diff --git "a/Processing_the_data_(TensorFlow).ipynb" "b/Processing_the_data_(TensorFlow).ipynb"
new file mode 100644--- /dev/null
+++ "b/Processing_the_data_(TensorFlow).ipynb"
@@ -0,0 +1,7163 @@
+{
+  "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",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m6.4 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",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting transformers[sentencepiece]\n",
+            "  Downloading transformers-4.32.1-py3-none-any.whl (7.5 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m33.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
+            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
+            "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
+            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
+            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
+            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.1)\n",
+            "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[31m20.1 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",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 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) (3.8.5)\n",
+            "Collecting huggingface-hub<1.0.0,>=0.14.0 (from datasets)\n",
+            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
+            "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",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (2023.6.3)\n",
+            "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",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m73.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\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",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m68.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\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[31m70.3 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",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
+            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
+            "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": "BLd2vQaV_XqK",
+        "outputId": "2642683b-7cbf-4424-abc3-b563b9f83ffc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286,
+          "referenced_widgets": [
+            "ef635d0527004750a07f83ee5880c314",
+            "9265713240014c75832e5989210e2353",
+            "2eb9a723cc7c4f3d9b8be90c65e13e45",
+            "5cff8438891048bf912ba4bdc5c8b068",
+            "84b9e7691ed24081af4b506a690e94bd",
+            "caa72476377243b68f8e8189eef7eba0",
+            "7c48a7c2d0984b15b7f29b4e88bacf0f",
+            "67b4f46340974ca88de9ddd46ac0a239",
+            "feff94fa6be947409f13a6d70e52b54a",
+            "7cf27989273943be8a45d773c18c8c2a",
+            "4112146a1a214474be6e6994e7849e5e",
+            "65e102c4ecef461183a58d31a2080d8e",
+            "fa1f6e2c81ba4a7cb5a39be158d2212d",
+            "f6ae94716d5440b2a2f36a9e2c8c32e5",
+            "9107810f1d884160a6c541b7d10221ae",
+            "21b4f4c88fc24b869dd210132f5df6e4",
+            "ecbfd8f2426d4047b15f8ec615495c5c",
+            "2b22d08d1b5b4f8ea89aaa7e27b20d46",
+            "07ada09972014061a69e44bb4fad2d46",
+            "3d1b8491c4e146e4a3710cd596fef388",
+            "8a5a07d8486446f794de32aebb4d5899",
+            "ac14d17c710a4b54814fd7beb1bc6914",
+            "ab026feeaacc47f696f31c0e222ff878",
+            "283c8464f0c343e9b49243b8297827a2",
+            "6940f90647b54ce786b774b2f8d70fae",
+            "0fd36d4fc503436699789a23030aceca",
+            "1d9b1e6dde414d2a85edfc4f2a9506bb",
+            "9d8b0c4d33944c989210d398fa986ba6",
+            "a8f23aaa85f745b38d50b7e346219fa0",
+            "b84139af8ee343b388441a9728f12666",
+            "eabe20b8274645858284fa421b819742",
+            "b3a6f3a6f0324b29bb8a0da8c1e3e973",
+            "8715624c7bbb4f238bdfd37be7018478",
+            "27675f6fbe9d45c2bb4b01168ea8cd4c",
+            "a37b18978d9c469a808526ec77fbf3a6",
+            "b04445af16304d22a301613e5fe9cbd6",
+            "610f0e430d6148ffa106ffd43962fb0b",
+            "2f1f0262964445b393355e7039228996",
+            "a80fc49b9d5c4a5b8ff267eed6d4cdf2",
+            "7472638d27c64b2b9f0ac3227a88f036",
+            "0623f4846aa6471eb1f7b6a7f17bd166",
+            "56f2d9fbdf9a4fa188ebee518b0f97ed",
+            "322c3a4b6da6499b8f5ce420b273dae3",
+            "25da192ccb5442b6affeb7a340732e1e",
+            "348573d075144b11b199d2143a3def73",
+            "7a18cd8b3bbb45c196a96c3aefbb7f06",
+            "cda703f7b0634f81b55b62b28f65704e",
+            "8ba64d023715471ab48d27f1bada840a",
+            "aea12e4fa4204bb9807d3012ef318403",
+            "faf76d1f4e354a5aa7776de93b61cc4e",
+            "66be8f2e732a40778089dd934dc4043d",
+            "e7e8e4c17a484ec5b478040443dea301",
+            "369e5bd4c67a47aa878bb3ec3833b29b",
+            "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"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "65e102c4ecef461183a58d31a2080d8e"
+            }
+          },
+          "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": "ab026feeaacc47f696f31c0e222ff878"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "27675f6fbe9d45c2bb4b01168ea8cd4c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+            ],
+            "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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 587,
+          "referenced_widgets": [
+            "d5bc6d8b2f5045348fae6e6c2e66f598",
+            "565d5d81052f4c5d8966769a11c79147",
+            "c6d68bead5644faa8c83fc8a0a3745b8",
+            "4d05be28e3e141389fbfc0a29a0324ce",
+            "a49b15a3e64e403590d4a686abf3a85b",
+            "6a93a7d1bd4b4feba4bfd6b3417a1e0b",
+            "857de20b60d440b1a2cc0dfd7d7f9b36",
+            "a05afb36a3bb48dfa0f64953c05a76b5",
+            "797da881d46243cfa852bf65423dc6be",
+            "c4afff0016334218a76a6fd732befd94",
+            "fec55103ebc147c0962dba97d2bdb1be",
+            "7efd300a201944e38e3b894ccd38fe2d",
+            "249b89b56039470f86bb6731fa742502",
+            "8f2bafebf0b54b1bb9a131d45ba576fc",
+            "654f71c1d6bf4699bb7395d36f1b6650",
+            "4d5633b488134f2e8fd806dbf07e68b5",
+            "6e3c1e96eb054270badf410621bb794a",
+            "8d735c00cb804711a02fac58e93491ec",
+            "e5dd25c7005a47779e407199f65c396b",
+            "4d28697a97b145bea27d4afa422b2f32",
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+            "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": {
+            "text/plain": [
+              "Downloading metadata:   0%|          | 0.00/28.7k [00:00<?, ?B/s]"
+            ],
+            "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"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "83c1f7cbfa8a481f957b7cf116f06d84"
+            }
+          },
+          "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": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "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"
+          ]
+        }
+      ],
+      "source": [
+        "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",
+        ")"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "Processing the data (TensorFlow)",
+      "provenance": []
+    },
+    "language_info": {
+      "name": "python"
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