",
        "        new_labels = [-100] * len(labels)\n",
        "        for word_id in np.where(mask)[0]:\n",
        "            word_id = word_id.item()\n",
        "            for idx in mapping[word_id]:\n",
        "                new_labels[idx] = labels[idx]\n",
        "                input_ids[idx] = tokenizer.mask_token_id\n",
        "        feature[\"labels\"] = new_labels\n",
        "\n",
        "    return default_data_collator(features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "0oYN2sNGS1T9",
        "outputId": "4357ee9a-04d2-4dfc-d26e-dbef879eda15",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "'>>> [CLS] i rented i am curious - [MASK] from my video store [MASK] [MASK] all the controversy that surrounded it [MASK] it [MASK] first released in [MASK]. [MASK] also heard [MASK] at first [MASK] [MASK] [MASK] by u. [MASK]. customs [MASK] it ever tried to enter this country [MASK] therefore being a fan of films [MASK] \" controversial [MASK] i really had to [MASK] this for myself. [MASK] br / > < br [MASK] > the plot is centered around a young swedish drama student named lena [MASK] wants to learn [MASK] she can about life. [MASK] particular she wants [MASK] focus her [MASK] [MASK] to making some sort of documentary [MASK] [MASK] [MASK] [MASK] swede thought [MASK] certain [MASK] issues [MASK]'\n",
            "\n",
            "'>>> as the vietnam war and race issues in the united states. in between asking politicians [MASK] ordinary [MASK] [MASK] [MASK] of stockholm [MASK] their opinions on [MASK] [MASK] she has sex with her drama teacher, classmates, and married men. < br [MASK] > < [MASK] / > what kills me about i am curious - [MASK] [MASK] that 40 years ago, [MASK] was considered pornographic. really [MASK] the sex and nudity [MASK] are few [MASK] far between, even then it's not shot like some cheaply made [MASK] [MASK]. while my countrymen [MASK] [MASK] it shocking, in reality sex and nudity are a major [MASK] in swedish cinema. even ingmar [MASK],'\n"
          ]
        }
      ],
      "source": [
        "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
        "batch = whole_word_masking_data_collator(samples)\n",
        "\n",
        "for chunk in batch[\"input_ids\"]:\n",
        "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Qvw5ppj_S1T-",
        "outputId": "f4179a66-e959-44a6-9e72-d9b3bce74936",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'word_ids', 'labels'],\n",
              "        num_rows: 10000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'word_ids', 'labels'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "train_size = 10_000\n",
        "test_size = int(0.1 * train_size)\n",
        "\n",
        "downsampled_dataset = lm_datasets[\"train\"].train_test_split(\n",
        "    train_size=train_size, test_size=test_size, seed=42\n",
        ")\n",
        "downsampled_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1MhGBTpES1T-"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "PzE5o9G2S1T-"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "batch_size = 64\n",
        "# Show the training loss with every epoch\n",
        "logging_steps = len(downsampled_dataset[\"train\"]) // batch_size\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=f\"{model_name}-finetuned-imdb\",\n",
        "    overwrite_output_dir=True,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    weight_decay=0.01,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    push_to_hub=True,\n",
        "    fp16=True,\n",
        "    logging_steps=logging_steps,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "N8qvBpJsS1T-"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=downsampled_dataset[\"train\"],\n",
        "    eval_dataset=downsampled_dataset[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "XL0WT_zGS1T-",
        "outputId": "dcd58da7-8a31-4f6e-dd2f-221b8469c6b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [16/16 00:01]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Perplexity: 21.94\n"
          ]
        }
      ],
      "source": [
        "import math\n",
        "\n",
        "eval_results = trainer.evaluate()\n",
        "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "aTpKp5TtS1T-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "outputId": "446c33a2-fd05-4492-df6e-1ee5ec9adaed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='471' max='471' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [471/471 02:47, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.702400</td>\n",
              "      <td>2.496793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.579400</td>\n",
              "      <td>2.428111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.535400</td>\n",
              "      <td>2.450894</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='32' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [16/16 00:54]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=471, training_loss=2.6049823275037634, metrics={'train_runtime': 167.3951, 'train_samples_per_second': 179.217, 'train_steps_per_second': 2.814, 'total_flos': 994208670720000.0, 'train_loss': 2.6049823275037634, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "R5Yj6nk3S1T_",
        "outputId": "d43991bd-41d2-40cb-c5c7-2c0647c5c9eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [16/16 00:01]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Perplexity: 11.16\n"
          ]
        }
      ],
      "source": [
        "eval_results = trainer.evaluate()\n",
        "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "L0qDsRQ9S1UA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "5cddc377e3e34dfcb0df6ba90d522ee6",
            "26b0b5b52df748b1bf78f99796ee59ad",
            "3e1b0a318dd74ff88ad2184ee3282a38",
            "f9e3f5d1b80348c686d73f00f1d77467",
            "8a0ca9edabdb4527b49bf3f58cb5c0c7",
            "244ea607d73e493b9f8b15a9257142e2",
            "b5eb89b839fc409e83cf384ec7d0f20f",
            "3daf50101f6c49bea154fa7f701c2f64",
            "d7d5d5591e1e43a786065119b8b35e99",
            "7743bf1fd42b42c3aebee04f148c8187",
            "dc0de1d91e8442f8a820301ef57cc2d7"
          ]
        },
        "outputId": "d2096046-29a9-4ae4-e994-69d566583a9b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "training_args.bin:   0%|          | 0.00/4.09k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5cddc377e3e34dfcb0df6ba90d522ee6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/daethyra/distilbert-base-uncased-finetuned-imdb/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "TKqMLhzxS1UA"
      },
      "outputs": [],
      "source": [
        "def insert_random_mask(batch):\n",
        "    features = [dict(zip(batch, t)) for t in zip(*batch.values())]\n",
        "    masked_inputs = data_collator(features)\n",
        "    # Create a new \"masked\" column for each column in the dataset\n",
        "    return {\"masked_\" + k: v.numpy() for k, v in masked_inputs.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "gLxQKk5bS1UA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77,
          "referenced_widgets": [
            "1f8a3ea49ef244a3a9286823bcf8b4af",
            "0256c78ca7fc4568b47f6f0ff9ccf939",
            "06b508b5e67e4c0fb266839c520498c5",
            "cd9a2f6e5f4d417d8b4ad03081db003a",
            "1e1c501f1f454920ad5c71fede17083b",
            "ba66d404c6e04f83bbfd3e0226d444d8",
            "da15550703bb44b99bc49c1cfbb90596",
            "e13f6593b5274aa1864ea677d65bdb2f",
            "6011edde424a44ed8edfb54cf8da8cb2",
            "3d160dde1b574ada80775e8fde97efaa",
            "65f14e78fb944be587846caaac6000b7"
          ]
        },
        "outputId": "70b61053-9bb6-4e92-8c29-a839eec09672"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1f8a3ea49ef244a3a9286823bcf8b4af"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "downsampled_dataset = downsampled_dataset.remove_columns([\"word_ids\"])\n",
        "eval_dataset = downsampled_dataset[\"test\"].map(\n",
        "    insert_random_mask,\n",
        "    batched=True,\n",
        "    remove_columns=downsampled_dataset[\"test\"].column_names,\n",
        ")\n",
        "eval_dataset = eval_dataset.rename_columns(\n",
        "    {\n",
        "        \"masked_input_ids\": \"input_ids\",\n",
        "        \"masked_attention_mask\": \"attention_mask\",\n",
        "        \"masked_labels\": \"labels\",\n",
        "    }\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "UnplhFLgS1UA"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from transformers import default_data_collator\n",
        "\n",
        "batch_size = 64\n",
        "train_dataloader = DataLoader(\n",
        "    downsampled_dataset[\"train\"],\n",
        "    shuffle=True,\n",
        "    batch_size=batch_size,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "eval_dataloader = DataLoader(\n",
        "    eval_dataset, batch_size=batch_size, collate_fn=default_data_collator\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "QG0JKtAsS1UA"
      },
      "outputs": [],
      "source": [
        "from torch.optim import AdamW\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "16DugwEqS1UE"
      },
      "outputs": [],
      "source": [
        "from accelerate import Accelerator\n",
        "\n",
        "accelerator = Accelerator()\n",
        "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
        "    model, optimizer, train_dataloader, eval_dataloader\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "OEgDzWnGS1UE"
      },
      "outputs": [],
      "source": [
        "from transformers import get_scheduler\n",
        "\n",
        "num_train_epochs = 3\n",
        "num_update_steps_per_epoch = len(train_dataloader)\n",
        "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
        "\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "fswJDhl5S1UF",
        "outputId": "0dcbe420-d73f-42d1-ea86-6bc5b14a7fdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'daethyra/distilbert-base-uncased-finetuned-imdb-accelerate'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "from huggingface_hub import get_full_repo_name\n",
        "\n",
        "model_name = \"distilbert-base-uncased-finetuned-imdb-accelerate\"\n",
        "repo_name = get_full_repo_name(model_name)\n",
        "repo_name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "MXl4zCmCS1UF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312,
          "referenced_widgets": [
            "0e0371efff5d4377a35953aa1e936b6a",
            "5ca761934a6946c3a3d032ea48356c8f",
            "975f16d367da459d8dded0eb826f480a",
            "4c761991ae834fbc8026ed62fa2f7da3",
            "169badf36ad54125aca063dcd8476038",
            "526c596446f14fbd902e4218e9bd135c",
            "55b2795cbef04a63a872cc8f9180ee0b",
            "a610e0a4d42a4f698d83aa24ac176efb",
            "c5cda47dec4a45c19aa4f81717a1394e",
            "89b44a7fc4b3481ba82245643869750f",
            "e02c0b241f1a40c4a9ae2347dd061893",
            "0033d7be475343068d70852d34ecef9d",
            "c8a6060d67c64e3089ff9e943b4d1ecd",
            "26c18aacdd2c445ba096c8c4e9cdaf03",
            "8576a3c77101496fab163aaf53af0698",
            "ac2b1d329a0e42c8b49d68c6a094ab14",
            "ee036aed2f114c15955073d9c0afa084",
            "44edd2a290254b479f561eeb05590f70",
            "1e8d3675bbf1463abc1551ba36d066d5",
            "36f8e2773cf348f9a2b226e8d61ce628",
            "fc677436203e4502b6db86714467c067",
            "a964f33d420947479cfbae584260a587",
            "a0ebbbe0055b4859b3245afdc9383931",
            "30a1ca18fa414b8db20e5b3b99c601bf",
            "90b8c042c154496e9d62fc73cf80fdd0",
            "952d47ce4e51420c87519971f333d227",
            "ec160100db084661946e7658235f1812",
            "63b6efac52aa46a7b8f62054f9c8ab13",
            "10f4883487dd4e8ba7967f3428465a60",
            "8821f66735fc451889a81c0d9cd03e18",
            "244f35d3b71b4bfe90d29860fa2c35bf",
            "24a1c4ba0e5e43319d202dd4e4a2cc07",
            "5eac7a6a8e8448089d6dfc1a831a73d9",
            "240a5f4bfcf647dd9359dc1ee703c578",
            "84040f3963f1405cb2403a29e5242fd2",
            "f10e129977174493b21aa555ecef487c",
            "ff36d79f172044e58dc5a2391ba6b6ca",
            "485e9928960c4a008d7d739c1406785f",
            "8f4fd859de424b4586d5238a67c8b395",
            "4f32b5f6d3b842ce9f0152165747ad39",
            "acdc92c6f372485cbd5027665af0033b",
            "28cc592ef8e84609b78b732f9193d6c2",
            "7ebfac2fd2494f02b2c4b6cce5d71ffa",
            "aa50079d0e2d4fae87c2babf4e0e36bd"
          ]
        },
        "outputId": "a146f75b-780d-4b4d-a83e-797802936dac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/daethyra/distilbert-base-uncased-finetuned-imdb into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/daethyra/distilbert-base-uncased-finetuned-imdb into local empty directory.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Download file pytorch_model.bin:   0%|          | 7.40k/256M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e0371efff5d4377a35953aa1e936b6a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Download file training_args.bin: 100%|##########| 4.00k/4.00k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0033d7be475343068d70852d34ecef9d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Clean file training_args.bin:  25%|##5       | 1.00k/4.00k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee036aed2f114c15955073d9c0afa084"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Clean file pytorch_model.bin:   0%|          | 1.00k/256M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "240a5f4bfcf647dd9359dc1ee703c578"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import Repository\n",
        "\n",
        "model_name = \"daethyra/distilbert-base-uncased-finetuned-imdb\"\n",
        "repo_name = \"daethyra/distilbert-base-uncased-finetuned-imdb\"  # Directly set to the correct value\n",
        "repo = Repository(output_dir, clone_from=repo_name)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "o0Gf0Ks8S1UF",
        "outputId": "d9c7b18d-8fd7-4f9b-8ddf-f60a5aa81cdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "28759f2a1afe401885b7ce1526f6c1be",
            "920262aeda1244f18697635c0f1efc2a",
            "1bc9541d48554df8947bffb882743fc8",
            "4d75ef7fb7ff4222a75442a4b13afa37",
            "2a390ea75df4471cadd7b4dcec91cb34",
            "31d395d14ad348e8b708d2950700afa9",
            "69dfaa0fcda7469e95b8a923534e5534",
            "6cb42a40a064425981fa4108a745ae60",
            "9ae55184f51846a4bd13a5586f03a926",
            "ce0abb25c5f149c79a63248b95249d4b",
            "509791923ae74ae293feea83f217166b"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/471 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28759f2a1afe401885b7ce1526f6c1be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Epoch 0: Perplexity: 11.271951871891424\n",
            ">>> Epoch 1: Perplexity: 10.925637472414294\n",
            ">>> Epoch 2: Perplexity: 10.753871580028367\n"
          ]
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "import torch\n",
        "import math\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "for epoch in range(num_train_epochs):\n",
        "    # Training\n",
        "    model.train()\n",
        "    for batch in train_dataloader:\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        accelerator.backward(loss)\n",
        "\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)\n",
        "\n",
        "    # Evaluation\n",
        "    model.eval()\n",
        "    losses = []\n",
        "    for step, batch in enumerate(eval_dataloader):\n",
        "        with torch.no_grad():\n",
        "            outputs = model(**batch)\n",
        "\n",
        "        loss = outputs.loss\n",
        "        losses.append(accelerator.gather(loss.repeat(batch_size)))\n",
        "\n",
        "    losses = torch.cat(losses)\n",
        "    losses = losses[: len(eval_dataset)]\n",
        "    try:\n",
        "        perplexity = math.exp(torch.mean(losses))\n",
        "    except OverflowError:\n",
        "        perplexity = float(\"inf\")\n",
        "\n",
        "    print(f\">>> Epoch {epoch}: Perplexity: {perplexity}\")\n",
        "\n",
        "    # Save and upload\n",
        "    accelerator.wait_for_everyone()\n",
        "    unwrapped_model = accelerator.unwrap_model(model)\n",
        "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
        "    if accelerator.is_main_process:\n",
        "        tokenizer.save_pretrained(output_dir)\n",
        "        repo.push_to_hub(\n",
        "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
        "        )"
      ]
    },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/552 [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
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            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.23k [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
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              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/82.0 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
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          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
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      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "mask_filler = pipeline(\n",
        "    \"fill-mask\", model=\"daethyra/distilbert-base-uncased-finetuned-imdb\"\n",
        ")"
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          "text": [
            ">>> this is a great film.\n",
            ">>> this is a great movie.\n",
            ">>> this is a great idea.\n",
            ">>> this is a great one.\n",
            ">>> this is a great story.\n"
          ]
        }
      ],
      "source": [
        "preds = mask_filler(text)\n",
        "\n",
        "for pred in preds:\n",
        "    print(f\">>> {pred['sequence']}\")"
      ]
    }
  ],
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