masked_lm_finetuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/loganathanspr/nlp_course/blob/main/masked_lm_finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kga6FOyalmYi"
      },
      "source": [
        "# Fine-tuning a masked language model (PyTorch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kbq1NSVAlmYk"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKDvEGTGlmYl",
        "outputId": "004ef856-fa7e-49a4-d2bd-b507c22ef499"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "Installing collected packages: sentencepiece, xxhash, safetensors, dill, responses, multiprocess, huggingface-hub, tokenizers, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.1 huggingface-hub-0.17.3 multiprocess-0.70.15 responses-0.18.0 safetensors-0.4.0 sentencepiece-0.1.99 tokenizers-0.14.1 transformers-4.34.0 xxhash-3.4.1\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",
            "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": "QXLCF4RSlmYl"
      },
      "source": [
        "You will need to setup git, adapt your email and name in the following cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "JT8uScpflmYm"
      },
      "outputs": [],
      "source": [
        "!git config --global user.email \"loganathanspr@gmail.com\"\n",
        "!git config --global user.name \"Loganathan Ramasamy\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_0QqKqvlmYm"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "457aa5ac505a4bd6a9228cf37447bf84",
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            "cffc54722b66485cbdaf51e6103b4407",
            "b7d5c9906f1b403587e86a6cf20dac3f",
            "ce9b444adf6e42ed837d62b7f8bbef39",
            "559349c2433c4c06a8ac1715e3735a73",
            "c09240079fec4f1c9b2d7ffbfe6007f3",
            "5c1818d891a84ca3bce53e6723343ba4",
            "db67519e048644fe8b83a730279e4b08",
            "ae00698303984501b62cff4f26bb30a2",
            "760774b57967407bb67f5befae78752f",
            "408e75ebb17c412c8561ebcee9c3269c",
            "ca517719eda14e98832105a4607a7e81",
            "ee22e1c6b6014366aafc8a5fab6c256a",
            "23fa52b74fda4c5fa213891be93546e1",
            "74f85acbb0424e68900d45f326ff85bb",
            "21b9a8d0db584343b8aa7a9d41379015",
            "3d309ab461aa4257a76db7d2405bfdf1",
            "d469a9709fb045f1872826d7e30d9ad3",
            "901c8593376b47e7af4b5172f638f9b3",
            "dd83828ae55b49508d10876bade34210",
            "b97c707a991a42749d2f29bd06f12fad",
            "3d03cbe0ce8a45c8919d32a5d3c427c5",
            "530f6f1dcba34d9e8795c7080f36b388",
            "df6d26b89d4f49b69c507942e1c7dfad"
          ]
        },
        "id": "gPCO5-3_lmYm",
        "outputId": "8b23c165-59dc-4666-b07c-db4cdf98060b"
      },
      "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,
              "version_minor": 0,
              "model_id": "457aa5ac505a4bd6a9228cf37447bf84"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "460b76edc42d4c09b30111fe453281ed",
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            "d8c6c690ab674aff8dec9465c192c8d7",
            "1da085ece03647279686ffdf4599d799",
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            "8e7b5d26dce94567a1e385c55dd1404f",
            "9844afc82294474388af9357909b5779",
            "89c04d634316484d89840063f88a1cd5",
            "92d3012d91c64ca2b0922c5255979e4f",
            "69c5867cf7154c1f99fc632e68617268",
            "88dff2697ac54474b73bc309396cac66",
            "c7eba4dc9ed1454e83025aac78a17c14"
          ]
        },
        "id": "g5qtUdgxlmYm",
        "outputId": "a820b6b5-114a-4f07-f500-50c223e75d03"
      },
      "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": "7535b47951364dadbb0f0a31f6d7ab79"
            }
          },
          "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": "c67a13ec2ef941268819e568e1e0b3a0"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
        "model_checkpoint = \"distilbert-base-uncased\"\n",
        "model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOxoLwRilmYm",
        "outputId": "61f2acdc-bfb5-4302-d357-1fb8184fe25f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'>>> DistilBERT number of parameters: 67M'\n",
            "'>>> BERT number of parameters: 110M'\n"
          ]
        }
      ],
      "source": [
        "distil_bert_num_parameters = model.num_parameters() / 1_000_000\n",
        "print(f\"'>>> DistilBERT number of parameters: {round(distil_bert_num_parameters)}M'\")\n",
        "print(f\"'>>> BERT number of parameters: 110M'\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "e4ReC_DtlmYn"
      },
      "outputs": [],
      "source": [
        "text = \"This is a great [MASK].\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "aa95b649b935496cbd95e81df89b1563",
            "a2c62a43862c45c89bc56de505096853",
            "6304b89fde9a4e119247ec259088ba76",
            "de04f977b83543cbae8c6add468c4269",
            "60b8cefb20ca4b92ab2c33ba29a2f14f",
            "004d40193cb940ddb7b1115044944476",
            "824ed7ed5e16438aace35b2ea63adae9",
            "ff16a32a6ed1467b81c93b4339948413"
          ]
        },
        "id": "_ZC8BCNnlmYn",
        "outputId": "d8260768-178f-4dfb-f7f4-840d5784235a"
      },
      "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": "67cd470f8cb0472092a2867eacc91143"
            }
          },
          "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": "7767cc928b634ec0ba21582408c84a71"
            }
          },
          "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": "d8e5de7888d14bb2922be7151cc80b6a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2g1G4BklmYn",
        "outputId": "3d45d0fd-8d27-4b22-c74e-7f904c93be4f"
      },
      "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": {
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        },
        "id": "KYD_5V1IlmYn",
        "outputId": "f8db5035-b8f4-489d-a976-9ec09609b931"
      },
      "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": "d9fcac2f20ad4299904cc89ef2ac5aa3"
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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": "814b6eac3eea4f9bb4b5fb0137ef9c6a"
            }
          },
          "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": "7a3ec0618a594c58bd8158b0669c0cc8"
            }
          },
          "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": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de0b7e05e5244a82b9b196119cb6f9d9"
            }
          },
          "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": "ffd371adc77246dabed24d7e9c19f9bc"
            }
          },
          "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": "ee32793bb55d4a5faaeaa7373d60ff9f"
            }
          },
          "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": "4338ca09d4fc44b0a8c550b531f10b04"
            }
          },
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pRwbNYPrlmYn",
        "outputId": "310ba6dc-0f00-4dce-c2ad-1ea716b7e1c3"
      },
      "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",
      "source": [
        "unsup_sample = imdb_dataset[\"unsupervised\"].shuffle(seed=42).select(range(3))\n",
        "\n",
        "for row in unsup_sample:\n",
        "    print(f\"\\n'>>> Review: {row['text']}\")\n",
        "    print(f\"'>>> Label: {row['label']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJp8X4iaKiQi",
        "outputId": "5b107635-31e7-4c3f-9131-1901223200fd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "'>>> Review: If you've seen the classic Roger Corman version starring Vincent Price it's hard to put it out of your head, but you probably should do because this one is totally different. Subtlety has been abandoned in favour of gross-out horror - nudity, gore and all-round unpleasantness. OK it's ridiculous, trashy, sensationalised and historically dubious (did any members of the Inquisition really wear horn-rimmed glasses?), but despite all this it is strangely compelling. I literally couldn't tear myself away from the screen until the end of the movie. If there's a bigger compliment you can pay to a film I don't know what it is.\n",
            "'>>> Label: -1\n",
            "\n",
            "'>>> Review: For me, this was the most moving film of the decade. Samira Makhmalbaf shows pure bravery and vision in the making. She has an intelligence and gift for speaking to the people, regardless of their nationality or beliefs. I am inspired and touched by her humanity and can only hope that she has touched many people the same way. Her message in this film is strong, simple and pure. The human soul can survive the most unheard of cruelties and repression, yet still have the capability to hope and dream even the biggest dreams. Under the most incredible circumstances, the most unexpected people rise up to be heroes. This young girl who has recently regained her voice, yet is still afraid to use her new found freedom, is our hero. She daydreams of becoming president of war torn Afghanistan, the only vision of power that she can imagine that could truly change her current situation. We catch a glimpse of her spirit while witnessing her hardships. In the end, we are left with hope, hope that when her young voice does eventually speak out, it speaks loud and clear for all to hear- sounding a message that transcends borders, nationality and religion. The true epitome of the phoenix rising from the ashes. Hats off to the simple tale of the complex truth.\n",
            "'>>> Label: -1\n",
            "\n",
            "'>>> Review: There really isn't much to say about this \"film\". It has the odd smile or chuckle moment, but on the whole it's bland, predictable and generally pretty dull.<br /><br />The only reason I gave it three out of ten was for the annoyingly catchy jingle (which I hope I will forget soon....please God!). Otherwise its junk. Or mostly junk, interspersed with adverts for Smirnoff Ice.<br /><br />The lead characters give OK performances, but they really don't have anything much to work with.<br /><br />Best advice: Avoid it like a dentist's appointment. Or better yet, make a dentist's appointment instead of watching it.\n",
            "'>>> Label: -1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131,
          "referenced_widgets": [
            "d6ba8ab0c5e34d67b2f7d9da6972c887",
            "d6e930768ea94acaba8770d2aa3644b3",
            "e041ae0cbc3548e08e02ed855601345f",
            "ad02dc03ab8c499ba49edfc83271c9ac",
            "70e9d44f16974124bf0537f6394c665e",
            "46f8db69cb764e4d95093ac3911b4f43",
            "f8d368508b2e49a4abcc1b9f81675096",
            "2c17406652974fe98380269f9b817ffa",
            "51c49f9bdb354bd1abb338f36968aba7",
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            "9de72c5123604dbe9f6b05c257ef7d95",
            "380ee28f1fbe4950b603512ed2c55e06",
            "95f9c18ad6884270bee85a2672cc7ce2",
            "8d9fcd4b36d94280a1b423a134fb3a25",
            "bc3706805cfa4fd8a3ce10ad08fb7f33",
            "5582558e11bb463097f7e2a59ccf63cb",
            "c9dbbdf5d9f7411a9e9a1b3018e89020",
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            "678d6ac7cd9f417cafd1345bde44ca22",
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            "217cacfa2446412eafbf9697715e81e6",
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            "99520b7f94754ca4a0a2fc1b4f162a98",
            "bc8845ea2e544e2b93baa6d25682fab2",
            "4fd4f21bcb6e426d967d9f53639ea54c",
            "ad98a5bf7f2a4e038e4a566db7b09cf2",
            "635c78352788462c90ba625f440265d0"
          ]
        },
        "id": "ee-RZKO-lmYo",
        "outputId": "7b9228ed-456a-4542-918f-c0b62282c0f6"
      },
      "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": "d6ba8ab0c5e34d67b2f7d9da6972c887"
            }
          },
          "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": "9de72c5123604dbe9f6b05c257ef7d95"
            }
          },
          "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": "ee94b913287f4f2c8c32c0e6821dd491"
            }
          },
          "metadata": {}
        }
      ],
      "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",
        "# Use batched=True to activate fast multithreading!\n",
        "tokenized_datasets = imdb_dataset.map(tokenize_function, batched=True, remove_columns=[\"text\", \"label\"])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNIvYKWDODdn",
        "outputId": "235921ca-0432-4ee5-cf6a-d0d424eac45d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rAXV7XL9lmYo",
        "outputId": "3f4f36dd-d205-4e1f-e4ac-0c3dc1f29be5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "512"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "tokenizer.model_max_length"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test\n",
        "Max length in other models"
      ],
      "metadata": {
        "id": "sDbZXENvT7aL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BigBird Model"
      ],
      "metadata": {
        "id": "7kyaEW4sUZMB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BigBirdModel\n",
        "big_bird_tokenizer = AutoTokenizer.from_pretrained(\"google/bigbird-roberta-base\")\n",
        "big_bird_tokenizer.model_max_length"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
          "referenced_widgets": [
            "ba3d2f8f15d64561b5238e341880f93f",
            "88ddaa21bd5349f4a5fcc664d24bc93f",
            "9bbde11724b54c1783dd0a5a6f1c5ffc",
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            "3ea66a5abce04887aaa3b127e638926a",
            "aa3ff413747d4cca934390ff50470e4b",
            "fccca914173d49e38f84e4e1e6cc0df1",
            "b03f00c2c2cc42d4b80209d4d6527f82",
            "56ab49eaf27b4808ad2db03ad8c8ca76",
            "0c0094f66d5a4500ade92b3fcb3e5af8"
          ]
        },
        "id": "vGg7xfDyT_Qo",
        "outputId": "27b09ca8-50f0-4595-eebb-8401d5247f6a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.02k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba3d2f8f15d64561b5238e341880f93f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/760 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5df1877cae454e1cba0930a76bfc770e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/846k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1598dd5d9be4c6cae4b6ebf033451c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/775 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51c20bf8ad1c42e8bd2deb8b73808f27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4096"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Longformer model"
      ],
      "metadata": {
        "id": "fXq28azJUcWa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "longformer_tokenizer = AutoTokenizer.from_pretrained(\"allenai/longformer-base-4096\")\n",
        "longformer_tokenizer.model_max_length"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
          "referenced_widgets": [
            "944c4ff89d154379a38ea6123776af06",
            "bbc85bee808d480f80c304be63e4dd6a",
            "6f6df7d61ac944cda91697c55320f428",
            "77a5244783b74c51b091f21d5355e870",
            "8b4bd6796b5a47378e320aed94afc4cb",
            "e4c18b748dee43afbc70d0c53e56855b",
            "1e979163e2c641e89f298a7a28479fc9",
            "c07b10a510934121a582045da8b52dd7",
            "01076a9892b0438fa258f029779a832d",
            "02ea6c62a1134c2d8c64159e7fbe1c36",
            "0637078a48444495a3a5045f24d0b18c",
            "f599c14d8c7a415aadebeabde8d33cf3",
            "4052d5d17d374d0eb516ead1465ae44a",
            "5f9d04ba631949d0a648edeba167b3ff",
            "baff015d69964652bd3d0a077240aec1",
            "ec0ecffa5b3946ed9b0558021e4708c6",
            "0c8d6c24ce0c42c79fd75da24dcd4f7d",
            "daf0f0091c6e4891bd7e55b578e502ed",
            "1c7a992c3ee94179adc461fb2d9a0a09",
            "4323cfbf0d6b4cc49cb60dc7a84c793f",
            "4c097fc18d4a438580973a0594eddd56",
            "8503831c0c1545efaf3f822aff4f2020",
            "7be4894dce5f4828be14508cdeecfb29",
            "2c3635f8d4a04271b1de8ac2f699fcba",
            "efddf759c918424aa801a2235d12bfaf",
            "87ac65b4ca9b4101afbe3909104f6da1",
            "175baf53e08e4dcf8f29742a5e008c46",
            "c813ec0ffea649c18fb686e4d0943753",
            "00f3cad09a3c4013a94f1731bda17e1b",
            "5e23df86fe5340f0b7fee31ac199e82c",
            "89491e8bdb064269af79408d5f5a976e",
            "84b47e496b2846f38ec34b35ff72bb7a",
            "fe5d657740a14307b9edd78a69b295f2",
            "623f9bbba5054886850c3a8ac135c1d9",
            "7f142852b4ea4c68bfa78364fe21dccf",
            "4d6affcde02d410dac86065d90e792d7",
            "d2aed7be7f8e4da0adfa2b5211d44c0d",
            "caf56b9dbcad44e6842792cf5ff18ac2",
            "ce9d95c168ae486095bc31cfdd35ba68",
            "b89d6cb56a6d4061a112f3fc8e1789bb",
            "6163ce19700d4c3ab105c9cee9feb017",
            "d043eabf8d27436ba025ddb6ff114755",
            "52f6f93c704940cba42545a57049e6d0",
            "c0d2c03f6e8242a28f10aedfa642fecf"
          ]
        },
        "id": "z8OmpCZaUfTv",
        "outputId": "a28f6fe3-f6d6-4290-ff4d-5ccfa450478e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/694 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "944c4ff89d154379a38ea6123776af06"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f599c14d8c7a415aadebeabde8d33cf3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7be4894dce5f4828be14508cdeecfb29"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "623f9bbba5054886850c3a8ac135c1d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4096"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Contd..."
      ],
      "metadata": {
        "id": "caDY56_-VZMQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chunk_size = 128"
      ],
      "metadata": {
        "id": "ix_huLDaWgwe"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YbqV4AdilmYo",
        "outputId": "db27e388-1b52-4436-eba7-08d45150eb8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'>>> Review 0 length: 363\n",
            "'>>> Review 1 length: 304\n",
            "'>>> Review 2 length: 133\n",
            "'>>> Review 3 length: 185\n",
            "'>>> Review 4 length: 495\n"
          ]
        }
      ],
      "source": [
        "tokenized_samples = tokenized_datasets[\"train\"][:5]\n",
        "\n",
        "for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n",
        "      print(f\"'>>> Review {idx} length: {len(sample)}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_samples.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jylhfg5Xbpi7",
        "outputId": "af39254a-df51-4c0b-9960-2a567bacc4f1"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask', 'word_ids'])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5uEcubXXlmYo",
        "outputId": "5f585f96-c9ef-4bf0-dab9-eae23f813493"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'>>> Length of the concatenated sequences (input_ids): 1480\n"
          ]
        }
      ],
      "source": [
        "concatenated_samples = {\n",
        "    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n",
        "}\n",
        "total_length = len(concatenated_samples[\"input_ids\"])\n",
        "print(f\"'>>> Length of the concatenated sequences (input_ids): {total_length}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "concatenated_samples.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjzri2VRB998",
        "outputId": "61931d75-79d6-438f-b9f1-3a3085ab748a"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask', 'word_ids'])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(concatenated_samples[\"input_ids\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Knx2v28rCDlN",
        "outputId": "7d1d3d66-18de-4419-d85a-e943f7f22e5c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1480"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVZNGzPZlmYo",
        "outputId": "12f7f2a9-0961-4b73-9acd-3609a8daab73"
      },
      "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: 128'\n",
            "'>>> Chunk length: 128'\n",
            "'>>> Chunk length: 128'\n",
            "'>>> Chunk length: 128'\n",
            "'>>> Chunk length: 128'\n",
            "'>>> Chunk length: 72'\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_samples.items()\n",
        "}\n",
        "\n",
        "for chunk in chunks[\"input_ids\"]:\n",
        "    print(f\"'>>> Chunk length: {len(chunk)}'\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "iewdXtaDlmYo"
      },
      "outputs": [],
      "source": [
        "def group_texts(examples):\n",
        "    # Concatenate all texts\n",
        "    concatenated_samples = {k: sum(examples[k], []) for k in examples.keys()}\n",
        "    # Compute length of concatenated texts\n",
        "    total_length = len(concatenated_samples[list(examples.keys())[0]])\n",
        "    # We drop the last chunk\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)] for k, t in concatenated_samples.items()\n",
        "    }\n",
        "    # Create a nerw column\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363,
          "referenced_widgets": [
            "736202c3262f479c8c0453f68237ac05",
            "69466b9454334f6f81010d468e5654be",
            "ecd550e2e5ef470c9a66ce9f017b52b5",
            "ff2ff05666764a4a8e2952524dbdc7a7",
            "69c159b7b08c47c691be9cef4e24689c",
            "b0b1535f034a4e45b9f51549bebe5770",
            "d8005e557f9144ecb70e616548f2ac79",
            "0b8482803fdd4a99938b9845c19e7b85",
            "f4cfc9cc38514ba1827441db1cb3b6d8",
            "9d68b1f9853c44a384113a541e5b7034",
            "c1cb83c3024549c58a809be36409cb82",
            "e20f90655a354bb7891286d681ccc2bb",
            "e7482d8bbb154c4fa4037bd1a90b560d",
            "dc43f2d85cd74cd8ae0b1cdbb5c8f2f8",
            "d47aa14b0aa143119abf520423dfde1b",
            "ec5b089788e340cc8a925b2de6bb5c1b",
            "cfe8ced54fa24daabd109380ffd2b726",
            "36e88484fa044dda8c39f7bac920efe6",
            "3382ed6469594168b6f27186ec3d1e49",
            "39abc9258aa14682bd2c4f7048beae4c",
            "a17fae1e92d44559bbb3b89fc19887e1",
            "7b82d943a79e4ed799eaeee56e0d1b41",
            "d991cb238bcf4ac1be67a80fb84abec3",
            "f108d54c280f4e068ff0c735a95c08e4",
            "748b0eef531745868e915f8cb75950ad",
            "ff2b513932c44470a35afa520156e08b",
            "e7b282c81e374c0f8fda0ea8882d5eb8",
            "b6ec7429d34a4233b880d2245bd0c01f",
            "c470255a030a4654a8045ec12d579b97",
            "078d149fac1346738717c6eff1264f1b",
            "f6b870d3816941829277e00c8c43c820",
            "42b6489576f04c0392c46817c7d01c26",
            "5ed4200312df4cbbaf379d01986f49e7"
          ]
        },
        "id": "oWBFDaLblmYo",
        "outputId": "9016735d-d240-4f1b-feff-78c02649d081"
      },
      "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": "736202c3262f479c8c0453f68237ac05"
            }
          },
          "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": "e20f90655a354bb7891286d681ccc2bb"
            }
          },
          "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": "d991cb238bcf4ac1be67a80fb84abec3"
            }
          },
          "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": 25
        }
      ],
      "source": [
        "lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n",
        "lm_datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(lm_datasets[\"train\"][0][\"input_ids\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "FMR1MTyeJoJD",
        "outputId": "6652efcc-38c0-4825-8edc-6d50eeaec15a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[CLS] i rented i am curious - yellow from my video store because of all the controversy that surrounded it when it was first released in 1967. i also heard that at first it was seized by u. s. customs if it ever tried to enter this country, therefore being a fan of films considered \" controversial \" i really had to see this for myself. < br / > < br / > the plot is centered around a 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 what the average swede thought about certain political issues such'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "6SjcXTvXlmYo",
        "outputId": "6721aa6a-e7f2-43fc-dc25-2962d1224805"
      },
      "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": 27
        }
      ],
      "source": [
        "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(lm_datasets[\"train\"][2][\"input_ids\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "VLwZSuGgKCDp",
        "outputId": "95d6ffa4-efb9-46ae-f85e-66c8f9762866"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'arguably their answer to good old boy john ford, had sex scenes in his films. < br / > < br / > i do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in america. i am curious - yellow is a good film for anyone wanting to study the meat and potatoes ( no pun intended ) of swedish cinema. but really, this film doesn\\'t have much of a plot. [SEP] [CLS] \" i am curious : yellow \" is a risible and pretentious steaming pile. it doesn'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(lm_datasets[\"train\"][2][\"labels\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "xLhKvBR7MG83",
        "outputId": "13d56df5-cfeb-45ba-af05-406d23dae7cf"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'arguably their answer to good old boy john ford, had sex scenes in his films. < br / > < br / > i do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in america. i am curious - yellow is a good film for anyone wanting to study the meat and potatoes ( no pun intended ) of swedish cinema. but really, this film doesn\\'t have much of a plot. [SEP] [CLS] \" i am curious : yellow \" is a risible and pretentious steaming pile. it doesn'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "wto_15ZtlmYo"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForLanguageModeling\n",
        "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "YRsoUSvVlmYo"
      },
      "outputs": [],
      "source": [
        "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
        "for sample in samples:\n",
        "    _ = sample.pop(\"word_ids\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "samples[0].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iymOQhe0ONja",
        "outputId": "d4869012-091a-4cb0-d99e-4d60b613d8d1"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask', 'labels'])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for chunk in data_collator(samples)[\"input_ids\"]:\n",
        "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UbUl4EcCOItF",
        "outputId": "3eaf5142-e075-4403-9db2-8ba9b7a3f757"
      },
      "execution_count": 33,
      "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] i rented i am curious - yellow from my video store because of all [MASK] controversy that surrounded it when it was first released in 1967. i also heard that at first [MASK] was seized by u. s. [MASK] if it [MASK] tried to enter [MASK] country, therefore being a fan [MASK] films [MASK] \" controversial \" i really had to see this for myself. [MASK] br / > < br / > the plot [MASK] centered around a [MASK] [MASK] drama [MASK] 1702 [MASK] who wants to [MASK] everything she can about life. in particular she wants to focus her attentions to making [MASK] sort of documentary on what the average swede thought about certain political issues such'\n",
            "\n",
            "'>>> as the [MASK] war and [MASK] issues in the united states [MASK] in between [MASK] politicians and ordinary [MASK]izens pristine stockholm about their [MASK] on politics [MASK] she has sex with [MASK] [MASK] teacher, [MASK], and married men. < br / [MASK] < br [MASK] > what kills me about i am curious - yellow is that 40 years ago, this was considered pornographic. [MASK], the sex [MASK] nudity scenes are few and [MASK] between, even then it'[MASK] not shot like some cheaply nutrient porno. while my [MASK] [MASK] mind find it shocking, in [MASK] sex and nudity are a major staple in swedish cinema. even functioningmar [MASK] [MASK]'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "oTrFN1ymlmYo"
      },
      "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": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmlOTXWnlmYo",
        "outputId": "b39d6527-429b-49ba-c920-426412f800f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "'>>> [CLS] [MASK] rented [MASK] [MASK] curious - [MASK] [MASK] my video store because [MASK] [MASK] [MASK] controversy that [MASK] it when [MASK] was first released [MASK] 1967. i [MASK] heard that at first [MASK] was seized [MASK] u [MASK] s. customs [MASK] it ever [MASK] to enter this country, [MASK] being a fan of films considered \" controversial \" i really [MASK] to see this for [MASK] [MASK] < br / > [MASK] br / > the plot [MASK] centered around a young [MASK] drama [MASK] named lena who [MASK] to [MASK] everything she can [MASK] life. in particular she wants to focus her attentions to making some [MASK] of documentary on what the average swede thought about [MASK] political issues [MASK]\n",
            "\n",
            "'>>> [MASK] the vietnam war [MASK] race issues in the united states. [MASK] [MASK] asking politicians and ordinary denizens of stockholm [MASK] their opinions on politics, she has sex [MASK] her drama teacher, classmates, [MASK] [MASK] men. < br / > [MASK] br / > what kills me about i am curious - yellow [MASK] [MASK] 40 years [MASK], this was considered pornographic. really [MASK] the [MASK] and [MASK] [MASK] scenes are [MASK] [MASK] far between, even then it [MASK] [MASK] [MASK] shot like [MASK] cheaply made porno. while my countrymen mind find it [MASK], in reality [MASK] and nudity [MASK] a major [MASK] [MASK] swedish cinema [MASK] even ingmar bergman,\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",
      "source": [
        "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
        "print(len(samples))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWlm2hOKb19Q",
        "outputId": "f32eee3e-2345-4cb2-b8e6-9777768f16c5"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(samples)):\n",
        "    print(f\"\\n'>>> {tokenizer.convert_ids_to_tokens(samples[i]['input_ids'])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVQG0vSpcrgK",
        "outputId": "d86ba2b8-5a77-44ee-e859-5fc75c3af5b5"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "'>>> ['[CLS]', 'i', 'rented', 'i', 'am', 'curious', '-', 'yellow', 'from', 'my', 'video', 'store', 'because', 'of', 'all', 'the', 'controversy', 'that', 'surrounded', 'it', 'when', 'it', 'was', 'first', 'released', 'in', '1967', '.', 'i', 'also', 'heard', 'that', 'at', 'first', 'it', 'was', 'seized', 'by', 'u', '.', 's', '.', 'customs', 'if', 'it', 'ever', 'tried', 'to', 'enter', 'this', 'country', ',', 'therefore', 'being', 'a', 'fan', 'of', 'films', 'considered', '\"', 'controversial', '\"', 'i', 'really', 'had', 'to', 'see', 'this', 'for', 'myself', '.', '<', 'br', '/', '>', '<', 'br', '/', '>', 'the', 'plot', 'is', 'centered', 'around', 'a', 'young', 'swedish', 'drama', 'student', 'named', 'lena', 'who', 'wants', 'to', 'learn', 'everything', 'she', 'can', 'about', 'life', '.', 'in', 'particular', 'she', 'wants', 'to', 'focus', 'her', 'attention', '##s', 'to', 'making', 'some', 'sort', 'of', 'documentary', 'on', 'what', 'the', 'average', 'sw', '##ede', 'thought', 'about', 'certain', 'political', 'issues', 'such']\n",
            "\n",
            "'>>> ['as', 'the', 'vietnam', 'war', 'and', 'race', 'issues', 'in', 'the', 'united', 'states', '.', 'in', 'between', 'asking', 'politicians', 'and', 'ordinary', 'den', '##ize', '##ns', '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', 'nu', '##dity', 'scenes', 'are', 'few', 'and', 'far', 'between', ',', 'even', 'then', 'it', \"'\", 's', 'not', 'shot', 'like', 'some', 'cheap', '##ly', 'made', 'porn', '##o', '.', 'while', 'my', 'country', '##men', 'mind', 'find', 'it', 'shocking', ',', 'in', 'reality', 'sex', 'and', 'nu', '##dity', 'are', 'a', 'major', 'staple', 'in', 'swedish', 'cinema', '.', 'even', 'ing', '##mar', 'bergman', ',']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "da_iQ9wNlmYo",
        "outputId": "7aace1f8-f8c6-4f3e-d8b6-5e7fb3ff6657"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'word_ids', 'labels'],\n",
              "        num_rows: 3000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'word_ids', 'labels'],\n",
              "        num_rows: 300\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "train_size = 3000\n",
        "test_size = int(0.1*train_size)\n",
        "downsampled_dataset = lm_datasets[\"train\"].train_test_split(train_size=train_size, test_size=test_size, seed=42)\n",
        "downsampled_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "525faeb7359446dab00306e0d803e3b7",
            "b91cc7f829314f3eb32785bb13770dbb",
            "d3d737d85e1349d3a031b300f6526f75",
            "b572fc6d38d7419a923a9d387a6dd88c",
            "f388223f21c342d693d52dc042534cbe",
            "ffa05f6447164621b11c186a8aa4fb05",
            "b5d2ac3a7db24a669777517cc63decf2",
            "3ab3086b112a4074be4800f906a8cb78",
            "83f65df54ebc4ebc9b4667b4ee229844",
            "a3b2e6a91ac240a7b52ce78925685940",
            "52153ceb40374daa9a2097c926a64d90",
            "f29d4a4847ab4b6aa31a184b7c940d06",
            "e224c0a30a16480a91712c23bc3e2a0a",
            "ef13a7ceddf94f05be4e43bf5109a2e4",
            "f450156fc7ca4c2b89f16b6b3c456e72",
            "bb38aa17381e4caeb04b929dbda15afe",
            "18bf1b3702a24ece8a9d88d70377894a",
            "b16df86131e84916b5a3bbb5e8eb5509",
            "3022cb1582674370991db390848b6234",
            "d2677943e79e40f89596f2e10ee53a27",
            "a199b7b1705f4eae9da311b9bd69548d",
            "4056af6e32ae4a36b945e8a8d89c1e9f",
            "998375f9d15a47d4a9b74b50bb03c542",
            "7bc103de867e438aaeada2df96907085",
            "e39b700fb2d34b3caf1363b009c935a5",
            "62624aab5805437d819059b64015e348",
            "76955fcf27b9473bbb698e1f61e32e80",
            "26fcb431a6d04bc8b3cefb64b2fb8ac9",
            "99ff7363b3b24fc6be24151a03b9314e"
          ]
        },
        "id": "kf27W8XglmYo",
        "outputId": "39973a4b-d51b-4f1f-f942-909243382ca4"
      },
      "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,
              "version_minor": 0,
              "model_id": "525faeb7359446dab00306e0d803e3b7"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "PnqV5fbGlmYo"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "batch_size = 64\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": 41,
      "metadata": {
        "id": "dxBMdpb3lmYo"
      },
      "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": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "id": "xdxjTWcBlmYp",
        "outputId": "0e7da0ff-cf08-43d0-a6f1-a65e4d1c3b59"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5' max='5' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5/5 00:00]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Perplexity: 22.40\n"
          ]
        }
      ],
      "source": [
        "import math\n",
        "eval_results = trainer.evaluate()\n",
        "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "4H8LJ703lmYp",
        "outputId": "49f3d4d1-7abb-4923-e694-96c4341fc783"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='141' max='141' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [141/141 00:50, 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>2.806200</td>\n",
              "      <td>2.590899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.677800</td>\n",
              "      <td>2.552578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.657100</td>\n",
              "      <td>2.575047</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='10' max='5' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5/5 00:35]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=141, training_loss=2.711846432787307, metrics={'train_runtime': 50.4563, 'train_samples_per_second': 178.372, 'train_steps_per_second': 2.794, 'total_flos': 298262601216000.0, 'train_loss': 2.711846432787307, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "id": "xO1DKC-zlmYp",
        "outputId": "6eed38d4-606e-448e-96e2-41847601bd99"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5' max='5' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5/5 00:00]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Perplexity: 12.60\n"
          ]
        }
      ],
      "source": [
        "eval_results = trainer.evaluate()\n",
        "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131,
          "referenced_widgets": [
            "db486b3339274e788eea48526a09f664",
            "f1b26e183ae148fd887bca74fa50ad88",
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            "32fc430d50c14be294832761fa4bd4f9",
            "c1c3998a07144499a9274d44e4fce80f",
            "385d8d308d4d4aa1a1356068961fb0fd",
            "0e732338dce9437ebe982b718ad72b52",
            "6be962297ea340baa10123a66fc3c8bf",
            "432459736d944f8492e8b66d066e0b8f",
            "9f81304aa7ae418dbb8b3caf167a2a88",
            "ca80307eaaa54b2596d5f4f7c831461a",
            "924c188a3eef4913ad7c4201fb88d163",
            "1f358b3f0994417b8e21e061be5e9071",
            "c1d7c348b8c5467889ac7cdd94e4f260",
            "1ee638dad18b42c3a3691e6036826f3e",
            "2dff9fe23a4342e186058d6477f53b35",
            "e36306cc3e4c45b6a760c40850b63272",
            "4780f36bd5f64ee8a30e2a44df0f85da",
            "53c487dab3bf42edb64b02ba4cb881f4",
            "1f601c158c334927b6510d97fb309472",
            "40ca8e9135c04fd89cb4e93c632a3691",
            "289c204ce5bb40d5abb6a1b760292c6b",
            "c0a6dc44031d4c259d230b9b1ebc95bb",
            "9bd1bbdfec1d4fffbca3bbe686780123",
            "13b2196dc8d14c5987961e5768086265",
            "651737b784b74434a61431eea4c18a2f",
            "8acabf9f7d9543c18438435ee25e8cfe",
            "e9558656758a4735b374b664265c2035",
            "bc2b7f33185f4e4f8f7ff860fe14aa54",
            "b00455c5b3ee4de2882b44345c444719",
            "deda47f48d564bb18e392f50369b96a0",
            "6345d014d3f14c71882d094aa70424bd",
            "e041a97e141c416f8d647b198ff038bb"
          ]
        },
        "id": "Kbsqw88YlmYp",
        "outputId": "41044e33-818a-4bb4-8796-9f3ab93da170"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db486b3339274e788eea48526a09f664"
            }
          },
          "metadata": {}
        },
        {
          "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": "924c188a3eef4913ad7c4201fb88d163"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0a6dc44031d4c259d230b9b1ebc95bb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/loganathanspr/distilbert-base-uncased-finetuned-imdb/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "oLEMuA9ClmYp"
      },
      "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": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5b71a7b77d444185bc5fff094bdf4c49",
            "8191c03dc5b34034a279f4e1193311eb",
            "54493b3d191445d38bab178fd89ac626",
            "1aa179fc67d4424a8f2c3952a20fdfb8",
            "d9d4349ff36940d9a4ee33e2eadd3a9c",
            "3bc8e11cdcf9412888acd0edd4239441",
            "dd9da29e3abc47baa92432b95391f8cf",
            "20107a2ca56541d9b3e26777e9d6677a",
            "1a49446337714103a29a5b530cc89a7c",
            "95ee8650ccff48c4bc94d9ba7177b095",
            "e34650be039b4638b863f3373b52b890"
          ]
        },
        "id": "HQHzvdnAlmYp",
        "outputId": "de50c500-fbcd-4563-ea12-9cc4da4eaea3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/300 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b71a7b77d444185bc5fff094bdf4c49"
            }
          },
          "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": 49,
      "metadata": {
        "id": "4lA1xtvAlmYp"
      },
      "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": 50,
      "metadata": {
        "id": "eRFg7_VflmYp"
      },
      "outputs": [],
      "source": [
        "from torch.optim import AdamW\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "CSWd3mvYlmYw"
      },
      "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": 52,
      "metadata": {
        "id": "FBu6L3halmYw"
      },
      "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": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "3vfV7mwBlmYw",
        "outputId": "c7a7fc70-b277-4369-bb7e-5d7123192065"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'loganathanspr/distilbert-base-uncased-finetuned-imdb-accelerate'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "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",
      "source": [
        "!rm -rf distilbert-base-uncased-finetuned-imdb-accelerate"
      ],
      "metadata": {
        "id": "hYDw9VBTvuLp"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJ2s0lT1lmYw",
        "outputId": "0ab366fe-a012-47bb-8663-8d2f853768b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/loganathanspr/distilbert-base-uncased-finetuned-imdb-accelerate into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/loganathanspr/distilbert-base-uncased-finetuned-imdb-accelerate into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import Repository\n",
        "\n",
        "output_dir = model_name\n",
        "repo = Repository(output_dir, clone_from=repo_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "1450703af7814691b19a2ed71140f192",
            "2489e7f960c242bb8277c76920717381",
            "2badd90706cb4ac8b2b4b852eaffe9e3",
            "355c41c136654685a499998ff1a57012",
            "78e92ae1f2394fa58d2f2598e9e9938b",
            "e726ec9045d143a7b10fcba2ba14372f",
            "3ff813d48a1a46f58a566d32b12f378c",
            "d0c690d0cbb648378767140d3abf1108",
            "44d2396407564bc8806a516da8d47f4f",
            "f7e11317992f49aeb80b083476c23c2f",
            "d1d545f0bca24f7bb94784920289b821"
          ]
        },
        "id": "LiLitpfZlmYw",
        "outputId": "0e1cb4af-d8c3-48f7-fdb6-1279b763f64c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/141 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">>> Epoch 0: Perplexity: 12.259462222940892\n",
            ">>> Epoch 1: Perplexity: 11.87533384548567\n",
            ">>> Epoch 2: Perplexity: 11.77451974236307\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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        "outputId": "31484dcc-f828-450b-ed91-fabdf49e6d22"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/552 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
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              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.23k [00:00<?, ?B/s]"
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              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/82.0 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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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"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "mask_filler = pipeline(\n",
        "    \"fill-mask\", model=\"loganathanspr/distilbert-base-uncased-finetuned-imdb\"\n",
        ")"
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          "output_type": "stream",
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          "text": [
            ">>> this is a great idea.\n",
            ">>> this is a great deal.\n",
            ">>> this is a great adventure.\n",
            ">>> this is a great movie.\n",
            ">>> this is a great film.\n"
          ]
        }
      ],
      "source": [
        "preds = mask_filler(text)\n",
        "\n",
        "for pred in preds:\n",
        "    print(f\">>> {pred['sequence']}\")"
      ]
    },
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