{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4tEKoTZ1cHe"
      },
      "source": [
        "# Tokenizers (TensorFlow)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwRCGCiN1cHk"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "rHmBWpn31cHq",
        "outputId": "7267872a-b0c3-4a4c-d558-a4ed77f6dc51",
        "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[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting evaluate\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
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            "\u001b[?25hCollecting transformers[sentencepiece]\n",
            "  Downloading transformers-4.32.1-py3-none-any.whl (7.5 MB)\n",
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            "  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 xxhash (from datasets)\n",
            "  Downloading xxhash-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hCollecting 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]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
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            "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",
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            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n",
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            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.12.2)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers[sentencepiece])\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m94.2 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[31m58.9 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[31m50.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.20.3)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (3.2.0)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.7.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.4 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.16.4 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.32.1 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "HfTOmBru1cHt",
        "outputId": "71ad6e81-7f1e-4054-aa7a-1db157d0e0dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Hi,', 'I', 'am', 'Mahule']\n"
          ]
        }
      ],
      "source": [
        "tokenized_text = \"Hi, I am Mahule\".split()\n",
        "print(tokenized_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "keDvrs8f1cHv",
        "outputId": "1a7603e8-499f-45e4-ffb5-1967ca9c00cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "a146cd54d1094308872b138d8df0c733",
            "e7eb9d0d2d65484aad6b3530c0b27953",
            "aec114fe707e4b54971e1f2e1a06c943",
            "2c25604196fe44fdaf31fc61b18a5b25",
            "afe3bff8730740839f726222bdf01690",
            "845523cd5b42457e82daeb120a63750b",
            "524d0bb4b55f46928f77ed207cc38960",
            "f58b1b2ba59542708154d435c99a129b",
            "006712e0e27a499aa68d470b65c6b3c6",
            "978a6c6b6fcc48b68f71fe5d2e75513b",
            "f6164a00e85a4c6aa9001f48e57e9094",
            "2c263a3d74e246c8bcf5d403977e7065",
            "ac03501d842c4ab298fcf64c9ad6df05",
            "2e7c7374e3e742e6a7e8f096815a6d4d",
            "dc26d02b754544fea290b9eecb47c449",
            "26c39fe80335499f94e3b61943911179",
            "ffe0a16b51ed4c4a893395ec7fd2fc25",
            "8a9514a81a2d4d54a95081298a05b568",
            "08150b2d05354e66b30e9a45a0cfc25d",
            "c61af07c69b7475b978c414b15d227ad",
            "14e9833113644d8ca7f68956f3a809a4",
            "a2f60a6b68c44d6e870fc65081a41eaf",
            "903c714944174a718671541ef8bfee50",
            "a84d7f0917d146e79a940482507332a9",
            "261e1d7d206945568f79d000e8ef9135",
            "25eb5ef0f71944259b99ac80001eae94",
            "3813850b90a74187bf18ec32a3634bf0",
            "968631b9293a4a49959bfce2b166343d",
            "79fded52694644b08614e9b194236fc2",
            "3c0350e52031407eb6e02701305330ff",
            "3cd19f516d894d22bf63659307067597",
            "22ca14fb75a6407a97dea746b5f02281",
            "aa40f63485dc41dca688d63cdb05fedb"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a146cd54d1094308872b138d8df0c733"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c263a3d74e246c8bcf5d403977e7065"
            }
          },
          "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": "903c714944174a718671541ef8bfee50"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import BertTokenizer\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Iu3PSCgF1cHx",
        "outputId": "636f1ae2-19c2-4300-a37d-62bb43e4103a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b4d293a21ebb427d961059feacb5a876",
            "d0dfa9da76d34f8dae3bb2b751d2816d",
            "50ee3e7cc36b41699fff0e0b2deca480",
            "2015d00ec93c4bd5b6ab0870e2f3283d",
            "49300220b8de4a90a25785585a9bab28",
            "b6d0476d32b945d6a0aaf0275035444f",
            "5c36a1ce72904291afcb56101ec45415",
            "dbaa1990251e48489ea6d647e4f20b5d",
            "169fd06eaf074fa5ac4bfb2100bfa598",
            "6b7a2727b9164145824742da5daf5ba4",
            "dfc2bb905bd74ad1ad91614be4b9734a"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b4d293a21ebb427d961059feacb5a876"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "08HgWshs1cH0",
        "outputId": "142addb8-adc1-4f89-a98a-266a044a12f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 7085, 24287, 1162, 1110, 3776, 1293, 1106, 1329, 170, 13809, 23763, 2443, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "tokenizer(\"Mahule is learning how to use a Transformer network\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "RNFGbCYu1cH1",
        "outputId": "5e0a93a8-5219-4acd-c7b7-25e3aa810eac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('directory_on_my_computer/tokenizer_config.json',\n",
              " 'directory_on_my_computer/special_tokens_map.json',\n",
              " 'directory_on_my_computer/vocab.txt',\n",
              " 'directory_on_my_computer/added_tokens.json',\n",
              " 'directory_on_my_computer/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "tokenizer.save_pretrained(\"directory_on_my_computer\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "xX5ab0VS1cH3",
        "outputId": "530c9567-dfc9-4130-cc43-c1e638e87dbb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Ma', '##hul', '##e', 'is', 'learning', 'how', 'to', 'use', 'a', 'Trans', '##former', 'network']\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "sequence = \"Mahule is learning how to use a Transformer network\"\n",
        "tokens = tokenizer.tokenize(sequence)\n",
        "\n",
        "print(tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "f72fxPfv1cH6",
        "outputId": "f2e2188f-5663-4cd8-b7a8-a420cbd0caee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7085, 24287, 1162, 1110, 3776, 1293, 1106, 1329, 170, 13809, 23763, 2443]\n"
          ]
        }
      ],
      "source": [
        "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "print(ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "b9yA27T71cH7",
        "outputId": "18c34eaf-2e7a-4fb4-ae05-c2c182a1d683",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mahule is learning how to use a Transformer network\n"
          ]
        }
      ],
      "source": [
        "decoded_string = tokenizer.decode([7085, 24287, 1162, 1110, 3776, 1293, 1106, 1329, 170, 13809, 23763, 2443])\n",
        "print(decoded_string)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Tokenizers (TensorFlow)",
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