kap10-bert-tasks.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-tasks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "912d7d82",
      "metadata": {
        "id": "912d7d82"
      },
      "source": [
        "# BERT Transformer Tasks\n",
        "\n",
        "* Mehr Beispiele: https://huggingface.co/transformers/task_summary.html\n",
        "\n",
        "## Der Transformer Zoo\n",
        "![Encoder-Decoder](https://raw.githubusercontent.com/DJCordhose/ml-resources/main/img/transformers/transformer-encoder-decoder.png)\n",
        "\n",
        "## BERT - das typische Encoder-Modell\n",
        "\n",
        "Kleinere Modelle, können typischerweise auch auf CPU ausgeführt werden\n",
        "\n",
        "BERT verstehen: https://pair.withgoogle.com/explorables/fill-in-the-blank/"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VqNWQz9Yb76e",
        "outputId": "a869c304-2057-4a88-c2ab-d529f1f1fb43"
      },
      "id": "VqNWQz9Yb76e",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Apr 26 17:24:22 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  NVIDIA L4                      Off | 00000000:00:03.0 Off |                    0 |\n",
            "| N/A   48C    P0              28W /  72W |   2253MiB / 23034MiB |      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",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tSnfbgENVV2l",
        "outputId": "4d67b22c-d48c-40ee-8a6f-b2fc921870d9",
        "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": "tSnfbgENVV2l"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QoU2ej9JVV2m"
      },
      "outputs": [],
      "source": [
        "# if IN_COLAB:\n",
        "    # https://huggingface.co/transformers/installation.html\n",
        "    # !pip install -q transformers"
      ],
      "id": "QoU2ej9JVV2m"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "BhKtZIx7BC8S",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "BhKtZIx7BC8S",
        "outputId": "e065485b-054d-4115-8ffa-82358f554f98"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.15.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "UKkytQc2Al0y",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "UKkytQc2Al0y",
        "outputId": "6a48455e-e3c5-46a0-f0ff-7eb95b7b9ec0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'4.40.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "import transformers\n",
        "transformers.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "de2FNBanVV2n"
      },
      "source": [
        "# Transformer Pipelines\n",
        "\n",
        "### Pipelines are made of:\n",
        "\n",
        "- A [tokenizer](tokenizer) in charge of mapping raw textual input to token.\n",
        "- A [model](model) to make predictions from the inputs.\n",
        "- Some (optional) post processing for enhancing model's output.\n",
        "\n",
        "### Available Tasks (November 2023)\n",
        "- `\"audio-classification\"`: will return a [`AudioClassificationPipeline`].\n",
        "- `\"automatic-speech-recognition\"`: will return a [`AutomaticSpeechRecognitionPipeline`].\n",
        "- `\"conversational\"`: will return a [`ConversationalPipeline`].\n",
        "- `\"depth-estimation\"`: will return a [`DepthEstimationPipeline`].\n",
        "- `\"document-question-answering\"`: will return a [`DocumentQuestionAnsweringPipeline`].\n",
        "- `\"feature-extraction\"`: will return a [`FeatureExtractionPipeline`].\n",
        "- `\"fill-mask\"`: will return a [`FillMaskPipeline`]:.\n",
        "- `\"image-classification\"`: will return a [`ImageClassificationPipeline`].\n",
        "- `\"image-segmentation\"`: will return a [`ImageSegmentationPipeline`].\n",
        "- `\"image-to-image\"`: will return a [`ImageToImagePipeline`].\n",
        "- `\"image-to-text\"`: will return a [`ImageToTextPipeline`].\n",
        "- `\"mask-generation\"`: will return a [`MaskGenerationPipeline`].\n",
        "- `\"object-detection\"`: will return a [`ObjectDetectionPipeline`].\n",
        "- `\"question-answering\"`: will return a [`QuestionAnsweringPipeline`].\n",
        "- `\"summarization\"`: will return a [`SummarizationPipeline`].\n",
        "- `\"table-question-answering\"`: will return a [`TableQuestionAnsweringPipeline`].\n",
        "- `\"text2text-generation\"`: will return a [`Text2TextGenerationPipeline`].\n",
        "- `\"text-classification\"` (alias `\"sentiment-analysis\"` available): will return a [`TextClassificationPipeline`].\n",
        "- `\"text-generation\"`: will return a [`TextGenerationPipeline`]:.\n",
        "- `\"text-to-audio\"` (alias `\"text-to-speech\"` available): will return a [`TextToAudioPipeline`]:.\n",
        "- `\"token-classification\"` (alias `\"ner\"` available): will return a [`TokenClassificationPipeline`].\n",
        "- `\"translation\"`: will return a [`TranslationPipeline`].\n",
        "- `\"translation_xx_to_yy\"`: will return a [`TranslationPipeline`].\n",
        "- `\"video-classification\"`: will return a [`VideoClassificationPipeline`].\n",
        "- `\"visual-question-answering\"`: will return a [`VisualQuestionAnsweringPipeline`].\n",
        "- `\"zero-shot-classification\"`: will return a [`ZeroShotClassificationPipeline`].\n",
        "- `\"zero-shot-image-classification\"`: will return a [`ZeroShotImageClassificationPipeline`].\n",
        "- `\"zero-shot-audio-classification\"`: will return a [`ZeroShotAudioClassificationPipeline`].\n",
        "- `\"zero-shot-object-detection\"`: will return a [`ZeroShotObjectDetectionPipeline`].\n"
      ],
      "id": "de2FNBanVV2n"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "WAmSIKEIEFiQ",
      "metadata": {
        "id": "WAmSIKEIEFiQ"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "78b74e90",
      "metadata": {
        "id": "78b74e90"
      },
      "outputs": [],
      "source": [
        "# shows all possible tasks\n",
        "# pipeline?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "tI1-bxeAGDdf",
      "metadata": {
        "id": "tI1-bxeAGDdf"
      },
      "source": [
        "## Sentiment Analysis\n",
        "\n",
        "model trained on the glue dataset: https://huggingface.co/datasets/glue\n"
      ]
    },
    {
      "cell_type": "code",
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        "id": "87b53c5b",
        "outputId": "5595908b-5123-46ff-ecdc-e3f2402f5ded"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
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        {
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            "text/plain": [
              "'distilbert/distilbert-base-uncased-finetuned-sst-2-english'"
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          "metadata": {},
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      ],
      "source": [
        "classifier = pipeline(task=\"sentiment-analysis\")\n",
        "classifier.model.name_or_path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Gp05bH77D5Py",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gp05bH77D5Py",
        "outputId": "51fc227f-ee4b-43ca-d82e-2894af8a1ef7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'NEGATIVE', 'score': 0.9991129040718079}]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "classifier(\"I hate you\")"
      ]
    },
    {
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      "execution_count": null,
      "id": "_068zScPFOaV",
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        },
        "id": "_068zScPFOaV",
        "outputId": "2a4151e4-f664-4d58-c28b-7d68080c731c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9998656511306763}]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "classifier(\"I love you\")"
      ]
    },
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "759b5fc767a04b248ed4b334d1141bf8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aac3ab1f744f4663b15cdb1ee0676826"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# https://huggingface.co/models?pipeline_tag=text-classification&language=de&sort=trending\n",
        "classifier = pipeline(task=\"sentiment-analysis\", model=\"oliverguhr/german-sentiment-bert\")\n"
      ],
      "id": "-ehNz8kOVV2q"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bZogY3OuVV2q",
        "outputId": "a2e15412-4bd7-426b-b90a-584c489a509d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'positive', 'score': 0.9846151471138}]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "classifier(\"Ich liebe dich\")"
      ],
      "id": "bZogY3OuVV2q"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vPCCUeybVV2q",
        "outputId": "10cdd19b-d48d-4b81-db18-8d5e7cecc257",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'positive', 'score': 0.9546653032302856}]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "classifier(\"Ick liebe dir\")"
      ],
      "id": "vPCCUeybVV2q"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UUBPTquRVV2q",
        "outputId": "3564f189-adb9-4e4c-9033-2d2333a3d943",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'positive', 'score': 0.9964079260826111}]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "classifier(\"Ich bin mit diesem Artikel sehr zufrieden\")"
      ],
      "id": "UUBPTquRVV2q"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S-_-SnUUVV2r",
        "outputId": "e09279c0-5759-4a96-8e1f-d7a187974c70",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'positive', 'score': 0.9832236766815186}]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "classifier(\"Ich bin mit diesem Artikel nicht unzufrieden\")"
      ],
      "id": "S-_-SnUUVV2r"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dn-lX7XsVV2r",
        "outputId": "27d1a9e7-ca52-486c-f6d1-64ff93fe0865",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'positive', 'score': 0.978872537612915}]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "classifier(\"Ich bin mit diesem Artikel zufrieden (von wegen)\")"
      ],
      "id": "Dn-lX7XsVV2r"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5M5jvIxkVV2r",
        "outputId": "374e4f70-cb15-4c46-e676-73f12af11a32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'negative', 'score': 0.9946463704109192}]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "classifier(\"Ich mag dich nicht\")"
      ],
      "id": "5M5jvIxkVV2r"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Oo-6PK6VV2r"
      },
      "source": [
        "## Extractive Question Answering\n",
        "\n",
        "* model: https://huggingface.co/distilbert-base-cased-distilled-squad\n",
        "* dataset: The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text.\n",
        "  * https://paperswithcode.com/dataset/squad\n",
        "  * https://huggingface.co/datasets/squad\n",
        "  * https://rajpurkar.github.io/SQuAD-explorer/"
      ],
      "id": "-Oo-6PK6VV2r"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1KajcK35VV2r",
        "outputId": "1e972b6f-968d-4a1d-9c67-f1905802c383",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230,
          "referenced_widgets": [
            "37420c3188914c3bae5546aa1691d972",
            "f77a9f881f9740769f93dc38971615c4",
            "952c0d5b314f46a8863198a080c41cda",
            "3c43f29cfa584cefb3ed361b5f5f14a6",
            "06b7cff15b3e4de7a0ced04031d306d1",
            "ae792ba8214a4ad18cf4d003b9bdcef9",
            "78015d57394f4d6d8c680e212b73cdea",
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            "0d00d43d3b31485193fc6e3ea92f1ee3",
            "5a352526ab8d4582870c13b08cdc7e0a",
            "1d7104c4bc624a7c91f041a8fc12a8d5",
            "dbc57f0def194bc3be917fb92368dc43",
            "0b01275b76664c1f997fa0066eee4e33",
            "ab85e11537ca47a98a01dc403ef10a68",
            "7b5c031b5bae4b04893ddf7896775d10",
            "91ab378234d941efa1f22afb98f9c9ae",
            "6f49bf52834440459e6d8d9301eacd6d",
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            "98ab8669f4794a30bd94f643bc8a7c2e",
            "da50ef585a8a48f68227c42dbc678aae",
            "8cab364a68574a149acc6fd22f03410f",
            "f2133a45b5b54cfd84719fdc7e8d8418",
            "a5532f0a729e44afbebfa17d1a9df619",
            "6df3b709ccee4d058bb9ec1b4c362fd2",
            "5cefa1b5b6df45bfb0a0c64fe751c0f2",
            "8e78bb4b19514a8b9bfe92b1c211b707",
            "88642bb18ab940f98828dc33f757cc69",
            "06cceaecfd9b464b8851158863eca931",
            "05a1bb0c363143d8ac595117e8a4b455",
            "80a9b8b240094414bb902321b3ce2516",
            "2bbba2fcbc604351bb8963d1f4d2d125",
            "ea5eb3a922614f1b864762cb4789e907",
            "96ab90ea74084e558ef556685e759e8e",
            "e29d3223c5224708ba136ada1646325f",
            "2fc4c570a5604f0aa55b0f92ecab9b25",
            "7ecc36b715944e1bbb96c83a9d7304d3",
            "88afc98bc8564061887ade037f2bd0cb",
            "b830abb0c1254dfb82805b4b83b4879c",
            "9d0dd4cd7ab148e7bc2715fabcbba275",
            "162d7ecf95774326b5657c714eedd4fa",
            "a9869ada39f24075afc26ae439df2ed3",
            "f3773a9b52064d66aca04c79a4d57779",
            "7cfb67de2336416cb01d0a99550b003b",
            "d8e9a16b6021490ea4e45720ff20eb66",
            "798b62ddd7374373850ebbccd068dcd2",
            "d71a53978b7647fba36b390307b24358",
            "19024e56146645789808f58a1167fbcc",
            "340f0ae107eb41fa810023068c95002f",
            "bc115fb16d3746f89932f6cb811cb66a",
            "6a53fffaa4f842e5a149d7f4e782538e",
            "16e97630eb6b4bed969d5d40a3c8ba9d"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-cased-distilled-squad and revision 626af31 (https://huggingface.co/distilbert/distilbert-base-cased-distilled-squad).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/473 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "37420c3188914c3bae5546aa1691d972"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/261M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61bc488d5216448e98cc6afbb0571a78"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98ab8669f4794a30bd94f643bc8a7c2e"
            }
          },
          "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": "80a9b8b240094414bb902321b3ce2516"
            }
          },
          "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": "a9869ada39f24075afc26ae439df2ed3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'distilbert/distilbert-base-cased-distilled-squad'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "question_answerer = pipeline(\"question-answering\")\n",
        "question_answerer.model.name_or_path"
      ],
      "id": "1KajcK35VV2r"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VczVLkR8VV2r"
      },
      "outputs": [],
      "source": [
        "context = r\"\"\"\n",
        "Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a\n",
        "question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune\n",
        "a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.\n",
        "\"\"\""
      ],
      "id": "VczVLkR8VV2r"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jbHdWPhNVV2r",
        "outputId": "9491cb77-c849-48f4-87f3-f2b3634bf834",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer: 'the task of extracting an answer from a text given a question', score: 0.6177, start: 34, end: 95\n"
          ]
        }
      ],
      "source": [
        "result = question_answerer(question=\"What is extractive question answering?\", context=context)\n",
        "print(f\"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")"
      ],
      "id": "jbHdWPhNVV2r"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KFwcLYA0VV2r",
        "outputId": "214d14d9-4bc4-487d-c4ee-9f45a3a56b96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer: 'SQuAD dataset', score: 0.5152, start: 147, end: 160\n"
          ]
        }
      ],
      "source": [
        "result = question_answerer(question=\"What is a good example of a question answering dataset?\", context=context)\n",
        "print(f\"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")"
      ],
      "id": "KFwcLYA0VV2r"
    },
    {
      "cell_type": "markdown",
      "id": "ZEC-kpdLGLhk",
      "metadata": {
        "id": "ZEC-kpdLGLhk"
      },
      "source": [
        "# Ein bisschen mehr low level - haben zwei Sequenzen dieselbe Bedeutung?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rzZ1F-13VV2r"
      },
      "outputs": [],
      "source": [
        "model_name = 'bert-base-cased-finetuned-mrpc'"
      ],
      "id": "rzZ1F-13VV2r"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tk3J3W5vVV2s"
      },
      "source": [
        "## Tokenizer"
      ],
      "id": "Tk3J3W5vVV2s"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WrFKR_TPVV2s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "b7a3c50012ca4ee7baa8e51941d8f00b",
            "936af14719014de4856901748d6651b0",
            "561b5d30dded4f89b057ad94527dd320",
            "b3772e5f433a476eb9913ac4f27463d9",
            "e96f6db251d64cd3a0226fc373e2469e",
            "43b8f07be1cf4d53bdad85fd2f67f298",
            "89a90c71a1e644cb8ff4fd72e7b18177",
            "16412e6d0a724325831f6fbc17e3e909",
            "460faded09034b1aa09bbb76310bcda2",
            "99d89799091d406bba5a3cc4d48dddf9",
            "fda8b11b96eb4ee3b13e1046f14ad19f",
            "e59759e7dd31415196cc502be7bf18f5",
            "70c8b78e044146d2879319a727f90f7d",
            "114b02938138441a82b92036c3a2c1e5",
            "1c43dc06a583462380fc17fdc059329f",
            "fc54ce66c0bb4ce388942f6db0835f5b",
            "728128f462274b2293ea0804256b5138",
            "4aa1a73272fe40148c4448a4f1279cd1",
            "5d09a30c8b624605b8ab6e97fd1f80f2",
            "69a60f83ef964400a593f7e8a68c05c7",
            "87bd07a850f946f891a54492ab94d5d8",
            "a0bc60948e114c60afc4d8218ad3b1a6",
            "13a6e56a597f4beb90fa949ba8266991",
            "447735a4a7ba46c2be9170fe1758d8c6",
            "a1ac4d14aa24443cab7cc74ab5727d93",
            "14d6c7b1309d4f98a39851be5f7c3b29",
            "e07e72b8a22d41a4908617b5436950ba",
            "91d673b9b71346ccac5a05b7ce7a5ad9",
            "7d4da131e2104c018b9a718197606001",
            "27a8f8ad0473454aa1381097d284b070",
            "04d2e0e8b5074e138662e83f3f3836af",
            "0e9e60b7768041dba46e2be54cca0844",
            "891f08516b9c4374a1b04548bddfe589",
            "d6123204dce04548a16553828eb611d0",
            "6b4c7a59fd2f4601b6c1052140ce04bf",
            "73c0d1014a954bb2bf11a4bab94859e4",
            "cc6e39ddedfb4178934d79752f45247e",
            "ecb12038bc25476f8d78cc3bf1047adf",
            "f9d2f0f69bbb40eeac826015aa32d329",
            "e841354bebf04d119c8f5cb7ee3c4525",
            "ea9466824dc843fb8f81ce698c4d3e1c",
            "655f316b7e954bd99f97ad13af12061f",
            "4e1e84ce59b7434c866b6a62b996786e",
            "daf426b87fb942eaaa9ab5b5cc8e7933"
          ]
        },
        "outputId": "dacea960-580a-4cbe-cf7e-d8432185b83c"
      },
      "outputs": [
        {
          "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": "b7a3c50012ca4ee7baa8e51941d8f00b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/433 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e59759e7dd31415196cc502be7bf18f5"
            }
          },
          "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": "13a6e56a597f4beb90fa949ba8266991"
            }
          },
          "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": "d6123204dce04548a16553828eb611d0"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "id": "WrFKR_TPVV2s"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sa8sS4IWVV2s",
        "outputId": "d1674f1b-867b-456e-cf38-dc0e2e6a6a84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 6373, 102], 'token_type_ids': [0, 0, 0], 'attention_mask': [1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "tokens = tokenizer(\"Short\")\n",
        "tokens"
      ],
      "id": "Sa8sS4IWVV2s"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GI92PwpUVV2s",
        "outputId": "411701ad-5618-4894-d30a-b50b48c5cd26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "101 = [CLS]\n",
            "6373 = Short\n",
            "102 = [SEP]\n"
          ]
        }
      ],
      "source": [
        "# whole words can become a single token, CLS and SEP tokens are added\n",
        "# CLS means classification, SEP means separation\n",
        "for token in tokens.input_ids:\n",
        "    print(f\"{token} = {tokenizer.decode([token])}\")"
      ],
      "id": "GI92PwpUVV2s"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OtzCkOifVV2s",
        "outputId": "57bb7b19-da3b-4838-8533-e7fc2cbe715a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "101 = [CLS]\n",
            "1148 = first\n",
            "102 = [SEP]\n",
            "1248 = second\n",
            "102 = [SEP]\n"
          ]
        }
      ],
      "source": [
        "# a sequence gets separated by [SEP] token\n",
        "tokens = tokenizer(\"first\", \"second\")\n",
        "for token in tokens.input_ids:\n",
        "    print(f\"{token} = {tokenizer.decode([token])}\")"
      ],
      "id": "OtzCkOifVV2s"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RS_7tOqgVV2s"
      },
      "source": [
        "## Model\n",
        "\n",
        "https://huggingface.co/bert-base-cased-finetuned-mrpc?library=true\n",
        "\n",
        "### Trained on\n",
        "\n",
        "Microsoft Research Paraphrase Corpus (MRPC) is a corpus consists of 5,801 sentence pairs collected from newswire articles. Each pair is labelled if it is a paraphrase or not by human annotators.\n",
        "\n",
        "https://paperswithcode.com/dataset/mrpc\n",
        "\n"
      ],
      "id": "RS_7tOqgVV2s"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8S40rYNeVV2s",
        "outputId": "90c7c4e9-c4eb-4768-aab3-cccaa486191f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "98149cd23ed64a2c8b7f31dd92c9e1a4",
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            "d3a776a431c44adf9e4716d151ea5778",
            "d3a48fa513604d61823d5c9c17334eee",
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            "b1bf8662a19b440e98cfdd69cdb8eddc",
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            "a2997c51cf0144ce9c30245ed8e9e360",
            "00d95aeff23f4528a6a3a58eeeed8bd4"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/433M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98149cd23ed64a2c8b7f31dd92c9e1a4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "All the weights of TFBertForSequenceClassification were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "from transformers import TFAutoModelForSequenceClassification\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(model_name)"
      ],
      "id": "8S40rYNeVV2s"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EivFOcnfVV2s",
        "outputId": "97c14dfe-f46a-481c-ad48-a8cb8f19c422",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of being a paraphrase: 90.5%\n"
          ]
        }
      ],
      "source": [
        "paraphrase = tokenizer(\n",
        "    \"The company HuggingFace is based in New York City\",\n",
        "    \"HuggingFace's headquarters are situated in Manhattan\",\n",
        "    return_tensors=\"tf\")\n",
        "paraphrase_classification_logits = model(paraphrase)[0]\n",
        "paraphrase_results = tf.nn.softmax(paraphrase_classification_logits, axis=1).numpy()[0]\n",
        "print(f\"Probability of being a paraphrase: {paraphrase_results[1]*100:.1f}%\")\n"
      ],
      "id": "EivFOcnfVV2s"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "33SkD910VV2s",
        "outputId": "e63ec63f-55db-4d62-c3ee-1de1169c23f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of being a paraphrase: 6.0%\n"
          ]
        }
      ],
      "source": [
        "paraphrase = tokenizer(\n",
        "    \"The company HuggingFace is based in New York City\",\n",
        "    \"Apples are especially bad for your health\",\n",
        "    return_tensors=\"tf\")\n",
        "paraphrase_classification_logits = model(paraphrase)[0]\n",
        "paraphrase_results = tf.nn.softmax(paraphrase_classification_logits, axis=1).numpy()[0]\n",
        "print(f\"Probability of being a paraphrase: {paraphrase_results[1]*100:.1f}%\")\n"
      ],
      "id": "33SkD910VV2s"
    },
    {
      "cell_type": "markdown",
      "id": "rTCXwNcXBxnh",
      "metadata": {
        "id": "rTCXwNcXBxnh"
      },
      "source": [
        "# Deutsche Modelle\n",
        "\n",
        "Deutsche einfache Transformer-Modelle sind leider etwas rar\n",
        "\n",
        "Modelle\n",
        "* Base: https://huggingface.co/bert-base-german-cased\n",
        "* Squad: https://huggingface.co/deutsche-telekom/bert-multi-english-german-squad2\n",
        "\n",
        "Data Sets\n",
        "* https://tblock.github.io/10kGNAD/\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f5d0ba34",
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        "colab": {
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.96M [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/714M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f58fca7b17964aadb6626821dbb4c33c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFBertForMaskedLM.\n",
            "\n",
            "All the weights of TFBertForMaskedLM were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForMaskedLM for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "from transformers import TFAutoModelForMaskedLM\n",
        "\n",
        "# model_name = \"distilbert-base-cased\"\n",
        "# model_name = \"bert-base-german-cased\"\n",
        "# model_name = \"bert-base-german-dbmdz-cased\"  # works with Pytorch only\n",
        "model_name = \"bert-base-multilingual-cased\"\n",
        "\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = TFAutoModelForMaskedLM.from_pretrained(model_name)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KIfcplX1VV2t",
        "outputId": "361e1128-e750-473b-94cb-4e46327e96c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Deutschland ist ein tolles Land. Als Bürger hast du das Recht, [MASK] zu tun.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# sequence = f\"Distilled models are smaller than the models they mimic. Using them instead of the large versions would help {tokenizer.mask_token} our carbon footprint.\"\n",
        "# sequence = f\"Deutschland ist ein tolles Land. Als Bürger hast du das Recht, {tokenizer.mask_token} zu tun was nicht gegen das Gesetz ist.\"\n",
        "sequence = f\"Deutschland ist ein tolles Land. Als Bürger hast du das Recht, {tokenizer.mask_token} zu tun.\"\n",
        "sequence"
      ],
      "id": "KIfcplX1VV2t"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xFvN8PxWVV2t",
        "outputId": "21ef3b8a-5221-471b-a5d4-3eb4f74ef59c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "101 = [CLS]\n",
            "13011 = Deutschland\n",
            "10298 = ist\n",
            "10290 = ein\n",
            "81754 = toll\n",
            "10171 = ##es\n",
            "12001 = Land\n",
            "119 = .\n",
            "11966 = Als\n",
            "47473 = Bürger\n",
            "10393 = has\n",
            "10123 = ##t\n",
            "10168 = du\n",
            "10242 = das\n",
            "31041 = Recht\n",
            "117 = ,\n",
            "103 = [MASK]\n",
            "10304 = zu\n",
            "53100 = tun\n",
            "119 = .\n",
            "102 = [SEP]\n"
          ]
        }
      ],
      "source": [
        "tokens = tokenizer.encode(sequence)\n",
        "for token in tokens:\n",
        "    print(f\"{token} = {tokenizer.decode([token])}\")"
      ],
      "id": "xFvN8PxWVV2t"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7V8-yBVfVV2w",
        "outputId": "c776908d-c47e-486d-9add-6a138303ea69",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=int64, numpy=16>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "input = tokenizer.encode(sequence, return_tensors=\"tf\")\n",
        "mask_token_index = tf.where(input == tokenizer.mask_token_id)[0, 1]\n",
        "mask_token_index"
      ],
      "id": "7V8-yBVfVV2w"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7pTfO92IVV2x",
        "outputId": "f4c1f628-1a7a-48b5-b098-98b53fb9b6da",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13011 = Deutschland\n"
          ]
        }
      ],
      "source": [
        "token_logits = model(input)[0]\n",
        "token = token_logits.numpy()[0][1].argmax()\n",
        "print(f\"{token} = {tokenizer.decode([token])}\")"
      ],
      "id": "7pTfO92IVV2x"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qZ2Lb5wCVV2x",
        "outputId": "997fb336-5ff8-49dc-c0ed-951a1cd8b6d3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.8577833 , 0.04147318, 0.03630371, 0.03398074, 0.03045904],\n",
              "       dtype=float32),\n",
              " array([13011, 38451, 54760, 27879, 23942], dtype=int32))"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "mask_token_logits = token_logits[0, mask_token_index, :]\n",
        "top_5 = tf.math.top_k(mask_token_logits, 5)\n",
        "probas = tf.nn.softmax(top_5.values).numpy()\n",
        "indices = top_5.indices.numpy()\n",
        "probas, indices\n"
      ],
      "id": "qZ2Lb5wCVV2x"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "voDDu287VV2x",
        "outputId": "27d49010-9148-4632-c6eb-6c5c3c31a0a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13011 = Deutschland, proba 85.8%\n",
            "38451 = nichts, proba 4.1%\n",
            "54760 = Freiheit, proba 3.6%\n",
            "27879 = Deutsch, proba 3.4%\n",
            "23942 = etwas, proba 3.0%\n"
          ]
        }
      ],
      "source": [
        "for token, proba in zip(indices, probas):\n",
        "    print(f\"{token} = {tokenizer.decode([token])}, proba {proba*100:.1f}%\")"
      ],
      "id": "voDDu287VV2x"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lxic2SpXVV2x",
        "outputId": "30370ed8-2b96-44ff-ac84-1a065601c519",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Deutschland ist ein tolles Land. Als Bürger hast du das Recht, Deutschland zu tun.\n",
            "Deutschland ist ein tolles Land. Als Bürger hast du das Recht, nichts zu tun.\n",
            "Deutschland ist ein tolles Land. Als Bürger hast du das Recht, Freiheit zu tun.\n",
            "Deutschland ist ein tolles Land. Als Bürger hast du das Recht, Deutsch zu tun.\n",
            "Deutschland ist ein tolles Land. Als Bürger hast du das Recht, etwas zu tun.\n"
          ]
        }
      ],
      "source": [
        "for token in indices:\n",
        "    print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))"
      ],
      "id": "lxic2SpXVV2x"
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.18"
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