kap10-bert-fine-tuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DJCordhose/buch-machine-learning-notebooks/blob/master/kap10-bert-fine-tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "54aa96ee",
      "metadata": {
        "id": "54aa96ee"
      },
      "source": [
        "# BERT sentiment fine tuning auf der\n",
        "\n",
        "**hierfür brauchen wir eine GPU*\n",
        "\n",
        "* https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/tensorflow/training.ipynb\n",
        "* https://huggingface.co/docs/transformers/training#train-a-tensorflow-model-with-keras\n",
        "* https://huggingface.co/docs/datasets/\n",
        "* https://github.com/huggingface/datasets\n",
        "* https://huggingface.co/datasets/imdb"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1768e85e",
      "metadata": {
        "id": "1768e85e"
      },
      "source": [
        "## Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "g9XJ4FUlhP6g",
        "outputId": "b68b3999-48c0-4e9e-bd98-4b03235d70bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import sys\n",
        "IN_COLAB = 'google.colab' in sys.modules\n",
        "IN_COLAB"
      ],
      "id": "g9XJ4FUlhP6g"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "On3tIaqchP6i",
        "outputId": "c1be6ec2-6555-4bf4-acb3-a47b97cb2b11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "if IN_COLAB:\n",
        "    # https://huggingface.co/transformers/installation.html\n",
        "    # https://huggingface.co/docs/datasets/installation.html\n",
        "    # !pip install -q transformers\n",
        "    !pip install -q datasets"
      ],
      "id": "On3tIaqchP6i"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "cf5f2460",
      "metadata": {
        "id": "cf5f2460"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "89438dee",
      "metadata": {
        "id": "89438dee"
      },
      "outputs": [],
      "source": [
        "import matplotlib as mpl\n",
        "mpl.rcParams['figure.figsize'] = (20, 8)\n",
        "mpl.rcParams['axes.titlesize'] = 24\n",
        "mpl.rcParams['axes.labelsize'] = 20\n",
        "\n",
        "figsize_3d = (20, 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "8rpM6hoffBH7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "8rpM6hoffBH7",
        "outputId": "1882df13-1804-424b-d98f-455fafe09f74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.15.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "5reVkcyPfBv-",
      "metadata": {
        "id": "5reVkcyPfBv-"
      },
      "outputs": [],
      "source": [
        "tf.random.set_seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "3-GkM1kDdFpY",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-GkM1kDdFpY",
        "outputId": "463aaae4-cc29-43c5-9367-d19e76e27a38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu May 16 14:50:53 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   62C    P8              11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "# je mehr Bums desto besser\n",
        "# A100 > V100 > L4 > T4 > CPU\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d76c766",
      "metadata": {
        "id": "1d76c766"
      },
      "source": [
        "## Daten holen und vorbereiten"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "4abd352c",
      "metadata": {
        "id": "4abd352c",
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        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/7.81k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b056372483b4534bf6bf046affdc4e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/21.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64aaee5e1d374a90a1fc91cac9bb083e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/20.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a93033f9e3b54c9da134ee089504d66d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/42.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28835932e0f744d084f5eca5756b15d5"
            }
          },
          "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": "f93ec78d64b24530a14582afdf4cc3e3"
            }
          },
          "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": "151139564f414271ab759a00715036a1"
            }
          },
          "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": "c4f678e716504feabd7dd7c122ab2af4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"imdb\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "b64bae96",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b64bae96",
        "outputId": "2daa5036-b968-4fa5-e7d3-289d472193af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    unsupervised: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 50000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "raw_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "85NiJ7LA3sS3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "85NiJ7LA3sS3",
        "outputId": "31be6bf0-521a-4ae9-fab3-378eae0b6048"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'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 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, 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.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "example_idx = 0\n",
        "raw_datasets[\"train\"]['text'][example_idx]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "r9an6Ijh36XY",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r9an6Ijh36XY",
        "outputId": "e515df8e-a5ad-45ca-dcda-8738b85a7972"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# 0: neg, 1: pos\n",
        "raw_datasets[\"train\"]['label'][example_idx]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "c45b1af2",
      "metadata": {
        "id": "c45b1af2",
        "outputId": "8b8bc9b7-3d9d-4203-bb2c-0c3a66ceb3b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201,
          "referenced_widgets": [
            "299a8366f168400e8043e31a47f0d9c1",
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            "ee0d664aabe04ad6b9938be5455dac1a",
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            "268fbfad7597487595a21b7ddb227014",
            "27b8203853144508a75e1e9e064f2b2c"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "299a8366f168400e8043e31a47f0d9c1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e073d8debb8489cb85cd5f18c284cd0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c330185ee0842c7bee51602c0c9eacb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b69bce2d20064f3fafb6108ffd0892d0"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "06bfb8ef",
      "metadata": {
        "id": "06bfb8ef",
        "outputId": "5d893897-d1ec-473b-abd3-44cf789e43d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "a1b21ec0d0794d77b5a2c062968b1b5b",
            "8e4053c8a7094a9dbc99fa08b2cc39ae",
            "650a1f449e2745d785661fcbb7c269cc",
            "be4f8397db2545dd8b62b9cbd670b6b5",
            "d155927159ff40ceb4d849f5f8b685d7",
            "b5906b902e7d412f88fb81a50952842c",
            "6d63180e61d04a168d828d8a6de1361a",
            "e1d48e38f24b4859a9ea4b3579c888ff",
            "dfee0941b89042958319b91e6122fe09",
            "1aae14fb6a3348a1bce4462cb9f666a7",
            "7cdd67317ccb4c27bd5386cfdafc3529",
            "9d0b232a20134a6b85cd61697361134d",
            "2bc9e2d496b24277b6906b396c2a2818",
            "29fc2b0267eb4e04b6ad35d64b2e0d94",
            "b8ebda4c665149a495eccf03efa622bd",
            "3711cf26928e4dac971bc690da467b60",
            "94c3b184e8be4abfadd59cea3c470c75",
            "78c06aac288e417fa9fb7f7c10916af2",
            "9b0983e93a3d452fb0d9260de35df4bd",
            "36e4533555ca436281811512dd223836",
            "08ef0e87ac174acd85f22625eb6cc925",
            "e9e0537b15484b5cb447858d73c0e97e"
          ]
        }
      },
      "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": "a1b21ec0d0794d77b5a2c062968b1b5b"
            }
          },
          "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": "9d0b232a20134a6b85cd61697361134d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "del raw_datasets[\"unsupervised\"]\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train_dataset = tokenized_datasets[\"train\"]\n",
        "# eval_dataset = tokenized_datasets[\"test\"]"
      ],
      "metadata": {
        "id": "KgnC6waArg3F"
      },
      "id": "KgnC6waArg3F",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "6e168e1b",
      "metadata": {
        "id": "6e168e1b"
      },
      "source": [
        "## Fine Tuning des vortrainierten Modells"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "0496a154",
      "metadata": {
        "id": "0496a154"
      },
      "outputs": [],
      "source": [
        "small_size = 1_000\n",
        "train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42).select(range(small_size))\n",
        "eval_dataset = tokenized_datasets[\"test\"].shuffle(seed=42).select(range(small_size))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "287d2c69",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444,
          "referenced_widgets": [
            "721f993a24774843a4988e0a22b6a258",
            "4c6f94f8b236405982ae31fb94cd41cb",
            "eff3004377074bd8be3f00522e69fd6a",
            "d70a29b2b8ba4c41b8c9597746230a46",
            "dc046f25bc7943038ccdebb7aef91db4",
            "28066bc91a034efba9ecef0f4842f7f6",
            "a19489e13739466e8b7047bbdc027fc2",
            "b1abe62e2ff34c2a97eeab7d3f5b09d3",
            "03a3545b88584ee3a366b6723d8368c3",
            "0a842ee7bea14f1aae1bddc9c55a4491",
            "28216c4b581b4051af0baa700630453d"
          ]
        },
        "id": "287d2c69",
        "outputId": "971f2f56-275a-483e-864b-7cf344fe93ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "721f993a24774843a4988e0a22b6a258"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some weights or buffers of the TF 2.0 model TFBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"tf_bert_for_sequence_classification\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bert (TFBertMainLayer)      multiple                  108310272 \n",
            "                                                                 \n",
            " dropout_37 (Dropout)        multiple                  0 (unused)\n",
            "                                                                 \n",
            " classifier (Dense)          multiple                  1538      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 108311810 (413.18 MB)\n",
            "Trainable params: 108311810 (413.18 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from transformers import TFAutoModelForSequenceClassification\n",
        "\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\")\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "32ee1b96",
      "metadata": {
        "id": "32ee1b96"
      },
      "outputs": [],
      "source": [
        "# 1 to 3 minutes per epoch on decent GPU (e.g. V100 1min, T4 3min)\n",
        "# don't train for too long, model will overfit\n",
        "EPOCHS = 5\n",
        "# reduce batch size when you encoder OOM error\n",
        "# note, model has more than 100M parameters\n",
        "# batch size 8 doable on 16GB\n",
        "BATCH_SIZE = 8\n",
        "# don't forget, we are fine tuning, increasing the LR might keep the model from even training\n",
        "# LR = 3e-5\n",
        "LR = 1e-5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "ba79af8f",
      "metadata": {
        "id": "ba79af8f"
      },
      "outputs": [],
      "source": [
        "from transformers import DefaultDataCollator\n",
        "\n",
        "data_collator = DefaultDataCollator(return_tensors=\"tf\")\n",
        "\n",
        "tf_train_dataset = train_dataset.to_tf_dataset(\n",
        "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
        "    label_cols=\"label\",\n",
        "    shuffle=True,\n",
        "    collate_fn=data_collator,\n",
        "    batch_size=BATCH_SIZE,\n",
        ")\n",
        "\n",
        "tf_validation_dataset = eval_dataset.to_tf_dataset(\n",
        "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
        "    label_cols=\"label\",\n",
        "    shuffle=False,\n",
        "    collate_fn=data_collator,\n",
        "    batch_size=BATCH_SIZE,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "db75e13d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "db75e13d",
        "outputId": "8b7fce9b-28d4-43b6-f0b1-e336ea9e9fd2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:AutoGraph could not transform <function infer_framework at 0x7ca45f17e3b0> and will run it as-is.\n",
            "Cause: for/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING: AutoGraph could not transform <function infer_framework at 0x7ca45f17e3b0> and will run it as-is.\n",
            "Cause: for/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "125/125 [==============================] - 307s 2s/step - loss: 0.5823 - sparse_categorical_accuracy: 0.6640 - val_loss: 0.3524 - val_sparse_categorical_accuracy: 0.8410\n",
            "Epoch 2/5\n",
            "125/125 [==============================] - 170s 1s/step - loss: 0.2640 - sparse_categorical_accuracy: 0.8980 - val_loss: 0.3567 - val_sparse_categorical_accuracy: 0.8680\n",
            "Epoch 3/5\n",
            "125/125 [==============================] - 207s 2s/step - loss: 0.1108 - sparse_categorical_accuracy: 0.9620 - val_loss: 0.4128 - val_sparse_categorical_accuracy: 0.8810\n",
            "Epoch 4/5\n",
            "125/125 [==============================] - 170s 1s/step - loss: 0.0526 - sparse_categorical_accuracy: 0.9870 - val_loss: 0.4780 - val_sparse_categorical_accuracy: 0.8760\n",
            "Epoch 5/5\n",
            "125/125 [==============================] - 207s 2s/step - loss: 0.0369 - sparse_categorical_accuracy: 0.9870 - val_loss: 0.4653 - val_sparse_categorical_accuracy: 0.8720\n",
            "CPU times: user 8min 26s, sys: 1min 52s, total: 10min 18s\n",
            "Wall time: 18min 35s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "model.compile(\n",
        "   optimizer=tf.keras.optimizers.Adam(learning_rate=LR),\n",
        "    # You don’t have to pass a loss argument to your models when you compile() them!\n",
        "    # Hugging Face models automatically choose a loss that is appropriate for their task and model architecture if this argument is left blank.\n",
        "    # You can always override this by specifying a loss yourself if you want to!\n",
        "    # loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "    metrics=tf.metrics.SparseCategoricalAccuracy()\n",
        ")\n",
        "\n",
        "history = model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=EPOCHS)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "048ebf4a",
      "metadata": {
        "id": "048ebf4a"
      },
      "source": [
        "## Trainiertes Modell evaluieren"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "38939e4d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "38939e4d",
        "outputId": "77326b1f-a6f8-4a52-d557-55f149c923d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "125/125 [==============================] - 46s 368ms/step - loss: 0.0139 - sparse_categorical_accuracy: 0.9970\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.013858703896403313, 0.996999979019165]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "model.evaluate(tf_train_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "8d9686f0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8d9686f0",
        "outputId": "e8f4ded7-6d3d-4e0d-97b2-9e0308a27681"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "125/125 [==============================] - 43s 347ms/step - loss: 0.4653 - sparse_categorical_accuracy: 0.8720\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4653184711933136, 0.871999979019165]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# training data set too small to not overfit on it\n",
        "model.evaluate(tf_validation_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "2eb8316c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "id": "2eb8316c",
        "outputId": "c5dfb3b0-9e02-434b-fcb7-c661f0e32230"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# plt.yscale('log')\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.title('Loss over time')\n",
        "\n",
        "plt.plot(history.history['loss']);\n",
        "plt.plot(history.history['val_loss']);\n",
        "\n",
        "plt.legend(['Training', 'Validation']);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "23ec4c5d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "23ec4c5d",
        "outputId": "3f72f284-c2a6-4594-ca10-9f5f18dd436d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# plt.yscale('log')\n",
        "plt.ylabel(\"accuracy\")\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.title('Accuracy over time')\n",
        "\n",
        "plt.plot(history.history['sparse_categorical_accuracy']);\n",
        "plt.plot(history.history['val_sparse_categorical_accuracy']);\n",
        "\n",
        "plt.legend(['Training', 'Validation']);\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67c33a4b",
      "metadata": {
        "id": "67c33a4b"
      },
      "source": [
        "## Anekdotiches ausprobieren\n",
        "\n",
        "Der Datensatz ist Englisch, also nur englische Beispiele sinnvoll\n",
        "\n",
        "https://huggingface.co/datasets/imdb\n",
        "* neg: 0\n",
        "* pos: 1\n",
        "\n",
        "https://huggingface.co/transformers/task_summary.html#sequence-classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "39a17765",
      "metadata": {
        "id": "39a17765"
      },
      "outputs": [],
      "source": [
        "movie1 = 'Two small cats seek and find adventures. Nice if you like cute movies.'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "f11ca67f",
      "metadata": {
        "id": "f11ca67f"
      },
      "outputs": [],
      "source": [
        "movie2 = 'Zombies attack a kindergarden. Splatter!'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "e23cc151",
      "metadata": {
        "id": "e23cc151"
      },
      "outputs": [],
      "source": [
        "movies = [movie1, movie2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "f0dcbc3e",
      "metadata": {
        "id": "f0dcbc3e"
      },
      "outputs": [],
      "source": [
        "inputs = tokenizer(movies, padding=\"max_length\", truncation=True, return_tensors=\"tf\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "277c07eb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "277c07eb",
        "outputId": "3702a76e-f581-48d1-fdc1-ac115f5d60f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.04970243, 0.95029753], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# this seems to be a good movie\n",
        "tf.nn.softmax(model(inputs)[0])[0].numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "f060138c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f060138c",
        "outputId": "4ba9ab8f-7cd1-44fb-aa63-cdef4c1b79a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.70871097, 0.291289  ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# this one not so much\n",
        "tf.nn.softmax(model(inputs)[0])[1].numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "L53LwkL_iY4y",
      "metadata": {
        "id": "L53LwkL_iY4y"
      },
      "outputs": [],
      "source": []
    }
  ],
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