{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dPrEUyJ4XXrr"
      },
      "source": [
        "# Putting it all together (TensorFlow)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xQjFMOvGXXru"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4IyvuubVXXrx",
        "outputId": "47421454-109f-499e-800f-635e9ffd5d79",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.14.4-py3-none-any.whl (519 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting evaluate\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
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            "\u001b[?25hCollecting transformers[sentencepiece]\n",
            "  Downloading transformers-4.32.1-py3-none-any.whl (7.5 MB)\n",
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            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
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            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
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            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n",
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            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.12.2)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers[sentencepiece])\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers[sentencepiece])\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hCollecting sentencepiece!=0.1.92,>=0.1.91 (from transformers[sentencepiece])\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m63.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.20.3)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (3.2.0)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.7.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.4 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.16.4 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.32.1 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "_IaZrjk2XXr5",
        "outputId": "e56a47dd-a01f-4786-88aa-fc036f901d39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "6e8d9515b0d64541badafb94f886fb53",
            "64c20d1c08bb4ef5a2c805191da6e0c3",
            "2e0141a2e18745329743dc82a713ab3a",
            "62d78d871c4247f597f5281b08659a5a",
            "d80a1e1d92c7427d8050315b7929ac44",
            "d294acadea3c4083807ce5de4c85b139",
            "2bb45a6a4640438692aef57c96bd1ef2",
            "d0251bef4e354aa7831b0d090bde8909",
            "d9a7e78542634d20b2d72e559eac321d",
            "c18a297542a448138c12dc0cb663472e",
            "6bc05f9825f547b6b1cab315985ec323",
            "fb8f882fd9e445f48c93abecb47c8707",
            "3b183e7bfa964561b385bb12397f767e",
            "8429cf84086540fe824480bdd89af8db",
            "673be5123a094a9f854eda985491a7b2",
            "114e3e99a9644a2d9af94b4790f0f43a",
            "4c8af8869fcf4f29a3fc24c50afb85c6",
            "e631c2774d4144c18d12555c291f0870",
            "2d9cfdefd9dd4c7f869c85e598dd660c",
            "2d546cf78b634df79f873e81ce7d82b3",
            "8762c8ae1e644f4698e8b0e5987d25e8",
            "5d8b7642a7264682ad0e88bd046f91e6",
            "7695d2d28c834eee9d3c719cea0ac2cf",
            "a5a9bc9913fc4fd2bf39bf411839713f",
            "1dbac2441c5743dc8358dcd421844971",
            "8154bfbe593d4e17899069bf261ecedc",
            "748786cbc5e945cbb2b3d5662e5c7b52",
            "b7481b2e7dad4b4491c86e9b5d4fc082",
            "46a7d5c190334718a6d786e96e14adc9",
            "2e17ea86befe47d19abc6b3b40fccfe4",
            "94141f0ef5374572b85f01089ff9901f",
            "2e4175d4f6b24d04b0a62742aea404bb",
            "d6ca5e34870444848083c75e75516d5a"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e8d9515b0d64541badafb94f886fb53"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fb8f882fd9e445f48c93abecb47c8707"
            }
          },
          "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": "7695d2d28c834eee9d3c719cea0ac2cf"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
        "\n",
        "model_inputs = tokenizer(sequence)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "1M-XZeCTXXr7"
      },
      "outputs": [],
      "source": [
        "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
        "\n",
        "model_inputs = tokenizer(sequence)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "LeKkAHGlXXr9"
      },
      "outputs": [],
      "source": [
        "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
        "\n",
        "model_inputs = tokenizer(sequences)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "lF1e6fKQXXr-"
      },
      "outputs": [],
      "source": [
        "# Will pad the sequences up to the maximum sequence length\n",
        "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
        "\n",
        "# Will pad the sequences up to the model max length\n",
        "# (512 for BERT or DistilBERT)\n",
        "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
        "\n",
        "# Will pad the sequences up to the specified max length\n",
        "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "nhOlP4X7XXsA"
      },
      "outputs": [],
      "source": [
        "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
        "\n",
        "# Will truncate the sequences that are longer than the model max length\n",
        "# (512 for BERT or DistilBERT)\n",
        "model_inputs = tokenizer(sequences, truncation=True)\n",
        "\n",
        "# Will truncate the sequences that are longer than the specified max length\n",
        "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "WU9TqDuqXXsB"
      },
      "outputs": [],
      "source": [
        "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
        "\n",
        "# Returns PyTorch tensors\n",
        "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
        "\n",
        "# Returns TensorFlow tensors\n",
        "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
        "\n",
        "# Returns NumPy arrays\n",
        "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "LidWgT5-XXsD",
        "outputId": "01d47ecc-a8fc-4d7c-d534-88c090bb26fe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
            "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]\n"
          ]
        }
      ],
      "source": [
        "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
        "\n",
        "model_inputs = tokenizer(sequence)\n",
        "print(model_inputs[\"input_ids\"])\n",
        "\n",
        "tokens = tokenizer.tokenize(sequence)\n",
        "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "print(ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "3rBbz5KFXXsE",
        "outputId": "cfcbde1b-fadc-4297-b336-ebb5e1a3f56c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS] i've been waiting for a huggingface course my whole life. [SEP]\n",
            "i've been waiting for a huggingface course my whole life.\n"
          ]
        }
      ],
      "source": [
        "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
        "print(tokenizer.decode(ids))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Aqsxx_DkXXsG",
        "outputId": "39407dfe-2872-4c66-83cc-0714a481111d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
            "ce3dd215ffc04932a8bbf1bc722573b5",
            "0c6b5f1167b64b86a218de01571a834a",
            "a559f55b93c54f5da149938f364f3d0f",
            "4f815b17eb98443baba91cc81c8e062d",
            "bd7626146d9e4e66bdc67e006abe604c",
            "c6158847d12b4fc3bd44f2793eea55b6",
            "a498708a4a6d4376878e8ef473df222c",
            "7ae3d18def4547939cccfe50f5e531c1",
            "eb3af9dc2af8486ab43534a7298ce725",
            "75d7054c234e45f191ecc7ed00f74928",
            "79cb34563b2f407794d763f0cd14dc51"
          ]
        }
      },
      "outputs": [
        {
          "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": "ce3dd215ffc04932a8bbf1bc722573b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFDistilBertForSequenceClassification.\n",
            "\n",
            "All the weights of TFDistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
        "\n",
        "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
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