{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cml6YCfc_Xp2"
      },
      "source": [
        "# Processing the data (TensorFlow)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4pht-Iuz_Xp8"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "hSSBfqP8_XqG",
        "outputId": "0ea88f61-3bed-4cca-806e-f643fd7317b7",
        "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",
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            "\u001b[?25hCollecting transformers[sentencepiece]\n",
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            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\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",
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            "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": "BLd2vQaV_XqK",
        "outputId": "2642683b-7cbf-4424-abc3-b563b9f83ffc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286,
          "referenced_widgets": [
            "ef635d0527004750a07f83ee5880c314",
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            "5cff8438891048bf912ba4bdc5c8b068",
            "84b9e7691ed24081af4b506a690e94bd",
            "caa72476377243b68f8e8189eef7eba0",
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            "0f62870e267a400d82c1db93bd8d2d7b",
            "1590607b76c0441eb40dbae787c0bd84"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef635d0527004750a07f83ee5880c314"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65e102c4ecef461183a58d31a2080d8e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "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": "ab026feeaacc47f696f31c0e222ff878"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27675f6fbe9d45c2bb4b01168ea8cd4c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "348573d075144b11b199d2143a3def73"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some weights or buffers of the TF 2.0 model TFBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.93443489074707"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
        "\n",
        "# Same as before\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "sequences = [\n",
        "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
        "    \"This course is amazing!\",\n",
        "]\n",
        "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
        "\n",
        "# This is new\n",
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
        "labels = tf.convert_to_tensor([1, 1])\n",
        "model.train_on_batch(batch, labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "k13BK2fm_XqN",
        "outputId": "6f6a069e-4b3c-46ad-c39e-45761f0c3688",
        "colab": {
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          "height": 587,
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            "f8f48138c09342ecb6e11134e31bb3dd",
            "28585de05b214961b373f151334f1938"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/28.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5bc6d8b2f5045348fae6e6c2e66f598"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7efd300a201944e38e3b894ccd38fe2d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/27.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c257b85266a14c2fb16ea70365726c65"
            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2dce5756fb11459abe38a20422abb9fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2c84f63d04648d49a86973e1becd79b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a57d3f22213d4475994c869f6578c9f6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "18fa2d02fcc54b1ca0920058fb3dfbd6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9e5156614414c038c586c4ba9ba6ef2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1725 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f48cc712e3024dfab389b168311464b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 3668\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 408\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 1725\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
        "raw_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Gy8r-RAL_XqQ",
        "outputId": "55292178-9e46-439f-9bcd-9fcb591282a8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
              " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .',\n",
              " 'label': 1,\n",
              " 'idx': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "raw_train_dataset = raw_datasets[\"train\"]\n",
        "raw_train_dataset[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "QxhZ7_AV_XqT",
        "outputId": "1af4a037-719c-40e9-ba0f-52641164b990",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': Value(dtype='string', id=None),\n",
              " 'sentence2': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None),\n",
              " 'idx': Value(dtype='int32', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "raw_train_dataset.features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "nNIQ8obC_XqV"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
        "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "XnGVRyit_XqX",
        "outputId": "8ed6ead8-73df-43ec-b6fd-7d018eccf731",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
        "inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "g3RFY8m1_Xqb",
        "outputId": "35f12aa1-6a0c-4370-fc90-cdb2adf88047",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'this',\n",
              " 'is',\n",
              " 'the',\n",
              " 'first',\n",
              " 'sentence',\n",
              " '.',\n",
              " '[SEP]',\n",
              " 'this',\n",
              " 'is',\n",
              " 'the',\n",
              " 'second',\n",
              " 'one',\n",
              " '.',\n",
              " '[SEP]']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "APY2YeWN_Xqd"
      },
      "outputs": [],
      "source": [
        "tokenized_dataset = tokenizer(\n",
        "    raw_datasets[\"train\"][\"sentence1\"],\n",
        "    raw_datasets[\"train\"][\"sentence2\"],\n",
        "    padding=True,\n",
        "    truncation=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Eof-suWQ_Xqg"
      },
      "outputs": [],
      "source": [
        "def tokenize_function(example):\n",
        "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "8a8RuBDs_Xqk",
        "outputId": "2e4cfe87-4bbe-44df-b3d5-198c651f15e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363,
          "referenced_widgets": [
            "5e52922585a247dab2c10cb6ed87d8cc",
            "e2cfa07a1c4a46848c6c06378e27e92d",
            "8b13dc01676b44beb6097d2c9f30b28e",
            "a3adac6049804522a8e7f0b00e619330",
            "0c5265e23fbf4ad08c9a4fbe746466f7",
            "ca8362f7992b43349f0c7798eb9c5938",
            "413798665ffa40148866a3e6b77c8f86",
            "6cac645895bd457493a38e8bb46e3bf4",
            "59326e75d9f24eab8234932e535aa34b",
            "18983d23c0f544f18cd164a22fadf481",
            "dff26ec267b54d2d926558ea7953472a",
            "764f9fb724944fa3bafebb0f49a0b898",
            "807ca8ce73104809b86170ed11d6ff0c",
            "e86cb7df79364b019e60391829adf5c3",
            "583dec26f340478c9390d2421e475b5a",
            "dbd45df27649460a8e1486e5cb7ebe6b",
            "c8ab78de8cb4485c97303deac92b2e49",
            "b2cf878953b046508c968e35543118bd",
            "0429e57e65f644ed94aeb5fc4c205271",
            "8c8e96d6eec441a88c64fd4b75072626",
            "fad5e182a82b4adea1479640e00e3ba5",
            "50b88aa54ce648749cb01a6da9be1c4c",
            "ded2efc47e1d4325ab76f0897622667f",
            "b77e57aeaf224dca9512119e3fbf6053",
            "f1b9cf0ae0b14e9fa2e0958f295e5346",
            "3726aba52ebc472998129cc5a2c772a9",
            "5a6b519b2ac443549b9488a2305decd8",
            "c4a9affeba8a44eaa493a3ec340b9f26",
            "233a8f65d1e74be9b686cbd8cc3073f0",
            "4bf116c1c07c45e89344d9f022b54518",
            "8a9b2dfb954f4ed197251f8b0657a73c",
            "e10ecf0bf2a14fd3aafab9ccd01c8943",
            "2a141778fec14f14b8b155eb30dadad5"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e52922585a247dab2c10cb6ed87d8cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "764f9fb724944fa3bafebb0f49a0b898"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ded2efc47e1d4325ab76f0897622667f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 3668\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 408\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1725\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "tokenized_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "qVo-58k__Xqo"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "TIf2RjUd_Xqp",
        "outputId": "5ac0f539-4130-471d-cf68-b7c7f486c4b9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[50, 59, 47, 67, 59, 50, 62, 32]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "samples = tokenized_datasets[\"train\"][:8]\n",
        "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
        "[len(x) for x in samples[\"input_ids\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "z9LKEDUU_Xqr",
        "outputId": "faa890cd-cec3-450e-dea0-990220b99061",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': TensorShape([8, 67]),\n",
              " 'token_type_ids': TensorShape([8, 67]),\n",
              " 'attention_mask': TensorShape([8, 67]),\n",
              " 'labels': TensorShape([8])}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "batch = data_collator(samples)\n",
        "{k: v.shape for k, v in batch.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "7bNB8fJN_Xqs",
        "outputId": "da874b4d-b426-498a-c5d9-7d8dd1936efb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:400: FutureWarning: The output of `to_tf_dataset` will change when a passing single element list for `labels` or `columns` in the next datasets version. To return a tuple structure rather than dict, pass a single string.\n",
            "Old behaviour: columns=['a'], labels=['labels'] -> (tf.Tensor, tf.Tensor)  \n",
            "             : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor)  \n",
            "New behaviour: columns=['a'],labels=['labels'] -> ({'a': tf.Tensor}, {'labels': tf.Tensor})  \n",
            "             : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor) \n",
            "  warnings.warn(\n"
          ]
        }
      ],
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        "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
        "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
        "    label_cols=[\"labels\"],\n",
        "    shuffle=True,\n",
        "    collate_fn=data_collator,\n",
        "    batch_size=8,\n",
        ")\n",
        "\n",
        "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
        "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
        "    label_cols=[\"labels\"],\n",
        "    shuffle=False,\n",
        "    collate_fn=data_collator,\n",
        "    batch_size=8,\n",
        ")"
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