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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Text classification NLP"
      ],
      "metadata": {
        "id": "jBdTVNkkpkAy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers datasets evaluate accelerate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EwA9s_kZpj4u",
        "outputId": "3c0555cc-e1c2-42d2-b38f-d14d307613f8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.16.1-py3-none-any.whl (507 kB)\n",
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            "\u001b[?25hCollecting evaluate\n",
            "  Downloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n",
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            "\u001b[?25hCollecting accelerate\n",
            "  Downloading accelerate-0.26.1-py3-none-any.whl (270 kB)\n",
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            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
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            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n",
            "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
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            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
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            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2023.10.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.1)\n",
            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu121)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
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            "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: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\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.post1)\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",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: dill, responses, multiprocess, accelerate, datasets, evaluate\n",
            "Successfully installed accelerate-0.26.1 datasets-2.16.1 dill-0.3.7 evaluate-0.4.1 multiprocess-0.70.15 responses-0.18.0\n"
          ]
        }
      ]
    },
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        "id": "VeNYbsG3pf0R",
        "outputId": "729827e1-9d1b-4e42-b797-83f3a7d81a28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/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": [
              "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": "2ab8f07d73994aa2a73d75ee31272db6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "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": "74dcffd83aa14e79bf51444ccd61e0d5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "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": "0e3ea74a4df64ad0910feee52a4af2ca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "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": "075c5ca42d244e83b5bde8c120638665"
            }
          },
          "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": "e7534d017bfa477fa1bb71adc9b4ec91"
            }
          },
          "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": "4941aecba6fa42bc8e962ca8670af47a"
            }
          },
          "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": "67e710a8c1bd40bd98124bf1424faf79"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "imdb = load_dataset(\"imdb\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imdb[\"test\"][0] # look at example"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0D0Zphvepspq",
        "outputId": "c283d3d5-a34d-4585-a170-f1a8d3b3391a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn\\'t match the background, and painfully one-dimensional characters cannot be overcome with a \\'sci-fi\\' setting. (I\\'m sure there are those of you out there who think Babylon 5 is good sci-fi TV. It\\'s not. It\\'s clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It\\'s really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it\\'s rubbish as they have to always say \"Gene Roddenberry\\'s Earth...\" otherwise people would not continue watching. Roddenberry\\'s ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again.',\n",
              " 'label': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Can see there are 2 fields: text, label (0 for neg, 1 for pos)"
      ],
      "metadata": {
        "id": "hyVV0mwFpy5J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "84c5175427874dddbb36051ec301bd35",
            "699a690d48e24fc8834f4e5eb392e205",
            "4f23e34991f94448a283822b947018a6",
            "ceda60681735462389365f8e5f4469e2",
            "769c7dc838c94bfa830a16cc25daa880",
            "796e939c63a14346852d6a10a3b52a9d",
            "ccfda50c5bd745b0b388abe76e7c1676",
            "78af70899feb42268aff67f63341f5d3",
            "deb10330b4fd4ee2a6876618077f9cde",
            "815909af84dd4cd5a300cb08b69b43f9",
            "53f5b87876a34ce196466ef273c9b34e",
            "e41a623f0436465ca1d8858e4a15dfdc",
            "e4d2e27e414f44eeb88566193d5f5e3e",
            "e1a9222cf4034b00861a2ace2582a989",
            "fcd778bebd1d408c894afb48186c8ed1",
            "bfd65421caf244709cf6b2d1ae22113d"
          ]
        },
        "id": "PUhq_wf_pvwF",
        "outputId": "761f5509-507f-4fd4-f379-25ede51c2a77"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49a9188141bb4377a601b798e5506636"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "333492686413446198bce5b5beadbcc9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d542a9fed9c4e61b8b861f570833b1a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
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              "model_id": "796e939c63a14346852d6a10a3b52a9d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(examples):\n",
        "    return tokenizer(examples[\"text\"], truncation=True)"
      ],
      "metadata": {
        "id": "OBEe2R-Gp50I"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_imdb = imdb.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "67c91cec0bbd4cc584ed4eb02e4d4441",
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            "3dfd74e566c941a0af3fdff682ce876b",
            "2fde0789dfe24606a7a33b4ec771f28c",
            "201afec26f82473abe203c0c1f5f9245",
            "26933dde2d2b425086ddad5385d34c18",
            "3ac325eb40f74d138e1d660b71ae1de2",
            "07362a5744e840e7b0166741ecf47cbd",
            "e8397d540fc4421498ad1f13c51ca4b3",
            "b0931cae2f364de2a9171b2ee9779e20",
            "3b67a272a0c54ec8bd3a48b5521d9665",
            "91143fe2c4164231a0ea5a891139fcb3",
            "eaf70d4f19a34fd09ad0d753d72ed4ee",
            "6b354ab5846844a9abac4403c22410ba",
            "3b91d388c1b44c408c0414fcbed4212c",
            "bd2c2ee1567a44538822293c60b7e7ca",
            "a2db7ecedc764e069dfd3b68f26738db",
            "9ca7e51c4bc1429694fef14ba8d2b909",
            "101e7fd900fb464e96ca7de912ce7092",
            "d93414866cfe43c6b396a7077d2710ba",
            "835dbd5b3ddf45b59bb4686bb90b53d4",
            "7610c9c1a9814c7088b7a1a2a9dabcb5",
            "a1f37bca4bbd4450981f58600ca648dc",
            "c854ff809dcb4df58ccd9edc6dbcc16e",
            "c901e7ad0b51483995e1efb988d4b19a",
            "e4c4e9007a6f48e88c80d7429d6ccc6b",
            "cf62e500696f45ecb56ccac0e208a367"
          ]
        },
        "id": "pnbFaLbMp7R4",
        "outputId": "40d1bffc-47ce-4ede-bc42-47cc9ab63e3d"
      },
      "execution_count": 7,
      "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": "db4a763e7b43411e923be8d2b32f0569"
            }
          },
          "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": "26933dde2d2b425086ddad5385d34c18"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2db7ecedc764e069dfd3b68f26738db"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now create a batch of examples using DataCollatorWithPadding. It’s more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.\n",
        "\n"
      ],
      "metadata": {
        "id": "t6axahXbqAbi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
      ],
      "metadata": {
        "id": "uD4Msmh6qA4O"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluate\n",
        "\n",
        "Including a metric during training is often helpful for evaluating your model’s performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):"
      ],
      "metadata": {
        "id": "D4sWC5UfqHd2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "accuracy = evaluate.load(\"accuracy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d5638dd1f0cc4d4abb6ef18e197e02ec",
            "717eb99de4dd40639d92e3dbf78c5f47",
            "438e1b87ce97471ebd006631e822e53e",
            "819c26ddc9f7420581c1cc053908d7d8",
            "9c8f4415547442e187c28530a76f2697",
            "54316374b56346e5b8169cbb8eee991d",
            "cb8104f349904e7aa79ed97fe5d4b024",
            "089335ac3b834416a0f3eb0beedad278",
            "e86802775e35452ea36c526e7a3b77b8",
            "1d7651a05c924a46a288b7dacbb84342",
            "64f18a0162ac48309f01e78b7b888634"
          ]
        },
        "id": "xOTonxnrqKcO",
        "outputId": "8581bc4b-0ca5-4b45-9ae8-436e2baf3b89"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5638dd1f0cc4d4abb6ef18e197e02ec"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then create a function that passes your predictions and labels to compute to calculate the accuracy:\n",
        "\n"
      ],
      "metadata": {
        "id": "ajI3xo-uqL6f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return accuracy.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "_Et3tZAdqMX6"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train\n",
        "\n",
        "Before you start training your model, create a map of the expected ids to their labels with id2label and label2id:"
      ],
      "metadata": {
        "id": "qCEuLg_9qO1v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {0: \"NEGATIVE\", 1: \"POSITIVE\"}\n",
        "label2id = {\"NEGATIVE\": 0, \"POSITIVE\": 1}"
      ],
      "metadata": {
        "id": "-JV8-iuBqRKb"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:\n"
      ],
      "metadata": {
        "id": "nVUkSZN8qULr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import create_optimizer\n",
        "import tensorflow as tf\n",
        "\n",
        "batch_size = 16\n",
        "num_epochs = 5\n",
        "batches_per_epoch = len(tokenized_imdb[\"train\"]) // batch_size\n",
        "total_train_steps = int(batches_per_epoch * num_epochs)\n",
        "optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)"
      ],
      "metadata": {
        "id": "qIXoV_utqSfm"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then you can load DistilBERT with TFAutoModelForSequenceClassification along with the number of expected labels, and the label mappings:"
      ],
      "metadata": {
        "id": "Dg8zp92MqWBo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFAutoModelForSequenceClassification\n",
        "\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(\n",
        "    \"distilbert-base-uncased\", num_labels=2, id2label=id2label, label2id=label2id\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "6c4bfd29023943f0bbf2561be8a9587e",
            "f41ada90707e4e7188a5dd73bc1a3c3c",
            "8c70a8afd2b442b4b77fb84edde2ff60",
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            "235dc975413446c893e7b8ead2483c54",
            "2b4ef727f1db4e4fac1236601ae50194"
          ]
        },
        "id": "RUMzXChlqU25",
        "outputId": "c75551a5-6249-483b-cbf9-ba80c40c717f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c4bfd29023943f0bbf2561be8a9587e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights or buffers of the TF 2.0 model TFDistilBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert your datasets to the tf.data.Dataset format with prepare_tf_dataset():\n",
        "\n"
      ],
      "metadata": {
        "id": "cqSlK7QBqYrb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf_train_set = model.prepare_tf_dataset(\n",
        "    tokenized_imdb[\"train\"],\n",
        "    shuffle=True,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "\n",
        "tf_validation_set = model.prepare_tf_dataset(\n",
        "    tokenized_imdb[\"test\"],\n",
        "    shuffle=False,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")"
      ],
      "metadata": {
        "id": "bSpo0iFsqZku",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6273c1f8-8471-4d73-f890-6df789cf3fe2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don’t need to specify one unless you want to:"
      ],
      "metadata": {
        "id": "bX9NyjDlqcMb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "model.compile(optimizer=optimizer)  # No loss argument!"
      ],
      "metadata": {
        "id": "X7YQnjjpqbBb"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Callbacks\n",
        "\n",
        "he last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks."
      ],
      "metadata": {
        "id": "fSfRRccqqhTA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers.keras_callbacks import KerasMetricCallback\n",
        "\n",
        "metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)"
      ],
      "metadata": {
        "id": "3ORzhk6gqj2k"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = [metric_callback]"
      ],
      "metadata": {
        "id": "dkJ6vAf9qnwU"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lf53oTuqqy2w",
        "outputId": "d47029ee-b2ae-4b22-fb21-6147df96a7c1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "1562/1562 [==============================] - 778s 484ms/step - loss: 0.2479 - val_loss: 0.1874 - accuracy: 0.9264\n",
            "Epoch 2/3\n",
            "1562/1562 [==============================] - 746s 478ms/step - loss: 0.1307 - val_loss: 0.1920 - accuracy: 0.9307\n",
            "Epoch 3/3\n",
            "1562/1562 [==============================] - 744s 476ms/step - loss: 0.0633 - val_loss: 0.2067 - accuracy: 0.9324\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x79be1c1b1480>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtR-rwDeDdvS",
        "outputId": "e5565571-db1b-4a52-ea54-b062db873f89"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"tf_distil_bert_for_sequence_classification\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " distilbert (TFDistilBertMa  multiple                  66362880  \n",
            " inLayer)                                                        \n",
            "                                                                 \n",
            " pre_classifier (Dense)      multiple                  590592    \n",
            "                                                                 \n",
            " classifier (Dense)          multiple                  1538      \n",
            "                                                                 \n",
            " dropout_19 (Dropout)        multiple                  0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 66955010 (255.41 MB)\n",
            "Trainable params: 66955010 (255.41 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(tf_validation_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aRRs6mK0DzEJ",
        "outputId": "708f09ce-f762-4553-e5dc-2988f926cc76"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1563/1563 [==============================] - 152s 97ms/step - loss: 0.2067\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.20666199922561646"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Inference"
      ],
      "metadata": {
        "id": "VPmxjDqmrAae"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"The movie was not good at all. I've watched tomatoes that were better\""
      ],
      "metadata": {
        "id": "bZZ7Ws85rCxy"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"tf\")\n",
        "\n",
        "logits = model(**inputs).logits\n",
        "\n",
        "predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n",
        "model.config.id2label[predicted_class_id]"
      ],
      "metadata": {
        "id": "gzCvc-TDrFkL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5fe9861b-95ea-4b8a-eb26-4ff2dfc10e04"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'NEGATIVE'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "6LlbwGpdF6WE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('tf_distil_bert_for_sequence_classification')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGztfM3PD_w8",
        "outputId": "7ab5ef2b-9635-40e8-a852-1ff45672f33e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.regularization.dropout.Dropout object at 0x79be1c52cb20>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.regularization.dropout.Dropout object at 0x79be1c52f700>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.regularization.dropout.Dropout object at 0x79be1c432380>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.regularization.dropout.Dropout object at 0x79be1c439000>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.regularization.dropout.Dropout object at 0x79be1c43bc40>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.regularization.dropout.Dropout object at 0x79be1c44a8c0>, because it is not built.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = tf.keras.models.load_model('/content/tf_distil_bert_for_sequence_classification', compile=False)\n",
        "loaded_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dOm5LFITGsYd",
        "outputId": "138bf75e-d0b2-4fa1-feb5-32c47d7b67c2"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"tf_distil_bert_for_sequence_classification\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " distilbert (TFDistilBertMa  multiple                  66362880  \n",
            " inLayer)                                                        \n",
            "                                                                 \n",
            " pre_classifier (Dense)      multiple                  590592    \n",
            "                                                                 \n",
            " classifier (Dense)          multiple                  1538      \n",
            "                                                                 \n",
            " dropout_19 (Dropout)        multiple                  0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 66955010 (255.41 MB)\n",
            "Trainable params: 66955010 (255.41 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy, metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "uTkzeKyyPbad"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loaded_model.evaluate(tf_validation_set)"
      ],
      "metadata": {
        "id": "Sd7VxSB5P0x6"
      },
      "execution_count": 38,
      "outputs": []
    }
  ]
}