Transformers_Tokenizer_For_Training_Study.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zetavg/LLM-Research/blob/main/Transformers_Tokenizer_For_Training_Study.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Transformers Tokenizer (For Training) Study\n",
        "\n",
        "Analyze the use of the tokenizer in training some open-source LLMs, and experiment with training a new tokenizer that performs better on CJK characters (Traditional Chinese)."
      ],
      "metadata": {
        "id": "O0cCHa_ordKZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "5c40qvRuq978",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "950ef15a-41d6-4c59-c276-8f0cc929fe80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0) (23.1)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "Installing collected packages: tokenizers, sentencepiece, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.14.1 sentencepiece-0.1.98 tokenizers-0.13.3 transformers-4.28.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers==4.28.0 sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# decapoda-research/llama-7b-hf seems to have strange tokenizer config\n",
        "llama_model_name = 'huggyllama/llama-7b'"
      ],
      "metadata": {
        "id": "abeaW3aWFCbW"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Basic: How Tokenizers Work"
      ],
      "metadata": {
        "id": "CjXEG_6t5R9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Load the GPT-2 Tokenizer\n",
        "\n",
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n",
        "tokenizer"
      ],
      "metadata": {
        "id": "svUvYSqo3oNa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199,
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        },
        "outputId": "fce96874-02fd-4e72-93c7-3c6308556565"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78f8a9da61b44f178af2d5a48444ddeb"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe9e0ab6d50740bfaffd117c369d8981"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2TokenizerFast(name_or_path='gpt2', vocab_size=50257, model_max_length=1024, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<|endoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>'}, clean_up_tokenization_spaces=True)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown This tokenizer has a few special symbols, like Ġ and Ċ, which denote spaces and newlines, respectively.\n",
        "\n",
        "tokenizer.tokenize(\"Hello, world!\\nNice to meet you!\")"
      ],
      "metadata": {
        "id": "vG5vVkJK4BZp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c56bc50-346a-43ae-9032-684dec608625"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Hello', ',', 'Ġworld', '!', 'Ċ', 'Nice', 'Ġto', 'Ġmeet', 'Ġyou', '!']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Load the LLaMA Tokenizer\n",
        "\n",
        "from transformers import LlamaTokenizer\n",
        "llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model_name)\n",
        "llama_tokenizer"
      ],
      "metadata": {
        "id": "-T7YMdSnsqFZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202,
          "referenced_widgets": [
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        "outputId": "cefd257c-b9cd-4751-829d-36675fbcc5e3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01633fdc3d69451c912ea8ae9c0e772a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/411 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21922b53fbdf49e58fed414fc3ce3b35"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/700 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "602caaf8b55847e68bc76e378ff993c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaTokenizer(name_or_path='huggyllama/llama-7b', vocab_size=32000, model_max_length=2048, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken(\"<unk>\", rstrip=False, lstrip=False, single_word=False, normalized=True)}, clean_up_tokenization_spaces=False)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown The LLaMA tokenizer handles special symbols differently.\n",
        "\n",
        "llama_tokenizer.tokenize(\"Hello, world!\\nNice to meet you!\")"
      ],
      "metadata": {
        "id": "EACe1rgS4X9o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf1fe35f-dfd4-4203-a75d-de4ca577f209"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['▁Hello',\n",
              " ',',\n",
              " '▁world',\n",
              " '!',\n",
              " '<0x0A>',\n",
              " 'N',\n",
              " 'ice',\n",
              " '▁to',\n",
              " '▁meet',\n",
              " '▁you',\n",
              " '!']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown The main method to tokenize. ([Docs](https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizer.__call__))\n",
        "# @markdown\n",
        "# @markdown It returns a [BatchEncoding](https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.PreTrainedTokenizer.__call__.returns) which has the following fields:\n",
        "# @markdown\n",
        "# @markdown * `input_ids` — List of token ids to be fed to a model. They are token indices, numerical representations of tokens.\n",
        "# @markdown * `token_type_ids` — List of token type ids to be fed to a model (when `return_token_type_ids=True` or if “token_type_ids” is in self.model_input_names). These are used by models that delas with classification on pairs of sentences or question answering ([See](https://huggingface.co/docs/transformers/glossary#token-type-ids)).\n",
        "# @markdown * `attention_mask` — List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if “attention_mask” is in self.model_input_names) ([See](https://huggingface.co/docs/transformers/glossary#attention-mask)).\n",
        "# @markdown * `overflowing_tokens` — List of overflowing tokens sequences (when a max_length is specified and `return_overflowing_tokens=True`).\n",
        "# @markdown * `num_truncated_tokens` — num_truncated_tokens — Number of tokens truncated (when a max_length is specified and `return_overflowing_tokens=True`).\n",
        "# @markdown * `special_tokens_mask` — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).\n",
        "# @markdown * `length` — The length of the inputs (when return_length=True)\n",
        "\n",
        "print(\"Let's see the default behaiver:\")\n",
        "print(\"GPT-2 tokenizer: \", tokenizer(\"Hello, world!\"))\n",
        "print(\"LLaMA tokenizer: \", llama_tokenizer(\"Hello, world!\"))\n",
        "print(\"\")\n",
        "\n",
        "print(\"Now, setting 'max_length=4', 'truncation=True, 'return_overflowing_tokens=True' and 'return_length=True':\")\n",
        "print(\n",
        "    \"GPT-2 tokenizer: \",\n",
        "    tokenizer(\n",
        "        \"Hello, world!\",\n",
        "        max_length=4, truncation=True, return_overflowing_tokens=True))\n",
        "print(\n",
        "    \"LLaMA tokenizer: \",\n",
        "    llama_tokenizer(\n",
        "        \"Hello, world!\",\n",
        "        max_length=4, truncation=True, return_overflowing_tokens=True))\n"
      ],
      "metadata": {
        "id": "1pcQ6fuA9lQ8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "524a8c04-0c69-4549-ec2b-ea2ccbbadb5c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Let's see the default behaiver:\n",
            "GPT-2 tokenizer:  {'input_ids': [15496, 11, 995, 0], 'attention_mask': [1, 1, 1, 1]}\n",
            "LLaMA tokenizer:  {'input_ids': [1, 15043, 29892, 3186, 29991], 'attention_mask': [1, 1, 1, 1, 1]}\n",
            "\n",
            "Now, setting 'max_length=4', 'truncation=True, 'return_overflowing_tokens=True' and 'return_length=True':\n",
            "GPT-2 tokenizer:  {'input_ids': [[15496, 11, 995, 0]], 'attention_mask': [[1, 1, 1, 1]], 'overflow_to_sample_mapping': [0]}\n",
            "LLaMA tokenizer:  {'overflowing_tokens': [29991], 'num_truncated_tokens': 1, 'input_ids': [1, 15043, 29892, 3186], 'attention_mask': [1, 1, 1, 1]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tokanizers in Training\n",
        "\n",
        "Here, we analyze how the tokenizers participated in the training of some open-source language models."
      ],
      "metadata": {
        "id": "CaBkLNHTD-Hx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Alpaca-LoRA\n",
        "\n",
        "From https://github.com/tloen/alpaca-lora/blob/65fb8225c09af81feb5edb1abb12560f02930703/finetune.py."
      ],
      "metadata": {
        "id": "yzxjUgIjsHQJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Sample data and default values\n",
        "\n",
        "For simulating the dataset and hyperparams on training."
      ],
      "metadata": {
        "id": "85qNeTpoITsS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cutoff_len = 512\n",
        "\n",
        "sample_aplaca_lora_user_prompt = \"\"\"\n",
        "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "Find the capital of Spain.\n",
        "\n",
        "### Response:\n",
        "\"\"\"\n",
        "\n",
        "sample_aplaca_lora_full_prompt = \"\"\"\n",
        "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "Find the capital of Spain.\n",
        "\n",
        "### Response:\n",
        "The capital of Spain is Madrid.\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "eZ1fp6NsEKLa"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Setup the tokenizer\n",
        "\n",
        "Code below are copied from https://github.com/tloen/alpaca-lora/blob/65fb8225/finetune.py#L126.\n"
      ],
      "metadata": {
        "id": "6fU6YE6GIjNh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alpaca_lora_tokenizer = LlamaTokenizer.from_pretrained(llama_model_name)"
      ],
      "metadata": {
        "id": "Mdc-bFDLFMEi"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Not sure why this is needed. The default seems to be `None`.\n",
        "\n",
        "alpaca_lora_tokenizer.pad_token_id = (\n",
        "    0  # unk. we want this to be different from the eos token\n",
        ")"
      ],
      "metadata": {
        "id": "8uORZOqqEyD5"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Not sure why this is needed. The default seems to be `'right'`.\n",
        "\n",
        "alpaca_lora_tokenizer.padding_side = \"left\"  # Allow batched inference"
      ],
      "metadata": {
        "id": "r8Hyz9KAFXD4"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Not sure why this is all 0, it seems to be 1, 2, 0 regarding to the [generation script](https://github.com/tloen/alpaca-lora/blob/630d114/generate.py#L76-L78). <br />\n",
        "# @markdown Update: using `huggyllama/llama-7b` instead of `decapoda-research/llama-7b-hf` fixes this.\n",
        "\n",
        "print(alpaca_lora_tokenizer.bos_token_id)\n",
        "print(alpaca_lora_tokenizer.eos_token_id)\n",
        "print(alpaca_lora_tokenizer.unk_token_id)"
      ],
      "metadata": {
        "id": "4friKHPHOOfZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea817b6e-c8f3-48fa-99c8-7a05e4373403"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### How the tokenizer works in alpaca-lora fine-tuning source code"
      ],
      "metadata": {
        "id": "k18eJRvlmmWe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown From https://github.com/tloen/alpaca-lora/blob/65fb8225/finetune.py#L126.<br />\n",
        "# @markdown * `truncation=True` and `max_length=cutoff_len` is being set to ensure the tokenized prompt is no longer than the cutoff length.<br />\n",
        "# @markdown * I think `padding=False` is set because the padding will be done by the [`transformers.DataCollatorForSeq2Seq` Data Collactor](https://huggingface.co/docs/transformers/v4.28.1/en/main_classes/data_collator#transformers.DataCollatorForSeq2Seq), which is passed to the trainer later and will dynamically pad the inputs received, as well as the labels.\n",
        "# @markdown * `return_tensors=None` is set also because `transformers.DataCollatorForSeq2Seq` is in charge of handling this later, alough `None` seems to be also the default value making this redundant. (Set it to 'pt' will let the tokenizer return PyTorch torch.Tensor objects.)\n",
        "# @markdown * As the comment, \"there's probably a way to do this with the tokenizer settings\", it seems that using the [`TemplateProcessing` post-processor](https://huggingface.co/docs/tokenizers/v0.13.3/en/api/post-processors#tokenizers.processors.TemplateProcessing) can achieve this (adding the eos token at the end of input), but I can't find a way to add [post-processors](https://huggingface.co/docs/tokenizers/v0.13.3/en/pipeline) to LLaMA tokenizer or GPT tokenizer. Maybe it's not the way of using TemplateProcessing. So I guess it make sense to do this manually. \n",
        "\n",
        "def tokenize(prompt, add_eos_token=True):\n",
        "    # there's probably a way to do this with the tokenizer settings\n",
        "    # but again, gotta move fast\n",
        "    result = alpaca_lora_tokenizer(\n",
        "        prompt,\n",
        "        truncation=True,\n",
        "        max_length=cutoff_len,\n",
        "        padding=False,\n",
        "        return_tensors=None,\n",
        "    )\n",
        "    if (\n",
        "        result[\"input_ids\"][-1] != alpaca_lora_tokenizer.eos_token_id\n",
        "        and len(result[\"input_ids\"]) < cutoff_len\n",
        "        and add_eos_token\n",
        "    ):\n",
        "        result[\"input_ids\"].append(alpaca_lora_tokenizer.eos_token_id)\n",
        "        result[\"attention_mask\"].append(1)\n",
        "\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "\n",
        "    return result\n",
        "\n",
        "# Test it\n",
        "tokenize(sample_aplaca_lora_full_prompt)\n",
        "\n",
        "# Test if long input will be truncated\n",
        "# tokenize(\" a\" * (cutoff_len + 10))\n"
      ],
      "metadata": {
        "id": "y0lpjbZNFi0N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2cc4261-dd81-475e-b388-c37119db6f8f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [1, 29871, 13, 21140, 340, 338, 385, 15278, 393, 16612, 263, 3414, 29889, 14350, 263, 2933, 393, 7128, 2486, 1614, 2167, 278, 2009, 29889, 13, 13, 2277, 29937, 2799, 4080, 29901, 13, 12542, 278, 7483, 310, 13616, 29889, 13, 13, 2277, 29937, 13291, 29901, 13, 1576, 7483, 310, 13616, 338, 9669, 29889, 13, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': [1, 29871, 13, 21140, 340, 338, 385, 15278, 393, 16612, 263, 3414, 29889, 14350, 263, 2933, 393, 7128, 2486, 1614, 2167, 278, 2009, 29889, 13, 13, 2277, 29937, 2799, 4080, 29901, 13, 12542, 278, 7483, 310, 13616, 29889, 13, 13, 2277, 29937, 13291, 29901, 13, 1576, 7483, 310, 13616, 338, 9669, 29889, 13, 2]}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown From https://github.com/tloen/alpaca-lora/blob/65fb8225/finetune.py#L148. The arguments are modified so that sample data can be passed into it directly without using the prompter to generate a prompt.<br /><br />\n",
        "# @markdown The main purpose of this function (other then calling `prompter.generate_prompt`) is to deal with `train_on_inputs`, if `train_on_inputs` is set to False, then the ground truth that is used to calcualte the loss, which is `labels`, will be modified so that the \"user input\" tokens will be relpaced by `-100`.\n",
        "# @markdown > <p>In most cases, the `IGNORE_TOKEN_ID` is set to -100. This value is chosen because the PyTorch implementation of common loss functions, such as CrossEntropyLoss, is designed to ignore targets with the value -100 during loss calculation. When the loss function encounters a target with this value, it doesn't contribute to the loss, effectively making the model ignore the corresponding input tokens during training.\n",
        "# @markdown > </p> -- GPT-4\n",
        "\n",
        "def generate_and_tokenize_prompt(user_prompt, full_prompt):\n",
        "    # full_prompt = prompter.generate_prompt(\n",
        "    #     data_point[\"instruction\"],\n",
        "    #     data_point[\"input\"],\n",
        "    #     data_point[\"output\"],\n",
        "    # )\n",
        "    tokenized_full_prompt = tokenize(full_prompt)\n",
        "    if not train_on_inputs:\n",
        "        # user_prompt = prompter.generate_prompt(\n",
        "        #     data_point[\"instruction\"], data_point[\"input\"]\n",
        "        # )\n",
        "        tokenized_user_prompt = tokenize(\n",
        "            user_prompt, add_eos_token=add_eos_token\n",
        "        )\n",
        "        user_prompt_len = len(tokenized_user_prompt[\"input_ids\"])\n",
        "\n",
        "        if add_eos_token:\n",
        "            user_prompt_len -= 1\n",
        "\n",
        "        tokenized_full_prompt[\"labels\"] = [\n",
        "            -100\n",
        "        ] * user_prompt_len + tokenized_full_prompt[\"labels\"][\n",
        "            user_prompt_len:\n",
        "        ]  # could be sped up, probably\n",
        "    return tokenized_full_prompt\n",
        "\n",
        "# Test it. Here the begining of 'labels' should be \"masked\" with a lot of \"-100\".\n",
        "add_eos_token = True\n",
        "train_on_inputs = False\n",
        "generate_and_tokenize_prompt(sample_aplaca_lora_user_prompt, sample_aplaca_lora_full_prompt)"
      ],
      "metadata": {
        "id": "z9MbIP9OFtEt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a01ad8d3-b64b-43c8-c6af-377e9f10fcf7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [1, 29871, 13, 21140, 340, 338, 385, 15278, 393, 16612, 263, 3414, 29889, 14350, 263, 2933, 393, 7128, 2486, 1614, 2167, 278, 2009, 29889, 13, 13, 2277, 29937, 2799, 4080, 29901, 13, 12542, 278, 7483, 310, 13616, 29889, 13, 13, 2277, 29937, 13291, 29901, 13, 1576, 7483, 310, 13616, 338, 9669, 29889, 13, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 1576, 7483, 310, 13616, 338, 9669, 29889, 13, 2]}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown In the [source code of alpaca-lora](https://github.com/tloen/alpaca-lora/blob/65fb8225/finetune.py#L257-L259), the trainer is being passed a `transformers.DataCollatorForSeq2Seq` data collector as in this code cell.\n",
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "data_collator = DataCollatorForSeq2Seq(\n",
        "    alpaca_lora_tokenizer,\n",
        "    pad_to_multiple_of=8,\n",
        "    return_tensors=\"pt\",\n",
        "    padding=True\n",
        ")"
      ],
      "metadata": {
        "id": "XGUKoL4NF2C7"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Not sure why but we will get an error\n",
        "# @markdown > ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.\n",
        "# @markdown\n",
        "# @markdown while using the data collector directly ff we don't add the `pad_token` with `alpaca_lora_tokenizer.add_special_tokens`. This part is not seen in the source code of alpaca-lora. I guess there're some magic happening as the trainer uses the data collector, or the version of the transformers lib.\n",
        "alpaca_lora_tokenizer.add_special_tokens({'pad_token': '<unk>'})\n",
        "\n",
        "# Check\n",
        "print(alpaca_lora_tokenizer.special_tokens_map)\n",
        "print(alpaca_lora_tokenizer.pad_token_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkzPRz0HjL-i",
        "outputId": "4a79d657-4dab-40af-a4c7-6c22eb415c3f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<unk>'}\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Now, check if the inputs in the same batch (?) will be padded to have the same length.\n",
        "\n",
        "batch_encoding_1 = generate_and_tokenize_prompt(\n",
        "    sample_aplaca_lora_user_prompt,\n",
        "    sample_aplaca_lora_full_prompt\n",
        ")\n",
        "batch_encoding_2 = generate_and_tokenize_prompt(\n",
        "    'Hi',\n",
        "    'Hi there!')\n",
        "features = data_collator([batch_encoding_1, batch_encoding_2])\n",
        "\n",
        "# @markdown In the output, we can see that the shorter input has been padded with lots of `0`s. The pad token is inserted from the left because we have `alpaca_lora_tokenizer.padding_side = \"left\"`. Also, the attention masks has also been set correctly to mask out the pad tokens.\n",
        "print(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGZHARpEgxvo",
        "outputId": "85636172-4e6f-430d-ca47-0c99dc9331e6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[    0,     0,     1, 29871,    13, 21140,   340,   338,   385, 15278,\n",
            "           393, 16612,   263,  3414, 29889, 14350,   263,  2933,   393,  7128,\n",
            "          2486,  1614,  2167,   278,  2009, 29889,    13,    13,  2277, 29937,\n",
            "          2799,  4080, 29901,    13, 12542,   278,  7483,   310, 13616, 29889,\n",
            "            13,    13,  2277, 29937, 13291, 29901,    13,  1576,  7483,   310,\n",
            "         13616,   338,  9669, 29889,    13,     2],\n",
            "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     1,  6324,   727, 29991,     2]]), 'attention_mask': tensor([[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "         1, 1, 1, 1, 1, 1, 1, 1],\n",
            "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 1, 1, 1, 1, 1]]), 'labels': tensor([[ -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
            "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
            "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
            "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
            "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  1576,  7483,   310,\n",
            "         13616,   338,  9669, 29889,    13,     2],\n",
            "        [ -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
            "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
            "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
            "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
            "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
            "          -100,  -100,  -100,   727, 29991,     2]])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Now, we try to decode it back.\n",
        "print(\"features['input_ids'][0]: \")\n",
        "print(\"---\")\n",
        "print(alpaca_lora_tokenizer.decode(features['input_ids'][0]))\n",
        "print(\"---\")\n",
        "print(\"\")\n",
        "\n",
        "# @markdown `features['labels'][0]` can't be decoded directly because those `-100`s will make the tokenizer return an `IndexError: piece id is out of range.`\n",
        "# @markdown Therefore, we map them into `0`s (`<unk>`) before decoding.\n",
        "print(\"features['labels'][0]: \")\n",
        "print(\"---\")\n",
        "print(alpaca_lora_tokenizer.decode([\n",
        "    0 if id < 0 else id for id in features['labels'][0]\n",
        "]))\n",
        "print(\"---\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47wLj1cjlKMn",
        "outputId": "289f4f66-d14e-4626-88e2-47223527ee75"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "features['input_ids'][0]: \n",
            "---\n",
            "<unk><unk><s> \n",
            "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
            "\n",
            "### Instruction:\n",
            "Find the capital of Spain.\n",
            "\n",
            "### Response:\n",
            "The capital of Spain is Madrid.\n",
            "</s>\n",
            "---\n",
            "\n",
            "features['labels'][0]: \n",
            "---\n",
            "<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk> The capital of Spain is Madrid.\n",
            "</s>\n",
            "---\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experiment On Training Tokenizers\n"
      ],
      "metadata": {
        "id": "e-mmFgYoomJU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tool for inspecting tokenize results for CJK characters\n",
        "\n",
        "Since CJK characters often got splitted into multiple tokens, it's hard to see how a sentence is being tokenized by the tokenized result. For example:\n",
        "\n",
        "```python\n",
        ">>> tokenizer.tokenize('好')\n",
        "['å¥', '½']  # Two tokens for a word\n",
        "```\n",
        "\n",
        "```python\n",
        ">>> tokenizer.tokenize('你好世界！')\n",
        "['ä½', 'ł', 'å¥', '½', 'ä¸', 'ĸ', 'çķ', 'Į', 'ï', '¼', 'ģ']\n",
        "# ⬆️ ???\n",
        "```\n",
        "\n",
        "To address this, we define a function that can return full CJK words of a tokenization result, and also how many tokens are used to form that word:"
      ],
      "metadata": {
        "id": "oTjcsZk8GEIK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_cjk(tokenizer, text):\n",
        "    tokens = tokenizer.tokenize(text)\n",
        "    processed_tokens = 0\n",
        "    i = 1\n",
        "    tokens_to_form_full_word = []\n",
        "    full_word = \"\"\n",
        "    while i <= len(tokens):\n",
        "        test_tokens_to_form_full_word = tokens[processed_tokens:i]\n",
        "        test_full_word = tokenizer.convert_tokens_to_string(test_tokens_to_form_full_word)\n",
        "        if len(test_full_word) > 1:\n",
        "            if full_word:\n",
        "                # We got tokens that should belong to the next word.\n",
        "                # Yield the previous full word and reset the list.\n",
        "                yield (full_word, len(tokens_to_form_full_word))\n",
        "            else:\n",
        "                # We do not have a previous word, so this might be an English word. Yield it.\n",
        "                yield (test_full_word, len(test_tokens_to_form_full_word))\n",
        "                i += 1\n",
        "            # Reset the list of tokens to form a full word.\n",
        "            tokens_to_form_full_word = []\n",
        "            full_word = \"\"\n",
        "            # Set processed_tokens to the first token of the next word.\n",
        "            processed_tokens = i - 1\n",
        "        else:\n",
        "            tokens_to_form_full_word = test_tokens_to_form_full_word\n",
        "            full_word = test_full_word\n",
        "            i += 1  # Try to add another token to the word on the next iteration.\n",
        "    # If we have anything left, yield it.\n",
        "    if full_word:\n",
        "        yield (full_word, len(tokens_to_form_full_word))"
      ],
      "metadata": {
        "id": "4Sbj09gDHdQt"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Here we got `[('你', 2), ('好', 2), ('，', 3), ('Anna', 1), ('！', 3)]`. It means that thw word \"你\" is formed by 2 tokens, \"好\" by 2 tokens and \"，\" by 3 tokens. \"Anna\" is formed by 1 token.\n",
        "\n",
        "tokenizer = \\\n",
        "    AutoTokenizer.from_pretrained(\"gpt2\")\n",
        "list(tokenize_cjk(tokenizer, \"你好，Anna！\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20GaTE5mHjta",
        "outputId": "a605d101-b5ee-4c3a-b845-a2747ab9514c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('你', 2), ('好', 2), ('，', 3), ('Anna', 1), ('！', 3)]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prepare Train Data"
      ],
      "metadata": {
        "id": "e5zyDAXZF_Sy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iTKbT8BF6k_I",
        "outputId": "47aa92b7-eed3-48d7-99e0-335585f54682"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, aiosignal, aiohttp, datasets\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.11.0 dill-0.3.6 frozenlist-1.3.3 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "import json\n",
        "from datasets import load_dataset\n",
        "# @markdown Load the Wikipedia dataset.\n",
        "wikipedia_ds = load_dataset(\n",
        "    # \"zetavg/wikipedia_random_page_summaries_zh_tw_5k\"\n",
        "    \"zetavg/wikipedia_random_page_summaries_zh_tw_10k\"\n",
        "    # \"zetavg/wikipedia_random_page_summaries_zh_tw_100k\"\n",
        ")['train']\n",
        "\n",
        "wikipedia_ds_count = len(wikipedia_ds)\n",
        "print(\"Wikipedia data count: \", wikipedia_ds_count)\n",
        "\n",
        "def get_wikipedia_page_summaries():\n",
        "    for batch in wikipedia_ds:\n",
        "        yield batch['page_summary']\n",
        "\n",
        "# @markdown Preview it.\n",
        "first_10_items = list(itertools.islice(\n",
        "    get_wikipedia_page_summaries(), 10))\n",
        "print(json.dumps(\n",
        "    first_10_items, indent=2, ensure_ascii=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465,
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        },
        "id": "wRnmKofO6Lrr",
        "outputId": "5e00eb28-f3a2-4c7a-fe25-e255e590b8b0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/658 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
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          "name": "stdout",
          "text": [
            "Downloading and preparing dataset None/None to /root/.cache/huggingface/datasets/zetavg___parquet/zetavg--wikipedia_random_page_summaries_zh_tw_10k-bf9678c22fef2612/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
          ]
        },
        {
          "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": "f1405bc9ced447a89cd5c31f23d09382"
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          "metadata": {}
        },
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              "model_id": "3356fc15d07747c985aeffb40c1281f3"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/9997 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/zetavg___parquet/zetavg--wikipedia_random_page_summaries_zh_tw_10k-bf9678c22fef2612/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Wikipedia data count:  9997\n",
            "[\n",
            "  \"TCI（The Children Investment Fund Management），是英國投資家 Chris Hohn 創立的對沖基金。\",\n",
            "  \"高元縣，中國舊縣名。\\n1958 年 5 月由高邑縣、元氏縣兩縣合併設置高元縣，治所在今河北省元氏縣槐陽鎮。同年 12 月高元縣撤銷，恢復高邑縣、元氏縣。\",\n",
            "  \"小行星 9649（英語：9649）是一顆圍繞太陽公轉的小行星。1995 年 12 月 2 日，小林隆男在大泉天文台發現了此天體。\\n這顆小行星的絕對星等為 326.1587678462532 等。\",\n",
            "  \"紀堯姆・伊曼紐爾・德霍曼 - 克里斯托（法語：Guillaume Emmanuel de Homem-Christo，法語發音：[ɡi manɥɛl də ɔmɛm kʁisto]，1974 年 2 月 8 日 —）或稱作蓋 - 馬努爾・德霍曼 - 克里斯托（法語：Guy-Manuel de Homem-Christo），是一名法國男音樂家、音樂製作人、歌手、詞曲作家、DJ、作曲家和導演，較著名的是和湯瑪斯・本高特共同組成的樂團傻瓜龐克，這使自己和其作品都獲得了廣大的知名度。他和埃里克・謝德維爾（Eric Chédeville）為唱片公司 Crydamoure 的共同創立者，且也和切德維爾組成了名為 Le Knight Club 的團體。\\n德霍曼 - 克里斯托擁有兩名孩子。\",\n",
            "  \"NGC 1026 是鯨魚座的一個星系。\",\n",
            "  \"蓖麻毒蛋白（英語：Ricin）是從蓖麻籽中所萃取出來的一種毒性蛋白質，幾乎對所有的真核細胞都具有殺傷作用。蓖麻毒蛋白的純品是一種白色粉末或結晶體，無味，可溶於稀酸或鹽類，不溶於苯、甲苯、乙醇、乙醚、三氯甲烷等有機溶劑，乾熱時具有良好的穩定性。蓖麻毒蛋白存在多種類型，如結晶型、B - 型、D 型、E 型、T3 型、G 型等，不同類型的蓖麻毒蛋白毒性不盡相同，其中以 D 型的毒性最大。\\n此種毒素對人類的平均致死量為 0.2 毫克，但也有一些文獻記載的劑量較高。蓖麻毒蛋白具有糖苷酶活性，作用於真核細胞的核糖體 RNA，使其降解，從而阻止蛋白質合成，導致細胞的死亡，進而對生物體造成傷害。研究顯示，8 顆蓖麻種子的毒素可對一名成人產生毒性。不過在已知紀錄中，因攝取植物種子而死亡的案例並不多見。自然界中還存在某些類似蓖麻毒的毒素，例如雞母珠中的雞母珠毒素（Abrin）。\",\n",
            "  \"亞洲金融風暴北京救港傳言，是一個主要在中國大陸及親中共群體流傳的傳言，該傳言指亞洲金融風暴期間，香港股市受到索羅斯為首的國際投機者狙擊，香港特區政府依靠中華人民共和國中央政府（通稱北京政府）幫助成功阻止索羅斯等的行動。但在香港，因對此事的廣泛認知是香港政府自行以外匯儲備穩定股票市場及聯繫匯率，當時主事之政府官員及主流傳媒均未有提及中國政府有實質、直接的協助，該傳言被質疑是虛假訊息。\",\n",
            "  \"楊樓站是一個隴海線上的鐵路車站，位於安徽省宿州市蕭縣楊樓鎮，建於 1915 年，目前為四等站，郵政編碼為 235221。目前客運：辦理旅客乘降；行李、包裹託運；貨運：辦理整車貨物發到；不辦理危險貨物發到。\",\n",
            "  \"紅領巾水庫是中國河北省石家莊市行唐縣境內的一座水庫，位於大清河系沙河上，建於 1958 年。水庫正常庫容為 2120 萬立方米，集雨面積為 73 平方千米，海拔為 167 米。\",\n",
            "  \"噬母（英語：Matriphagy）是生物體食用自己母體或母體局部的一種現象。這種現象經常會在蜘蛛身上出現。目前已知的四萬多種蜘蛛中，噬母行為主要見於以下幾類：管巢蛛科紅螯蛛屬、隆頭蛛科穹蛛屬、暗蛛科暗蛛屬和蟹蛛科狩蛛屬。除此之外，在一些蚓螈目動物中，也發現了這種現象。這些蚓螈目動物的幼體在出生後會啃食母體的皮膚來獲取營養，不過由於皮膚可以更新，所以這種噬母行為不會導致母體死亡。而蜘蛛往往是將活著的母體全部吃下。\"\n",
            "]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Load vocab from moedict.\n",
        "\n",
        "import requests\n",
        "import json\n",
        "import pandas as pd\n",
        "from google.colab import data_table\n",
        "data_table.enable_dataframe_formatter()\n",
        "\n",
        "moedict_data_cat_response = requests.get(\n",
        "    \"https://raw.githubusercontent.com/g0v/moedict-data/master/dict-cat.json\")\n",
        "moedict_data_cat = json.loads(moedict_data_cat_response.text)\n",
        "# @markdown Display a table of categories and their sample entries.\n",
        "cat_and_sample_entries = [\n",
        "    {\n",
        "        'name': cat['name'],\n",
        "     'entries_count': len(cat['entries']),\n",
        "        'sample_entries': (\", \").join(cat['entries'][:10]),\n",
        "    } for cat in moedict_data_cat]\n",
        "display(\n",
        "    pd.DataFrame.from_dict(cat_and_sample_entries, orient='columns'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "B-HOQnZh9Kl2",
        "outputId": "bba81110-2bdd-40ca-9626-1a9cd2759ed1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    name  entries_count                                     sample_entries\n",
              "0     成語           3008  八千里路雲和月, 八子七婿, 拔本塞原, 拔茅連茹, 拔來報往, 拔葵去織, 拔薤, 拔幟易...\n",
              "1     諺語            891  八輩兒五沒根基, 八竿子打不著, 八棍子撂不著, 拔了蘿蔔地皮寬, 撥火又長，拄門又短, 鵓...\n",
              "2    歇後語            323  八仙桌上擺夜壺, 八十年不下雨, 八十歲學吹鼓手, 被窩裡放屁, 包黑臉斷案子, 斑鳩跌彈,...\n",
              "3     音譯            484           扒魯, 巴, 巴波亞, 巴力門, 巴剎, 巴士, 巴茲卡, 巴爾, 吧, 芭蕾舞\n",
              "4     義譯            112  白皮書, 白領, 白領階級, 筆記型電腦, 配接卡, 魔術數字, 模擬器, 媒體, 免費軟體...\n",
              "..   ...            ...                                                ...\n",
              "61  股票術語             96       拔檔, 寶塔線, 本利比, 本益比, 崩盤, 丙種經紀人, 補空, 盤面, 盤整, 騙線\n",
              "62  大陸用語            633          芭賽, 把口, 把場, 撥改貸, 掰了, 白班, 白條, 白案, 白衣教練, 白煙\n",
              "63    量詞            434                      巴, 巴爾, 撥, 波, 杯, 盃, 倍, 輩, 包, 抱\n",
              "64    節氣             34            八節, 白露, 芒種, 大寒, 大雪, 大暑, 冬至, 太陰曆, 農曆, 立冬\n",
              "65    節日             29  美術節, 佛誕節, 佛誕日, 逢年過節, 逢時按節, 逢時遇節, 復活節, 端午節, 年節,...\n",
              "\n",
              "[66 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-877336ef-2228-49b8-a02b-c42d47cd0fcc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>entries_count</th>\n",
              "      <th>sample_entries</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>成語</td>\n",
              "      <td>3008</td>\n",
              "      <td>八千里路雲和月, 八子七婿, 拔本塞原, 拔茅連茹, 拔來報往, 拔葵去織, 拔薤, 拔幟易...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>諺語</td>\n",
              "      <td>891</td>\n",
              "      <td>八輩兒五沒根基, 八竿子打不著, 八棍子撂不著, 拔了蘿蔔地皮寬, 撥火又長，拄門又短, 鵓...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>歇後語</td>\n",
              "      <td>323</td>\n",
              "      <td>八仙桌上擺夜壺, 八十年不下雨, 八十歲學吹鼓手, 被窩裡放屁, 包黑臉斷案子, 斑鳩跌彈,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>音譯</td>\n",
              "      <td>484</td>\n",
              "      <td>扒魯, 巴, 巴波亞, 巴力門, 巴剎, 巴士, 巴茲卡, 巴爾, 吧, 芭蕾舞</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>義譯</td>\n",
              "      <td>112</td>\n",
              "      <td>白皮書, 白領, 白領階級, 筆記型電腦, 配接卡, 魔術數字, 模擬器, 媒體, 免費軟體...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>股票術語</td>\n",
              "      <td>96</td>\n",
              "      <td>拔檔, 寶塔線, 本利比, 本益比, 崩盤, 丙種經紀人, 補空, 盤面, 盤整, 騙線</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>大陸用語</td>\n",
              "      <td>633</td>\n",
              "      <td>芭賽, 把口, 把場, 撥改貸, 掰了, 白班, 白條, 白案, 白衣教練, 白煙</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>量詞</td>\n",
              "      <td>434</td>\n",
              "      <td>巴, 巴爾, 撥, 波, 杯, 盃, 倍, 輩, 包, 抱</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>節氣</td>\n",
              "      <td>34</td>\n",
              "      <td>八節, 白露, 芒種, 大寒, 大雪, 大暑, 冬至, 太陰曆, 農曆, 立冬</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>節日</td>\n",
              "      <td>29</td>\n",
              "      <td>美術節, 佛誕節, 佛誕日, 逢年過節, 逢時按節, 逢時遇節, 復活節, 端午節, 年節,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>66 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-877336ef-2228-49b8-a02b-c42d47cd0fcc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-877336ef-2228-49b8-a02b-c42d47cd0fcc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-877336ef-2228-49b8-a02b-c42d47cd0fcc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/99dac6621f6ae8c4/data_table.js\";\n\n      window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n\"\\u6210\\u8a9e\",\n{\n            'v': 3008,\n            'f': \"3008\",\n        },\n\"\\u516b\\u5343\\u91cc\\u8def\\u96f2\\u548c\\u6708, \\u516b\\u5b50\\u4e03\\u5a7f, \\u62d4\\u672c\\u585e\\u539f, \\u62d4\\u8305\\u9023\\u8339, \\u62d4\\u4f86\\u5831\\u5f80, \\u62d4\\u8475\\u53bb\\u7e54, \\u62d4\\u85a4, \\u62d4\\u5e5f\\u6613\\u5e5f, \\u62d4\\u5c71\\u84cb\\u4e16, \\u62d4\\u5c71\\u625b\\u9f0e\"],\n [{\n            'v': 1,\n            'f': \"1\",\n        },\n\"\\u8afa\\u8a9e\",\n{\n            'v': 891,\n            'f': \"891\",\n        },\n\"\\u516b\\u8f29\\u5152\\u4e94\\u6c92\\u6839\\u57fa, \\u516b\\u7aff\\u5b50\\u6253\\u4e0d\\u8457, \\u516b\\u68cd\\u5b50\\u6482\\u4e0d\\u8457, 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\\u4f5b\\u8a95\\u65e5, \\u9022\\u5e74\\u904e\\u7bc0, \\u9022\\u6642\\u6309\\u7bc0, \\u9022\\u6642\\u9047\\u7bc0, \\u5fa9\\u6d3b\\u7bc0, \\u7aef\\u5348\\u7bc0, \\u5e74\\u7bc0, \\u52de\\u52d5\\u7bc0\"]],\n        columns: [[\"number\", \"index\"], [\"string\", \"name\"], [\"number\", \"entries_count\"], [\"string\", \"sample_entries\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    "
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Select only some categories from moedict data so that we won't have too many words:\n"
      ],
      "metadata": {
        "id": "3LgYcsKVOOsG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selected_categories = [\n",
        "    '量詞',\n",
        "    # '成語',\n",
        "    '音譯', '義譯', '音義合譯',\n",
        "    '動物名', '植物名',\n",
        "    # '微生物',\n",
        "    '稱謂', '職官名',\n",
        "    # '節氣',\n",
        "    # '節日',\n",
        "    '國名', \n",
        "    # '朝代名', '帝號',\n",
        "    '人名',  \n",
        "    # '種族、民族',\n",
        "    '地名', '州名', '省名', '城市名', '縣名', '鄉鎮名', '郡名', '島名', '半島名', '群島名',\n",
        "    '方位名', '山名', '山脈名', '山峰名', '河川名', '湖泊名', '海洋名', '海峽名', '海灣名', '運河名', '水庫名',\n",
        "    # '星名',\n",
        "    '星座名',\n",
        "    # '書名', '書體名', '文體名',\n",
        "    # '詩名', '詞牌名', '曲牌名', \n",
        "    '樂曲名', '樂器名', \n",
        "    # '戲劇曲藝', '雜劇', '傳奇',\n",
        "    '舞曲舞蹈', '球類',\n",
        "    # '神話',\n",
        "    # '武器名',\n",
        "    # '病名',\n",
        "    # '股票術語',\n",
        "]\n",
        "filtered_moedict_data_cat = [\n",
        "    cat for cat in moedict_data_cat\n",
        "    if cat['name'] in selected_categories]\n",
        "print(\"Selected categories: \", len(filtered_moedict_data_cat))\n",
        "filtered_moedict_entries = [\n",
        "    entry\n",
        "    for cat in filtered_moedict_data_cat\n",
        "    for entry in cat['entries']]\n",
        "print(\"Entries: \", len(filtered_moedict_entries))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lO4Z9U9UCi40",
        "outputId": "34ca443f-6c7a-4cc7-c8bf-18a2b7547a9b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected categories:  36\n",
            "Entries:  10412\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Mix moedict entries and random wikipedia page summaries for training.\n",
        "import itertools\n",
        "import random\n",
        "\n",
        "word_connectors = [\"和\", \"的\", \"及\", \"以及\", \"與\", \"或\", \"或者\", \"跟\", \"既\", \"又\", \"還\", \"還有\",\n",
        "                   \"而\", \"而且\", \"同\", \"並\", \"並且\", \"即\", \"就\", \"總之\", \"因此\", \"如\", \"若\", \"若是\", \"假若\", \"假如\"]\n",
        "# cycling_word_connectors = itertools.cycle(word_connectors)\n",
        "\n",
        "def get_training_words_text():\n",
        "    # return (\"\").join([\n",
        "    #     f\"{word}{connector}\" for word, connector\n",
        "    #     in list(\n",
        "    #         zip(filtered_moedict_entries, cycling_word_connectors)\n",
        "    #     )])\n",
        "    return (\"\").join([\n",
        "        f\"{word}{random.choice(word_connectors)}\" for word\n",
        "        in filtered_moedict_entries])\n",
        "\n",
        "print(\"Sample training words text: \", get_training_words_text()[:100])\n",
        "print(\"Sample wikipedia page_summary: \", wikipedia_ds[0]['page_summary'])\n",
        "\n",
        "# word_text_iterations = int(wikipedia_ds_count / 20)\n",
        "word_text_iterations = 100\n",
        "\n",
        "training_corpus_count = word_text_iterations + wikipedia_ds_count\n",
        "print(\"Training corpus count: \", training_corpus_count)\n",
        "\n",
        "def get_training_corpus():\n",
        "    for _ in range(word_text_iterations):\n",
        "        yield get_training_words_text()\n",
        "    for batch in wikipedia_ds:\n",
        "        yield batch['page_summary']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsgbuA7oFpC4",
        "outputId": "ee55ecb6-0f37-4b30-eddc-f3b4118b42de"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample training words text:  扒魯的巴總之巴波亞及巴力門並且巴剎假若巴士就巴茲卡並且巴爾總之吧因此芭蕾舞即波特酒即波雷羅舞曲或者波羅蜜而波羅提木叉假如波羅夷及波爾卡舞及般若的泊車還柏青哥和勃露斯即白蘭地就百靈舌又百事可樂或者拜拜就\n",
            "Sample wikipedia page_summary:  TCI（The Children Investment Fund Management），是英國投資家 Chris Hohn 創立的對沖基金。\n",
            "Training corpus count:  10097\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prepare Tokenizer and Train"
      ],
      "metadata": {
        "id": "WE46hoScWxVA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Prepare tokenizer for training\n",
        "old_tokenizer = \\\n",
        "    AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6b')\n",
        "old_tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298,
          "referenced_widgets": [
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        },
        "id": "sYYpFqkRjoLn",
        "outputId": "ab6e8db6-28df-4965-b170-0824e06fd742"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/619 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70ec8458d0dc46b48ef64ad3e8b6088e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab3c7b0bcf514adbbb8ee3967dd52653"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00d68b38d3184b30a2555395118f1f12"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.37M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78a6dfd2439e4bb8a892849587739068"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/4.04k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e7785f99e2942c19c11ce2e59143156"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/357 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8c4ad3a5e0f45da88b617084d72ed66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2TokenizerFast(name_or_path='EleutherAI/gpt-j-6b', vocab_size=50257, model_max_length=2048, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True)}, clean_up_tokenization_spaces=True)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Check how the tokenizer behaves.\n",
        "import json\n",
        "example_text = \"網際網路（英語：Internet）是指 20 世紀末期興起電腦網路與電腦網路之間所串連成的龐大網路系統。\"\n",
        "# @markdown This is not an efficient tokenizer for Chinese words, as most of the word are broken into 2 ~ 3 tokens. The phrases \"網際網路\" occupies 11 tokens in total.\n",
        "print(list(tokenize_cjk(old_tokenizer, example_text)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RshkJON1j71Y",
        "outputId": "1b7d3fcb-117e-466f-839a-b8fd113dc094"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('網', 3), ('際', 2), ('網', 3), ('路', 3), ('（', 3), ('英', 3), ('語', 2), ('：', 3), ('Internet', 1), ('）', 3), ('是', 1), ('指', 3), (' 20', 1), (' ', 1), ('世', 2), ('紀', 3), ('末', 2), ('期', 2), ('�', 1), ('��', 1), ('�', 1), ('�', 1), ('電', 2), ('腦', 3), ('網', 3), ('路', 3), ('與', 2), ('電', 2), ('腦', 3), ('網', 3), ('路', 3), ('之', 1), ('間', 2), ('所', 2), ('串', 2), ('連', 2), ('成', 2), ('的', 1), ('龐', 2), ('大', 1), ('網', 3), ('路', 3), ('系', 3), ('統', 3), ('。', 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"old_tokenizer.vocab_size: \", old_tokenizer.vocab_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iF7wi2mmxz73",
        "outputId": "5cd323e4-d7e9-46c3-b1b4-c7ef44343655"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "old_tokenizer.vocab_size:  50257\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocabs_to_add = 20000\n",
        "\n",
        "# @markdown Train a new tokenizer with `zh-tw` corpus.\n",
        "new_trained_tokenizer = old_tokenizer.train_new_from_iterator(\n",
        "    get_training_corpus(), \n",
        "    vocab_size=vocabs_to_add,\n",
        "    length=training_corpus_count\n",
        ")"
      ],
      "metadata": {
        "id": "8m9DlbmHWnA0"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Merge the new trained tokenizer with the original tokenizer to form a new tokenizer.\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "old_tokenizer.save_pretrained('/tmp/old_tokenizer')\n",
        "new_trained_tokenizer.save_pretrained('/tmp/new_trained_tokenizer')\n",
        "os.makedirs(\"/tmp/merged_tokenizer\", exist_ok=True)\n",
        "\n",
        "shutil.copy(\"/tmp/old_tokenizer/tokenizer_config.json\", \"/tmp/merged_tokenizer/tokenizer_config.json\")\n",
        "shutil.copy(\"/tmp/old_tokenizer/special_tokens_map.json\", \"/tmp/merged_tokenizer/special_tokens_map.json\")\n",
        "# ValueError: Non-consecutive added token '<|extratoken_1|>' found. Should have index 51693 but has index 50257 in saved vocabulary.\n",
        "# shutil.copy(\"/tmp/old_tokenizer/added_tokens.json\", \"/tmp/merged_tokenizer/added_tokens.json\")\n",
        "\n",
        "vocab = json.load(open('/tmp/old_tokenizer/vocab.json'))\n",
        "new_vocab = json.load(open('/tmp/new_trained_tokenizer/vocab.json'))\n",
        "idx = max(list(vocab.values())) + 1\n",
        "for word in new_vocab.keys():\n",
        "    if word not in vocab.keys():\n",
        "        vocab[word] = idx\n",
        "        idx += 1\n",
        "\n",
        "with open('/tmp/merged_tokenizer/vocab.json', 'w') as f:\n",
        "    json.dump(vocab, f, ensure_ascii=False)\n",
        "print(\"New vocab size: \", len(vocab.values()))\n",
        "\n",
        "with open('/tmp/old_tokenizer/merges.txt', 'r') as original_merges,\\\n",
        "     open('/tmp/new_trained_tokenizer/merges.txt', 'r') as new_merges,\\\n",
        "     open('/tmp/merged_tokenizer/merges.txt', 'w') as output_merges:\n",
        "\n",
        "    output_merges.write(original_merges.read())\n",
        "    lines = new_merges.readlines()[1:]\n",
        "    output_merges.writelines(lines)\n",
        "\n",
        "new_tokenizer = AutoTokenizer.from_pretrained('/tmp/merged_tokenizer')\n",
        "new_tokenizer"
      ],
      "metadata": {
        "id": "3ymJL-s7XT08",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c1b0ef8-6725-43f4-dcb2-e5bfb0e1bc7f"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New vocab size:  68964\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2TokenizerFast(name_or_path='/tmp/merged_tokenizer', vocab_size=68964, model_max_length=2048, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True)}, clean_up_tokenization_spaces=True)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Results"
      ],
      "metadata": {
        "id": "wT4nplWsWzxG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "print(\"Old vocab size: \", old_tokenizer.vocab_size)\n",
        "print(\"New vocab size: \", new_tokenizer.vocab_size)\n",
        "\n",
        "old_vocab = old_tokenizer.vocab\n",
        "new_vocab = new_tokenizer.vocab\n",
        "modified_vocab_table = []\n",
        "for key, value in old_vocab.items():\n",
        "    if key not in new_vocab:\n",
        "        modified_vocab_table.append([key, value, None])\n",
        "    elif value != new_vocab[key]:\n",
        "        modified_vocab_table.append([key, value, new_vocab[key]])\n",
        "modified_vocab_count = len(modified_vocab_table)\n",
        "if modified_vocab_count:\n",
        "    print(f\"{modified_vocab_count} modified vocabs.\")\n",
        "    # print(json.dumps(modified_vocab_table, indent=2))\n",
        "    df = pd.DataFrame(modified_vocab_table, columns=['Vocab', 'Original ID', 'New ID'])\n",
        "    display(df)"
      ],
      "metadata": {
        "id": "Kgncn-Il_Rjk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 691
        },
        "outputId": "d13a180d-36b7-4a5a-be97-304d74eb0fdc"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Old vocab size:  50257\n",
            "New vocab size:  68964\n",
            "143 modified vocabs.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                  Vocab  Original ID New ID\n",
              "0     <|extratoken_12|>        50268   None\n",
              "1     <|extratoken_66|>        50322   None\n",
              "2    <|extratoken_114|>        50370   None\n",
              "3      <|extratoken_2|>        50258   None\n",
              "4     <|extratoken_73|>        50329   None\n",
              "..                  ...          ...    ...\n",
              "138  <|extratoken_122|>        50378   None\n",
              "139   <|extratoken_79|>        50335   None\n",
              "140   <|extratoken_77|>        50333   None\n",
              "141   <|extratoken_56|>        50312   None\n",
              "142   <|extratoken_26|>        50282   None\n",
              "\n",
              "[143 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-401b0fc9-af5a-48bf-ac0d-5d8e4201a105\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Vocab</th>\n",
              "      <th>Original ID</th>\n",
              "      <th>New ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;|extratoken_12|&gt;</td>\n",
              "      <td>50268</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;|extratoken_66|&gt;</td>\n",
              "      <td>50322</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>&lt;|extratoken_114|&gt;</td>\n",
              "      <td>50370</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>&lt;|extratoken_2|&gt;</td>\n",
              "      <td>50258</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>&lt;|extratoken_73|&gt;</td>\n",
              "      <td>50329</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>&lt;|extratoken_122|&gt;</td>\n",
              "      <td>50378</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>&lt;|extratoken_79|&gt;</td>\n",
              "      <td>50335</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>&lt;|extratoken_77|&gt;</td>\n",
              "      <td>50333</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>&lt;|extratoken_56|&gt;</td>\n",
              "      <td>50312</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>&lt;|extratoken_26|&gt;</td>\n",
              "      <td>50282</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>143 rows × 3 columns</p>\n",
              "</div>\n",
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50258,\n            'f': \"50258\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 4,\n            'f': \"4\",\n        },\n\"<|extratoken_73|>\",\n{\n            'v': 50329,\n            'f': \"50329\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 5,\n            'f': \"5\",\n        },\n\"<|extratoken_128|>\",\n{\n            'v': 50384,\n            'f': \"50384\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 6,\n            'f': \"6\",\n        },\n\"<|extratoken_139|>\",\n{\n            'v': 50395,\n            'f': \"50395\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 7,\n            'f': \"7\",\n        },\n\"<|extratoken_63|>\",\n{\n            'v': 50319,\n            'f': \"50319\",\n        },\n{\n            'v': null,\n            'f': 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},\n\"<|extratoken_104|>\",\n{\n            'v': 50360,\n            'f': \"50360\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 13,\n            'f': \"13\",\n        },\n\"<|extratoken_70|>\",\n{\n            'v': 50326,\n            'f': \"50326\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 14,\n            'f': \"14\",\n        },\n\"<|extratoken_72|>\",\n{\n            'v': 50328,\n            'f': \"50328\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 15,\n            'f': \"15\",\n        },\n\"<|extratoken_103|>\",\n{\n            'v': 50359,\n            'f': \"50359\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 16,\n            'f': \"16\",\n        },\n\"<|extratoken_28|>\",\n{\n            'v': 50284,\n            'f': \"50284\",\n        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21,\n            'f': \"21\",\n        },\n\"<|extratoken_98|>\",\n{\n            'v': 50354,\n            'f': \"50354\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 22,\n            'f': \"22\",\n        },\n\"<|extratoken_97|>\",\n{\n            'v': 50353,\n            'f': \"50353\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 23,\n            'f': \"23\",\n        },\n\"<|extratoken_52|>\",\n{\n            'v': 50308,\n            'f': \"50308\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 24,\n            'f': \"24\",\n        },\n\"<|extratoken_131|>\",\n{\n            'v': 50387,\n            'f': \"50387\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 25,\n            'f': \"25\",\n        },\n\"<|extratoken_117|>\",\n{\n            'v': 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\"null\",\n        }],\n [{\n            'v': 52,\n            'f': \"52\",\n        },\n\"<|extratoken_140|>\",\n{\n            'v': 50396,\n            'f': \"50396\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 53,\n            'f': \"53\",\n        },\n\"<|extratoken_23|>\",\n{\n            'v': 50279,\n            'f': \"50279\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 54,\n            'f': \"54\",\n        },\n\"<|extratoken_16|>\",\n{\n            'v': 50272,\n            'f': \"50272\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 55,\n            'f': \"55\",\n        },\n\"<|extratoken_42|>\",\n{\n            'v': 50298,\n            'f': \"50298\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 56,\n            'f': \"56\",\n        },\n\"<|extratoken_102|>\",\n{\n            'v': 50358,\n            'f': \"50358\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 57,\n            'f': \"57\",\n        },\n\"<|extratoken_64|>\",\n{\n            'v': 50320,\n            'f': \"50320\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 58,\n            'f': \"58\",\n        },\n\"<|extratoken_126|>\",\n{\n            'v': 50382,\n            'f': \"50382\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 59,\n            'f': \"59\",\n        },\n\"<|extratoken_143|>\",\n{\n            'v': 50399,\n            'f': \"50399\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 60,\n            'f': \"60\",\n        },\n\"<|extratoken_11|>\",\n{\n            'v': 50267,\n            'f': \"50267\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 61,\n            'f': \"61\",\n        },\n\"<|extratoken_95|>\",\n{\n            'v': 50351,\n            'f': \"50351\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 62,\n            'f': \"62\",\n        },\n\"<|extratoken_33|>\",\n{\n            'v': 50289,\n            'f': \"50289\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 63,\n            'f': \"63\",\n        },\n\"<|extratoken_118|>\",\n{\n            'v': 50374,\n            'f': \"50374\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 64,\n            'f': \"64\",\n        },\n\"<|extratoken_51|>\",\n{\n            'v': 50307,\n            'f': \"50307\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 65,\n            'f': \"65\",\n        },\n\"<|extratoken_21|>\",\n{\n            'v': 50277,\n            'f': \"50277\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 66,\n            'f': \"66\",\n        },\n\"<|extratoken_124|>\",\n{\n            'v': 50380,\n            'f': \"50380\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 67,\n            'f': \"67\",\n        },\n\"<|extratoken_27|>\",\n{\n            'v': 50283,\n            'f': \"50283\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 68,\n            'f': \"68\",\n        },\n\"<|extratoken_55|>\",\n{\n            'v': 50311,\n            'f': \"50311\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 69,\n            'f': \"69\",\n        },\n\"<|extratoken_60|>\",\n{\n            'v': 50316,\n            'f': \"50316\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 70,\n            'f': \"70\",\n        },\n\"<|extratoken_82|>\",\n{\n            'v': 50338,\n            'f': \"50338\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 71,\n            'f': \"71\",\n        },\n\"<|extratoken_86|>\",\n{\n            'v': 50342,\n            'f': \"50342\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 72,\n            'f': \"72\",\n        },\n\"<|extratoken_62|>\",\n{\n            'v': 50318,\n            'f': \"50318\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 73,\n            'f': \"73\",\n        },\n\"<|extratoken_96|>\",\n{\n            'v': 50352,\n            'f': \"50352\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 74,\n            'f': \"74\",\n        },\n\"<|extratoken_111|>\",\n{\n            'v': 50367,\n            'f': \"50367\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 75,\n            'f': \"75\",\n        },\n\"<|extratoken_89|>\",\n{\n            'v': 50345,\n            'f': \"50345\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 76,\n            'f': \"76\",\n        },\n\"<|extratoken_5|>\",\n{\n            'v': 50261,\n            'f': \"50261\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 77,\n            'f': \"77\",\n        },\n\"<|extratoken_75|>\",\n{\n            'v': 50331,\n            'f': \"50331\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 78,\n            'f': \"78\",\n        },\n\"<|extratoken_108|>\",\n{\n            'v': 50364,\n            'f': \"50364\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 79,\n            'f': \"79\",\n        },\n\"<|extratoken_39|>\",\n{\n            'v': 50295,\n            'f': \"50295\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 80,\n            'f': \"80\",\n        },\n\"<|extratoken_113|>\",\n{\n            'v': 50369,\n            'f': \"50369\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 81,\n            'f': \"81\",\n        },\n\"<|extratoken_47|>\",\n{\n            'v': 50303,\n            'f': \"50303\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 82,\n            'f': \"82\",\n        },\n\"<|extratoken_134|>\",\n{\n            'v': 50390,\n            'f': \"50390\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 83,\n            'f': \"83\",\n        },\n\"<|extratoken_125|>\",\n{\n            'v': 50381,\n            'f': \"50381\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 84,\n            'f': \"84\",\n        },\n\"<|extratoken_90|>\",\n{\n            'v': 50346,\n            'f': \"50346\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 85,\n            'f': \"85\",\n        },\n\"<|extratoken_1|>\",\n{\n            'v': 50257,\n            'f': \"50257\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 86,\n            'f': \"86\",\n        },\n\"<|extratoken_106|>\",\n{\n            'v': 50362,\n            'f': \"50362\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 87,\n            'f': \"87\",\n        },\n\"<|extratoken_36|>\",\n{\n            'v': 50292,\n            'f': \"50292\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 88,\n            'f': \"88\",\n        },\n\"<|extratoken_40|>\",\n{\n            'v': 50296,\n            'f': \"50296\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 89,\n            'f': \"89\",\n        },\n\"<|extratoken_10|>\",\n{\n            'v': 50266,\n            'f': \"50266\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 90,\n            'f': \"90\",\n        },\n\"<|extratoken_58|>\",\n{\n            'v': 50314,\n            'f': \"50314\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 91,\n            'f': \"91\",\n        },\n\"<|extratoken_115|>\",\n{\n            'v': 50371,\n            'f': \"50371\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 92,\n            'f': \"92\",\n        },\n\"<|extratoken_87|>\",\n{\n            'v': 50343,\n            'f': \"50343\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 93,\n            'f': \"93\",\n        },\n\"<|extratoken_67|>\",\n{\n            'v': 50323,\n            'f': \"50323\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 94,\n            'f': \"94\",\n        },\n\"<|extratoken_93|>\",\n{\n            'v': 50349,\n            'f': \"50349\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 95,\n            'f': \"95\",\n        },\n\"<|extratoken_135|>\",\n{\n            'v': 50391,\n            'f': \"50391\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 96,\n            'f': \"96\",\n        },\n\"<|extratoken_78|>\",\n{\n            'v': 50334,\n            'f': \"50334\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 97,\n            'f': \"97\",\n        },\n\"<|extratoken_129|>\",\n{\n            'v': 50385,\n            'f': \"50385\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 98,\n            'f': \"98\",\n        },\n\"<|extratoken_13|>\",\n{\n            'v': 50269,\n            'f': \"50269\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 99,\n            'f': \"99\",\n        },\n\"<|extratoken_48|>\",\n{\n            'v': 50304,\n            'f': \"50304\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 100,\n            'f': \"100\",\n        },\n\"<|extratoken_59|>\",\n{\n            'v': 50315,\n            'f': \"50315\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 101,\n            'f': \"101\",\n        },\n\"<|extratoken_101|>\",\n{\n            'v': 50357,\n            'f': \"50357\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 102,\n            'f': \"102\",\n        },\n\"<|extratoken_100|>\",\n{\n            'v': 50356,\n            'f': \"50356\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 103,\n            'f': \"103\",\n        },\n\"<|extratoken_142|>\",\n{\n            'v': 50398,\n            'f': \"50398\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 104,\n            'f': \"104\",\n        },\n\"<|extratoken_116|>\",\n{\n            'v': 50372,\n            'f': \"50372\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 105,\n            'f': \"105\",\n        },\n\"<|extratoken_44|>\",\n{\n            'v': 50300,\n            'f': \"50300\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 106,\n            'f': \"106\",\n        },\n\"<|extratoken_34|>\",\n{\n            'v': 50290,\n            'f': \"50290\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 107,\n            'f': \"107\",\n        },\n\"<|extratoken_61|>\",\n{\n            'v': 50317,\n            'f': \"50317\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 108,\n            'f': \"108\",\n        },\n\"<|extratoken_50|>\",\n{\n            'v': 50306,\n            'f': \"50306\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 109,\n            'f': \"109\",\n        },\n\"<|extratoken_54|>\",\n{\n            'v': 50310,\n            'f': \"50310\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 110,\n            'f': \"110\",\n        },\n\"<|extratoken_85|>\",\n{\n            'v': 50341,\n            'f': \"50341\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 111,\n            'f': \"111\",\n        },\n\"<|extratoken_6|>\",\n{\n            'v': 50262,\n            'f': \"50262\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 112,\n            'f': \"112\",\n        },\n\"<|extratoken_123|>\",\n{\n            'v': 50379,\n            'f': \"50379\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 113,\n            'f': \"113\",\n        },\n\"<|extratoken_30|>\",\n{\n            'v': 50286,\n            'f': \"50286\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 114,\n            'f': \"114\",\n        },\n\"<|extratoken_133|>\",\n{\n            'v': 50389,\n            'f': \"50389\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 115,\n            'f': \"115\",\n        },\n\"<|extratoken_92|>\",\n{\n            'v': 50348,\n            'f': \"50348\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 116,\n            'f': \"116\",\n        },\n\"<|extratoken_35|>\",\n{\n            'v': 50291,\n            'f': \"50291\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 117,\n            'f': \"117\",\n        },\n\"<|extratoken_74|>\",\n{\n            'v': 50330,\n            'f': \"50330\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 118,\n            'f': \"118\",\n        },\n\"<|extratoken_84|>\",\n{\n            'v': 50340,\n            'f': \"50340\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 119,\n            'f': \"119\",\n        },\n\"<|extratoken_107|>\",\n{\n            'v': 50363,\n            'f': \"50363\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 120,\n            'f': \"120\",\n        },\n\"<|extratoken_7|>\",\n{\n            'v': 50263,\n            'f': \"50263\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 121,\n            'f': \"121\",\n        },\n\"<|extratoken_68|>\",\n{\n            'v': 50324,\n            'f': \"50324\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 122,\n            'f': \"122\",\n        },\n\"<|extratoken_80|>\",\n{\n            'v': 50336,\n            'f': \"50336\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 123,\n            'f': \"123\",\n        },\n\"<|extratoken_45|>\",\n{\n            'v': 50301,\n            'f': \"50301\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 124,\n            'f': \"124\",\n        },\n\"<|extratoken_109|>\",\n{\n            'v': 50365,\n            'f': \"50365\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 125,\n            'f': \"125\",\n        },\n\"<|extratoken_137|>\",\n{\n            'v': 50393,\n            'f': \"50393\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 126,\n            'f': \"126\",\n        },\n\"<|extratoken_65|>\",\n{\n            'v': 50321,\n            'f': \"50321\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 127,\n            'f': \"127\",\n        },\n\"<|extratoken_71|>\",\n{\n            'v': 50327,\n            'f': \"50327\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 128,\n            'f': \"128\",\n        },\n\"<|extratoken_14|>\",\n{\n            'v': 50270,\n            'f': \"50270\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 129,\n            'f': \"129\",\n        },\n\"<|extratoken_9|>\",\n{\n            'v': 50265,\n            'f': \"50265\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 130,\n            'f': \"130\",\n        },\n\"<|extratoken_88|>\",\n{\n            'v': 50344,\n            'f': \"50344\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 131,\n            'f': \"131\",\n        },\n\"<|extratoken_105|>\",\n{\n            'v': 50361,\n            'f': \"50361\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 132,\n            'f': \"132\",\n        },\n\"<|extratoken_22|>\",\n{\n            'v': 50278,\n            'f': \"50278\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 133,\n            'f': \"133\",\n        },\n\"<|extratoken_37|>\",\n{\n            'v': 50293,\n            'f': \"50293\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 134,\n            'f': \"134\",\n        },\n\"<|extratoken_43|>\",\n{\n            'v': 50299,\n            'f': \"50299\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 135,\n            'f': \"135\",\n        },\n\"<|extratoken_130|>\",\n{\n            'v': 50386,\n            'f': \"50386\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 136,\n            'f': \"136\",\n        },\n\"<|extratoken_32|>\",\n{\n            'v': 50288,\n            'f': \"50288\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 137,\n            'f': \"137\",\n        },\n\"<|extratoken_94|>\",\n{\n            'v': 50350,\n            'f': \"50350\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 138,\n            'f': \"138\",\n        },\n\"<|extratoken_122|>\",\n{\n            'v': 50378,\n            'f': \"50378\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 139,\n            'f': \"139\",\n        },\n\"<|extratoken_79|>\",\n{\n            'v': 50335,\n            'f': \"50335\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 140,\n            'f': \"140\",\n        },\n\"<|extratoken_77|>\",\n{\n            'v': 50333,\n            'f': \"50333\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 141,\n            'f': \"141\",\n        },\n\"<|extratoken_56|>\",\n{\n            'v': 50312,\n            'f': \"50312\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }],\n [{\n            'v': 142,\n            'f': \"142\",\n        },\n\"<|extratoken_26|>\",\n{\n            'v': 50282,\n            'f': \"50282\",\n        },\n{\n            'v': null,\n            'f': \"null\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"string\", \"Vocab\"], [\"number\", \"Original ID\"], [\"number\", \"New ID\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    "
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Compare old and new trained tokenizer. After reading thousands of zh-tw wiki summaries, the trained tokenizer performs much better.\n",
        "\n",
        "print(\"new_trained_tokenizer.vocab_size: \", new_trained_tokenizer.vocab_size)\n",
        "print(\"example_text: \", example_text)\n",
        "print(\"old: \",\n",
        "      list(tokenize_cjk(old_tokenizer, example_text)))\n",
        "print(\"new: \",\n",
        "      list(tokenize_cjk(new_tokenizer, example_text)))"
      ],
      "metadata": {
        "id": "LcvCsHGC8WXu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcfce9e6-3bd8-42f2-d9fb-64c4f82343c3"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "new_trained_tokenizer.vocab_size:  20000\n",
            "example_text:  網際網路（英語：Internet）是指 20 世紀末期興起電腦網路與電腦網路之間所串連成的龐大網路系統。\n",
            "old:  [('網', 3), ('際', 2), ('網', 3), ('路', 3), ('（', 3), ('英', 3), ('語', 2), ('：', 3), ('Internet', 1), ('）', 3), ('是', 1), ('指', 3), (' 20', 1), (' ', 1), ('世', 2), ('紀', 3), ('末', 2), ('期', 2), ('�', 1), ('��', 1), ('�', 1), ('�', 1), ('電', 2), ('腦', 3), ('網', 3), ('路', 3), ('與', 2), ('電', 2), ('腦', 3), ('網', 3), ('路', 3), ('之', 1), ('間', 2), ('所', 2), ('串', 2), ('連', 2), ('成', 2), ('的', 1), ('龐', 2), ('大', 1), ('網', 3), ('路', 3), ('系', 3), ('統', 3), ('。', 1)]\n",
            "new:  [('網際網路', 1), ('（', 1), ('英語', 1), ('：', 1), ('Internet', 1), ('）', 1), ('是指', 1), (' 20', 1), (' 世紀', 1), ('末期', 1), ('�', 1), ('��', 1), ('�', 1), ('�', 1), ('電腦', 1), ('網路', 1), ('與', 1), ('電腦', 1), ('網路', 1), ('之間', 1), ('所', 1), ('串', 1), ('連', 1), ('成的', 1), ('龐', 1), ('大', 1), ('網路', 1), ('系統', 1), ('。', 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown Compare old and new trained tokenizer. After reading thousands of zh-tw wiki summaries, the trained tokenizer performs much better.\n",
        "\n",
        "sample_text_list = [\n",
        "    \"人工智慧是電腦科學、心理學、哲學等學科融合的跨領域學科。\",\n",
        "    \"高雄市充滿藝術氣息與海港風情，擁有獨具特色的駁二藝術特區、充滿藝術氛圍的美術館、現代化的高雄流行音樂中心、大型展覽館與會議中心、以及壯觀的高雄港等多元旅遊景點。透過便捷的輕軌與大眾運輸工具，到訪的旅客可以輕鬆地往返這些地點，體驗高雄豐富的文化與歷史並享受美好的時光。\"\n",
        "    \"程式設計師們越來越依賴 Git 進行版本控制、使用 Python、Java 或 JavaScript 等程式語言開發 Web 應用程式，還需要在 Linux 或 Windows 作業系統上操作，並熟悉各種資料庫系統如 MySQL、MongoDB 和 PostgreSQL，以及對 API、RESTful 架構和 Docker 容器化技術有深入了解，這都是為了追求在軟體開發領域的卓越表現。\",\n",
        "    \"在機器學習領域，研究人員利用各種算法如 SVM、Random Forest 和 Neural Networks 來分析大量數據，並對應用如自然語言處理（NLP）、圖像識別（Image Recognition）以及強化學習（Reinforcement Learning）進行深入研究，同時，他們也需要掌握 TensorFlow、PyTorch 等深度學習框架，以實現更為高效、準確的模型訓練和預測，以期在人工智能（AI）領域取得突破性的成果。\",\n",
        "    \"過幾天天天天氣不好。\",\n",
        "]\n",
        "\n",
        "for text in sample_text_list:\n",
        "    print(\"sample_text: \", text)\n",
        "    print(\"old: \",\n",
        "          list(tokenize_cjk(old_tokenizer, text)))\n",
        "    print(\"new: \",\n",
        "          list(tokenize_cjk(new_tokenizer, text)))\n",
        "    print()"
      ],
      "metadata": {
        "id": "rhKgkewsKFY6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e41ae363-2b7b-4252-83c7-eef558d09728"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample_text:  人工智慧是電腦科學、心理學、哲學等學科融合的跨領域學科。\n",
            "old:  [('人', 1), ('工', 2), ('智', 3), ('慧', 3), ('是', 1), ('電', 2), ('腦', 3), ('科', 3), ('學', 2), ('、', 1), ('心', 2), ('理', 2), ('學', 2), ('、', 1), ('哲', 3), ('學', 2), ('等', 3), ('學', 2), ('科', 3), ('融', 3), ('合', 2), ('的', 1), ('跨', 3), ('領', 3), ('域', 3), ('學', 2), ('科', 3), ('。', 1)]\n",
            "new:  [('人工智慧', 1), ('是', 1), ('電腦', 1), ('科學', 1), ('、', 1), ('心理', 1), ('學', 1), ('、', 1), ('哲學', 1), ('等', 1), ('學科', 1), ('融', 1), ('合', 1), ('的', 1), ('跨', 1), ('領域', 1), ('學科', 1), ('。', 1)]\n",
            "\n",
            "sample_text:  高雄市充滿藝術氣息與海港風情，擁有獨具特色的駁二藝術特區、充滿藝術氛圍的美術館、現代化的高雄流行音樂中心、大型展覽館與會議中心、以及壯觀的高雄港等多元旅遊景點。透過便捷的輕軌與大眾運輸工具，到訪的旅客可以輕鬆地往返這些地點，體驗高雄豐富的文化與歷史並享受美好的時光。程式設計師們越來越依賴 Git 進行版本控制、使用 Python、Java 或 JavaScript 等程式語言開發 Web 應用程式，還需要在 Linux 或 Windows 作業系統上操作，並熟悉各種資料庫系統如 MySQL、MongoDB 和 PostgreSQL，以及對 API、RESTful 架構和 Docker 容器化技術有深入了解，這都是為了追求在軟體開發領域的卓越表現。\n",
            "old:  [('高', 2), ('雄', 2), ('市', 2), ('充', 2), ('滿', 3), ('藝', 3), ('術', 2), ('氣', 2), ('息', 3), ('與', 2), ('海', 2), ('港', 3), ('風', 3), ('情', 2), ('，', 3), ('擁', 3), ('有', 2), ('獨', 3), ('具', 2), ('特', 2), ('色', 3), ('的', 1), ('駁', 3), ('二', 2), ('藝', 3), ('術', 2), ('特', 2), ('區', 2), ('、', 1), ('充', 2), ('滿', 3), ('藝', 3), ('術', 2), ('氛', 2), ('圍', 2), ('的', 1), ('美', 3), ('術', 2), ('館', 3), ('、', 1), ('現', 3), ('代', 1), ('化', 2), ('的', 1), ('高', 2), ('雄', 2), ('流', 2), ('行', 2), ('音', 3), ('樂', 3), ('中', 1), ('心', 2), ('、', 1), ('大', 1), ('型', 3), ('展', 3), ('覽', 2), ('館', 3), ('與', 2), ('會', 2), ('議', 3), ('中', 1), ('心', 2), ('、', 1), ('以', 2), ('及', 2), ('壯', 2), ('觀', 3), ('的', 1), ('高', 2), ('雄', 2), ('港', 3), ('等', 3), ('多', 2), ('元', 2), ('旅', 2), ('遊', 2), ('景', 3), ('點', 3), ('。', 1), ('透', 2), ('過', 2), ('便', 3), ('捷', 3), ('的', 1), ('輕', 3), ('軌', 2), ('與', 2), ('大', 1), ('眾', 2), ('運', 2), ('輸', 3), ('工', 2), ('具', 2), ('，', 3), ('到', 2), ('訪', 3), ('的', 1), ('旅', 2), ('客', 2), ('可', 2), ('以', 2), ('輕', 3), ('鬆', 3), ('地', 2), ('往', 2), ('返', 2), ('這', 2), ('些', 2), ('地', 2), ('點', 3), ('，', 3), ('體', 3), ('驗', 3), ('高', 2), ('雄', 2), ('豐', 3), ('富', 2), ('的', 1), ('文', 2), ('化', 2), ('與', 2), ('歷', 2), ('史', 2), ('並', 2), ('享', 2), ('受', 2), ('美', 3), ('好', 2), ('的', 1), ('時', 2), ('光', 1), ('。', 1), ('程', 3), ('式', 2), ('設', 3), ('計', 3), ('師', 2), ('們', 3), ('越', 3), ('來', 3), ('越', 3), ('依', 3), ('賴', 3), (' Git', 1), (' �', 1), ('�', 1), ('�', 1), ('行', 2), ('版', 1), ('本', 2), ('控', 3), ('制', 2), ('、', 1), ('使', 1), ('用', 2), (' Python', 1), ('、', 1), ('Java', 1), (' �', 1), ('�', 1), ('�', 1), (' JavaScript', 1), (' �', 1), ('�', 1), ('�', 1), ('程', 3), ('式', 2), ('語', 2), ('言', 3), ('開', 2), ('發', 3), (' Web', 1), (' �', 1), ('�', 1), ('�', 1), ('用', 2), ('程', 3), ('式', 2), ('，', 3), ('還', 3), ('需', 3), ('要', 2), ('在', 2), (' Linux', 1), (' �', 1), ('�', 1), ('�', 1), (' Windows', 1), (' ', 1), ('作', 1), ('業', 3), ('系', 3), ('統', 3), ('上', 1), ('操', 3), ('作', 1), ('，', 3), ('並', 2), ('熟', 3), ('悉', 3), ('各', 2), ('種', 3), ('資', 3), ('料', 2), ('庫', 2), ('系', 3), ('統', 3), ('如', 2), (' MySQL', 1), ('、', 1), ('M', 1), ('ongo', 1), ('DB', 1), (' �', 1), ('�', 1), ('�', 1), (' Post', 1), ('greSQL', 1), ('，', 3), ('以', 2), ('及', 2), ('對', 2), (' API', 1), ('、', 1), ('R', 1), ('EST', 1), ('ful', 1), (' �', 1), ('�', 1), ('��', 1), ('�', 1), ('�', 1), ('和', 3), (' Docker', 1), (' �', 1), ('�', 1), ('�', 1), ('器', 3), ('化', 2), ('技', 3), ('術', 2), ('有', 2), ('深', 3), ('入', 2), ('了', 2), ('解', 3), ('，', 3), ('這', 2), ('都', 2), ('是', 1), ('為', 3), ('了', 2), ('追', 2), ('求', 3), ('在', 2), ('軟', 2), ('體', 3), ('開', 2), ('發', 3), ('領', 3), ('域', 3), ('的', 1), ('卓', 2), ('越', 3), ('表', 2), ('現', 3), ('。', 1)]\n",
            "new:  [('高', 2), ('雄', 1), ('市', 1), ('充', 1), ('滿', 1), ('藝術', 1), ('氣', 1), ('息', 1), ('與海', 1), ('港', 1), ('風', 1), ('情', 1), ('，', 1), ('擁有', 1), ('獨', 1), ('具', 1), ('特', 1), ('色的', 1), ('駁', 2), ('二', 1), ('藝術', 1), ('特區', 1), ('、', 1), ('充', 1), ('滿', 1), ('藝術', 1), ('氛', 2), ('圍', 1), ('的美', 1), ('術', 1), ('館', 1), ('、', 1), ('現代', 1), ('化的', 1), ('高', 2), ('雄', 1), ('流行', 1), ('音樂', 1), ('中心', 1), ('、', 1), ('大型', 1), ('展覽', 1), ('館', 1), ('與', 1), ('會議', 1), ('中心', 1), ('、', 1), ('以及', 1), ('壯', 1), ('觀', 1), ('的', 1), ('高', 2), ('雄', 1), ('港', 1), ('等', 1), ('多', 1), ('元', 1), ('旅遊', 1), ('景點', 1), ('。', 1), ('透過', 1), ('便', 1), ('捷', 1), ('的', 1), ('輕', 1), ('軌', 1), ('與大', 1), ('眾', 1), ('運輸', 1), ('工具', 1), ('，', 1), ('到', 1), ('訪', 1), ('的', 1), ('旅客', 1), ('可以', 1), ('輕', 1), ('鬆', 2), ('地', 1), ('往', 1), ('返', 1), ('這些', 1), ('地點', 1), ('，', 1), ('體', 1), ('驗', 1), ('高', 2), ('雄', 1), ('豐富', 1), ('的', 1), ('文化', 1), ('與', 1), ('歷史', 1), ('並', 1), ('享', 1), ('受', 1), ('美', 1), ('好的', 1), ('時', 2), ('光', 1), ('。', 1), ('程式', 1), ('設計師', 1), ('們', 1), ('越', 1), ('來', 1), ('越', 1), ('依', 1), ('賴', 1), (' Git', 1), (' �', 1), ('�', 1), ('�', 1), ('行', 1), ('版本', 1), ('控制', 1), ('、', 1), ('使用', 1), (' Python', 1), ('、', 1), ('Java', 1), (' �', 1), ('�', 1), ('�', 1), (' JavaScript', 1), (' �', 1), ('�', 1), ('�', 1), ('程式', 1), ('語言', 1), ('開發', 1), (' Web', 1), (' �', 1), ('�', 1), ('�', 1), ('用', 1), ('程式', 1), ('，', 1), ('還', 1), ('需要', 1), ('在', 1), (' Linux', 1), (' �', 1), ('�', 1), ('�', 1), (' Windows', 1), (' ', 1), ('作業', 1), ('系統', 1), ('上', 1), ('操作', 1), ('，', 1), ('並', 1), ('熟', 1), ('悉', 1), ('各種', 1), ('資料庫', 1), ('系統', 1), ('如', 1), (' MySQL', 1), ('、', 1), ('M', 1), ('ongo', 1), ('DB', 1), (' �', 1), ('�', 1), ('�', 1), (' Post', 1), ('greSQL', 1), ('，', 1), ('以及', 1), ('對', 1), (' API', 1), ('、', 1), ('R', 1), ('EST', 1), ('ful', 1), (' �', 1), ('�', 1), ('��', 1), ('�', 1), ('�', 1), ('和', 1), (' Docker', 1), (' �', 1), ('�', 1), ('�', 1), ('器', 1), ('化', 1), ('技術', 1), ('有', 1), ('深', 1), ('入', 1), ('了', 1), ('解', 1), ('，', 1), ('這', 1), ('都是', 1), ('為了', 1), ('追', 2), ('求', 1), ('在', 1), ('軟體', 1), ('開發', 1), ('領域', 1), ('的', 1), ('卓', 1), ('越', 1), ('表現', 1), ('。', 1)]\n",
            "\n",
            "sample_text:  在機器學習領域，研究人員利用各種算法如 SVM、Random Forest 和 Neural Networks 來分析大量數據，並對應用如自然語言處理（NLP）、圖像識別（Image Recognition）以及強化學習（Reinforcement Learning）進行深入研究，同時，他們也需要掌握 TensorFlow、PyTorch 等深度學習框架，以實現更為高效、準確的模型訓練和預測，以期在人工智能（AI）領域取得突破性的成果。\n",
            "old:  [('在', 2), ('機', 1), ('器', 3), ('學', 2), ('習', 3), ('領', 3), ('域', 3), ('，', 3), ('研', 3), ('究', 3), ('人', 1), ('員', 3), ('利', 2), ('用', 2), ('各', 2), ('種', 3), ('算', 3), ('法', 2), ('如', 2), (' S', 1), ('VM', 1), ('、', 1), ('Random', 1), (' Forest', 1), (' �', 1), ('�', 1), ('�', 1), (' Neural', 1), (' Networks', 1), (' ', 1), ('來', 3), ('分', 2), ('析', 3), ('大', 1), ('量', 2), ('數', 2), ('據', 3), ('，', 3), ('並', 2), ('對', 2), ('應', 3), ('用', 2), ('如', 2), ('自', 3), ('然', 2), ('語', 2), ('言', 3), ('處', 3), ('理', 2), ('（', 3), ('N', 1), ('LP', 1), ('）', 3), ('、', 1), ('圖', 2), ('像', 3), ('識', 3), ('別', 2), ('（', 3), ('Image', 1), (' Recogn', 1), ('ition', 1), ('）', 3), ('以', 2), ('及', 2), ('強', 2), ('化', 2), ('學', 2), ('習', 3), ('（', 3), ('Re', 1), ('in', 1), ('forcement', 1), (' Learning', 1), ('）', 3), ('進', 2), ('行', 2), ('深', 3), ('入', 2), ('研', 3), ('究', 3), ('，', 3), ('同', 2), ('時', 2), ('，', 3), ('他', 2), ('們', 3), ('也', 2), ('需', 3), ('要', 2), ('掌', 3), ('握', 3), (' T', 1), ('ensor', 1), ('Flow', 1), ('、', 1), ('Py', 1), ('Tor', 1), ('ch', 1), (' �', 1), ('�', 1), ('�', 1), ('深', 3), ('度', 2), ('學', 2), ('習', 3), ('框', 3), ('架', 3), ('，', 3), ('以', 2), ('實', 2), ('現', 3), ('更', 3), ('為', 3), ('高', 2), ('效', 2), ('、', 1), ('準', 3), ('確', 3), ('的', 1), ('模', 3), ('型', 3), ('訓', 3), ('練', 2), ('和', 3), ('預', 3), ('測', 3), ('，', 3), ('以', 2), ('期', 2), ('在', 2), ('人', 1), ('工', 2), ('智', 3), ('能', 2), ('（', 3), ('AI', 1), ('）', 3), ('領', 3), ('域', 3), ('取', 2), ('得', 2), ('突', 3), ('破', 3), ('性', 2), ('的', 1), ('成', 2), ('果', 3), ('。', 1)]\n",
            "new:  [('在', 1), ('機器', 1), ('學習', 1), ('領域', 1), ('，', 1), ('研究', 1), ('人員', 1), ('利用', 1), ('各種', 1), ('算法', 1), ('如', 1), (' S', 1), ('VM', 1), ('、', 1), ('Random', 1), (' Forest', 1), (' �', 1), ('�', 1), ('�', 1), (' Neural', 1), (' Networks', 1), (' ', 1), ('來', 1), ('分析', 1), ('大量', 1), ('數據', 1), ('，', 1), ('並', 1), ('對', 1), ('應用', 1), ('如', 1), ('自然', 1), ('語言', 1), ('處理', 1), ('（', 1), ('N', 1), ('LP', 1), ('）、', 1), ('圖', 1), ('像', 1), ('識', 1), ('別', 1), ('（', 1), ('Image', 1), (' Recogn', 1), ('ition', 1), ('）', 1), ('以及', 1), ('強', 1), ('化學', 1), ('習', 1), ('（', 1), ('Re', 1), ('in', 1), ('forcement', 1), (' Learning', 1), ('）', 1), ('進行', 1), ('深', 1), ('入', 1), ('研究', 1), ('，', 1), ('同', 1), ('時', 2), ('，', 1), ('他們', 1), ('也', 1), ('需要', 1), ('掌', 1), ('握', 1), (' T', 1), ('ensor', 1), ('Flow', 1), ('、', 1), ('Py', 1), ('Tor', 1), ('ch', 1), (' �', 1), ('�', 1), ('�', 1), ('深度', 1), ('學習', 1), ('框', 2), ('架', 1), ('，', 1), ('以', 1), ('實現', 1), ('更為', 1), ('高', 2), ('效', 1), ('、', 1), ('準', 1), ('確', 1), ('的', 1), ('模型', 1), ('訓練', 1), ('和', 1), ('預', 1), ('測', 1), ('，', 1), ('以', 1), ('期', 1), ('在', 1), ('人工', 1), ('智', 1), ('能', 1), ('（', 1), ('AI', 1), ('）', 1), ('領域', 1), ('取得', 1), ('突', 1), ('破', 1), ('性的', 1), ('成', 1), ('果', 1), ('。', 1)]\n",
            "\n",
            "sample_text:  過幾天天天天氣不好。\n",
            "old:  [('過', 2), ('幾', 2), ('天', 1), ('天', 1), ('天', 1), ('天', 1), ('氣', 2), ('不', 1), ('好', 2), ('。', 1)]\n",
            "new:  [('過', 1), ('幾', 1), ('天', 1), ('天', 1), ('天', 1), ('天', 1), ('氣', 1), ('不', 1), ('好', 1), ('。', 1)]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown For generating random sample text\n",
        "!pip install git+https://github.com/zetavg/python_wikipedia pangu\n",
        "import wikipedia\n",
        "import pangu\n",
        "wikipedia.set_lang(\"zh-tw\")\n"
      ],
      "metadata": {
        "id": "q6lxa1vi89eV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e25dea1-9111-4753-a865-7b9772134935"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/zetavg/python_wikipedia\n",
            "  Cloning https://github.com/zetavg/python_wikipedia to /tmp/pip-req-build-3evy83wy\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/zetavg/python_wikipedia /tmp/pip-req-build-3evy83wy\n",
            "  Resolved https://github.com/zetavg/python_wikipedia to commit 2e9c2bfc63217af36cf7355e0c157a63c4738a2e\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pangu\n",
            "  Downloading pangu-4.0.6.1-py3-none-any.whl (6.4 kB)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from wikipedia==1.4.0) (4.11.2)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from wikipedia==1.4.0) (2.27.1)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia==1.4.0) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia==1.4.0) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia==1.4.0) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia==1.4.0) (1.26.15)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->wikipedia==1.4.0) (2.4.1)\n",
            "Building wheels for collected packages: wikipedia\n",
            "  Building wheel for wikipedia (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wikipedia: filename=wikipedia-1.4.0-py3-none-any.whl size=11802 sha256=d0d54c2a73748bdc3cf47f80fb512dd9e2e6f27ca59378f741a502acf20c6081\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-91ztjcgv/wheels/9b/d3/d8/2d3cc48d53fdd151a7ddbe0bf6f56c91bce355ef96777fc6d1\n",
            "Successfully built wikipedia\n",
            "Installing collected packages: pangu, wikipedia\n",
            "Successfully installed pangu-4.0.6.1 wikipedia-1.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown More random examples\n",
        "\n",
        "for i in range(0, 20):\n",
        "    page = wikipedia.random()\n",
        "    try:\n",
        "        text = pangu.spacing_text(\n",
        "            wikipedia.summary(page, sentences=1)\n",
        "        )\n",
        "        if not example_text:\n",
        "            continue\n",
        "        print(\"example_text: \", text)\n",
        "        print(\"old: \",\n",
        "              list(tokenize_cjk(old_tokenizer, text)))\n",
        "        print(\"new: \",\n",
        "              list(tokenize_cjk(new_tokenizer, text)))\n",
        "        print(\"\")\n",
        "    except:\n",
        "        pass"
      ],
      "metadata": {
        "id": "h8dof7kNW641",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d35797b6-3a36-4d6f-fdde-51a7bc87e00d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "example_text:  外星人（英語：extraterrestrial intelligence，簡稱 ET），又稱宇宙人、異星人，是人類對地球以外的智慧生命的統稱。\n",
            "old:  [('外', 2), ('星', 2), ('人', 1), ('（', 3), ('英', 3), ('語', 2), ('：', 3), ('ext', 1), ('r', 1), ('ater', 1), ('restrial', 1), (' intelligence', 1), ('，', 3), ('簡', 3), ('稱', 3), (' ET', 1), ('）', 3), ('，', 3), ('又', 2), ('稱', 3), ('宇', 2), ('宙', 2), ('人', 1), ('、', 1), ('異', 2), ('星', 2), ('人', 1), ('，', 3), ('是', 1), ('人', 1), ('類', 3), ('對', 2), ('地', 2), ('球', 2), ('以', 2), ('外', 2), ('的', 1), ('智', 3), ('慧', 3), ('生', 1), ('命', 2), ('的', 1), ('統', 3), ('稱', 3), ('。', 1)]\n",
            "new:  [('外', 1), ('星', 1), ('人', 1), ('（', 1), ('英語', 1), ('：', 1), ('ext', 1), ('r', 1), ('ater', 1), ('restrial', 1), (' intelligence', 1), ('，', 1), ('簡稱', 1), (' ET', 1), ('），', 1), ('又稱', 1), ('宇宙', 1), ('人', 1), ('、', 1), ('異', 1), ('星', 1), ('人', 1), ('，', 1), ('是', 1), ('人類', 1), ('對', 1), ('地球', 1), ('以外', 1), ('的', 1), ('智慧', 1), ('生命', 1), ('的', 1), ('統', 1), ('稱', 1), ('。', 1)]\n",
            "\n",
            "example_text:  尼日總理，是尼日共和國政府首腦。\n",
            "old:  [('尼', 2), ('日', 2), ('總', 3), ('理', 2), ('，', 3), ('是', 1), ('尼', 2), ('日', 2), ('共', 2), ('和', 3), ('國', 2), ('政', 3), ('府', 2), ('首', 3), ('腦', 3), ('。', 1)]\n",
            "new:  [('尼日', 1), ('總理', 1), ('，', 1), ('是', 1), ('尼日', 1), ('共和國', 1), ('政府', 1), ('首', 1), ('腦', 1), ('。', 1)]\n",
            "\n",
            "example_text:  「夜空的彼方」是 SMAP 的第 27 張單曲，於 1998 年 1 月 14 日由勝利娛樂發行。\n",
            "old:  [('「', 1), ('夜', 2), ('空', 3), ('的', 1), ('彼', 2), ('方', 1), ('」', 1), ('是', 1), (' SM', 1), ('AP', 1), (' �', 1), ('�', 1), ('�', 1), ('第', 3), (' 27', 1), (' �', 1), ('�', 1), ('�', 1), ('單', 3), ('曲', 3), ('，', 3), ('於', 2), (' 1998', 1), (' �', 1), ('�', 1), ('�', 1), (' 1', 1), (' �', 1), ('�', 1), (' 14', 1), (' �', 1), ('�', 1), ('�', 1), ('由', 2), ('勝', 2), ('利', 2), ('娛', 3), ('樂', 3), ('發', 3), ('行', 2), ('。', 1)]\n",
            "new:  [('「', 1), ('夜', 1), ('空', 1), ('的', 1), ('彼', 1), ('方', 1), ('」', 1), ('是', 1), (' SM', 1), ('AP', 1), (' �', 1), ('�', 1), ('�', 1), ('第', 1), (' 27', 1), (' �', 1), ('�', 1), ('�', 1), ('單曲', 1), ('，', 1), ('於', 1), (' 1998', 1), (' �', 1), ('�', 1), ('�', 1), (' 1', 1), (' �', 1), ('�', 1), (' 14', 1), (' �', 1), ('�', 1), ('�', 1), ('由', 1), ('勝利', 1), ('娛樂', 1), ('發行', 1), ('。', 1)]\n",
            "\n",
            "example_text:  八戶自動車道（日語：八戸自動車道／はちのへじどうしゃどう Hachinohe jidōshadō */?）是從日本岩手縣八幡平市安代系統交流道至青森縣八戶市的高速公路（高速自動車國道）。\n",
            "old:  [('八', 2), ('戶', 2), ('自', 3), ('動', 2), ('車', 2), ('道', 2), ('（', 3), ('日', 2), ('語', 2), ('：', 3), ('八', 2), ('戸', 2), ('自', 3), ('動', 2), ('車', 2), ('道', 2), ('／', 3), ('は', 1), ('ち', 2), ('の', 1), ('へ', 2), ('じ', 2), ('ど', 2), ('う', 1), ('し', 1), ('ゃ', 2), ('ど', 2), ('う', 1), (' H', 1), ('ach', 1), ('ino', 1), ('he', 1), (' j', 1), ('id', 1), ('ō', 1), ('sh', 1), ('ad', 1), ('ō', 1), (' */', 1), ('?', 1), ('）', 3), ('是', 1), ('從', 2), ('日', 2), ('本', 2), ('岩', 3), ('手', 2), ('縣', 3), ('八', 2), ('幡', 2), ('平', 2), ('市', 2), ('安', 2), ('代', 1), ('系', 3), ('統', 3), ('交', 2), ('流', 2), ('道', 2), ('至', 3), ('青', 3), ('森', 3), ('縣', 3), ('八', 2), ('戶', 2), ('市', 2), ('的', 1), ('高', 2), ('速', 2), ('公', 2), ('路', 3), ('（', 3), ('高', 2), ('速', 2), ('自', 3), ('動', 2), ('車', 2), ('國', 2), ('道', 2), ('）', 3), ('。', 1)]\n",
            "new:  [('八', 1), ('戶', 1), ('自動', 1), ('車', 1), ('道', 1), ('（', 1), ('日語', 1), ('：', 1), ('八', 1), ('戸', 2), ('自動', 1), ('車', 1), ('道', 1), ('／', 1), ('は', 1), ('ち', 1), ('の', 1), ('へ', 2), ('じ', 1), ('ど', 2), ('う', 1), ('し', 1), ('ゃ', 2), ('ど', 2), ('う', 1), (' H', 1), ('ach', 1), ('ino', 1), ('he', 1), (' j', 1), ('id', 1), ('ō', 1), ('sh', 1), ('ad', 1), ('ō', 1), (' */', 1), ('?', 1), ('）', 1), ('是', 1), ('從', 1), ('日本', 1), ('岩', 1), ('手', 1), ('縣', 1), ('八幡', 1), ('平', 1), ('市', 1), ('安', 1), ('代', 1), ('系統', 1), ('交流', 1), ('道', 1), ('至', 1), ('青', 1), ('森', 1), ('縣', 1), ('八', 1), ('戶', 1), ('市的', 1), ('高', 2), ('速', 1), ('公路', 1), ('（', 1), ('高', 2), ('速', 1), ('自動', 1), ('車', 1), ('國道', 1), ('）。', 1)]\n",
            "\n",
            "example_text:  文森・沙素 (Vincent Julien Sasso，1991 年 2 月 16 日 —），是一名法國的職業足球運動員，司職後衛，現效力於英格蘭足球冠軍聯賽錫周三。\n",
            "old:  [('文', 2), ('森', 3), ('・', 1), ('沙', 3), ('素', 3), (' (', 1), ('V', 1), ('incent', 1), (' Jul', 1), ('ien', 1), (' S', 1), ('asso', 1), ('，', 3), ('1991', 1), (' �', 1), ('�', 1), ('�', 1), (' 2', 1), (' �', 1), ('�', 1), (' 16', 1), (' �', 1), ('�', 1), ('�', 1), (' —', 1), ('）', 3), ('，', 3), ('是', 1), ('一', 1), ('名', 2), ('法', 2), ('國', 2), ('的', 1), ('職', 3), ('業', 3), ('足', 3), ('球', 2), ('運', 2), ('動', 2), ('員', 3), ('，', 3), ('司', 2), ('職', 3), ('後', 2), ('衛', 2), ('，', 3), ('現', 3), ('效', 2), ('力', 2), ('於', 2), ('英', 3), ('格', 2), ('蘭', 3), ('足', 3), ('球', 2), ('冠', 2), ('軍', 2), ('聯', 3), ('賽', 3), ('錫', 3), ('周', 2), ('三', 1), ('。', 1)]\n",
            "new:  [('文', 1), ('森', 1), ('・', 1), ('沙', 1), ('素', 1), (' (', 1), ('V', 1), ('incent', 1), (' Jul', 1), ('ien', 1), (' S', 1), ('asso', 1), ('，', 1), ('1991', 1), (' �', 1), ('�', 1), ('�', 1), (' 2', 1), (' �', 1), ('�', 1), (' 16', 1), (' �', 1), ('�', 1), ('�', 1), (' —），', 1), ('是一名', 1), ('法國', 1), ('的', 1), ('職業', 1), ('足球運動員', 1), ('，', 1), ('司職', 1), ('後衛', 1), ('，', 1), ('現效力於', 1), ('英格蘭', 1), ('足球', 1), ('冠軍', 1), ('聯賽', 1), ('錫', 1), ('周', 1), ('三', 1), ('。', 1)]\n",
            "\n",
            "example_text:  藤原忻子（ふじわら の きんし（よしこ）、1134 年（長承 3 年）—1209 年 9 月 12 日（承元 3 年八月十二）），平安時代末期后妃。\n",
            "old:  [('藤', 3), ('原', 2), ('忻', 2), ('子', 1), ('（', 3), ('ふ', 2), ('じ', 2), ('わ', 2), ('ら', 1), (' ', 1), ('の', 1), (' �', 1), ('�', 1), ('ん', 1), ('し', 1), ('（', 3), ('よ', 2), ('し', 1), ('こ', 1), ('）', 3), ('、', 1), ('11', 1), ('34', 1), (' �', 1), ('�', 1), ('�', 1), ('（', 3), ('長', 3), ('承', 2), (' 3', 1), (' �', 1), ('�', 1), ('�', 1), ('）', 3), ('—', 1), ('12', 1), ('09', 1), (' �', 1), ('�', 1), ('�', 1), (' 9', 1), (' �', 1), ('�', 1), (' 12', 1), (' �', 1), ('�', 1), ('�', 1), ('（', 3), ('承', 2), ('元', 2), (' 3', 1), (' �', 1), ('�', 1), ('�', 1), ('八', 2), ('月', 2), ('十', 2), ('二', 2), ('）', 3), ('）', 3), ('，', 3), ('平', 2), ('安', 2), ('時', 2), ('代', 1), ('末', 2), ('期', 2), ('后', 2), ('妃', 2), ('。', 1)]\n",
            "new:  [('藤', 1), ('原', 1), ('忻', 2), ('子', 1), ('（', 1), ('ふ', 2), ('じ', 1), ('わ', 2), ('ら', 1), (' ', 1), ('の', 1), (' �', 1), ('�', 1), ('ん', 1), ('し', 1), ('（', 1), ('よ', 2), ('し', 1), ('こ', 1), ('）、', 1), ('11', 1), ('34', 1), (' �', 1), ('�', 1), ('�', 1), ('（', 1), ('長', 1), ('承', 1), (' 3', 1), (' �', 1), ('�', 1), ('�', 1), ('）', 1), ('—', 1), ('12', 1), ('09', 1), (' �', 1), ('�', 1), ('�', 1), (' 9', 1), (' �', 1), ('�', 1), (' 12', 1), (' �', 1), ('�', 1), ('�', 1), ('（', 1), ('承', 1), ('元', 1), (' 3', 1), (' �', 1), ('�', 1), ('�', 1), ('八', 1), ('月', 1), ('十二', 1), ('）', 1), ('），', 1), ('平安', 1), ('時', 2), ('代', 1), ('末期', 1), ('后', 1), ('妃', 1), ('。', 1)]\n",
            "\n",
            "example_text:  大拜沙（葡萄牙語：Baixa Grande）是巴西巴伊亞州的一個市鎮。\n",
            "old:  [('大', 1), ('拜', 3), ('沙', 3), ('（', 3), ('葡', 3), ('萄', 3), ('牙', 2), ('語', 2), ('：', 3), ('Ba', 1), ('ix', 1), ('a', 1), (' Grande', 1), ('）', 3), ('是', 1), ('巴', 2), ('西', 3), ('巴', 2), ('伊', 2), ('亞', 2), ('州', 2), ('的', 1), ('一', 1), ('個', 3), ('市', 2), ('鎮', 3), ('。', 1)]\n",
            "new:  [('大', 1), ('拜', 1), ('沙', 1), ('（', 1), ('葡萄牙語', 1), ('：', 1), ('Ba', 1), ('ix', 1), ('a', 1), (' Grande', 1), ('）', 1), ('是巴西', 1), ('巴', 1), ('伊', 1), ('亞', 1), ('州的一個市鎮', 1), ('。', 1)]\n",
            "\n",
            "example_text:  西爾肯・勞曼（德語：Silken Laumann，1964 年 11 月 14 日 —），加拿大女子賽艇運動員。\n",
            "old:  [('西', 3), ('爾', 3), ('肯', 3), ('・', 1), ('勞', 2), ('曼', 3), ('（', 3), ('德', 2), ('語', 2), ('：', 3), ('Sil', 1), ('ken', 1), (' La', 1), ('umann', 1), ('，', 3), ('1964', 1), (' �', 1), ('�', 1), ('�', 1), (' 11', 1), (' �', 1), ('�', 1), (' 14', 1), (' �', 1), ('�', 1), ('�', 1), (' —', 1), ('）', 3), ('，', 3), ('加', 2), ('拿', 3), ('大', 1), ('女', 1), ('子', 1), ('賽', 3), ('艇', 3), ('運', 2), ('動', 2), ('員', 3), ('。', 1)]\n",
            "new:  [('西', 1), ('爾', 1), ('肯', 1), ('・', 1), ('勞', 1), ('曼', 1), ('（', 1), ('德語', 1), ('：', 1), ('Sil', 1), ('ken', 1), (' La', 1), ('umann', 1), ('，', 1), ('1964', 1), (' �', 1), ('�', 1), ('�', 1), (' 11', 1), (' �', 1), ('�', 1), (' 14', 1), (' �', 1), ('�', 1), ('�', 1), (' —），', 1), ('加拿大', 1), ('女子', 1), ('賽艇', 1), ('運動員', 1), ('。', 1)]\n",
            "\n",
            "example_text:  柯特野馬 XSP 手槍（英語：Colt Mustang XSP）是一款由柯特所設計和生產以後，於 2013 年推出的輕便袖珍型單動操作式半自動手槍，為採用野馬袖珍精簡型結構的更新型聚合物底把版本，發射.380 ACP 口徑手槍子彈。\n",
            "old:  [('柯', 3), ('特', 2), ('野', 2), ('馬', 3), (' X', 1), ('SP', 1), (' �', 1), ('�', 1), ('�', 1), ('槍', 3), ('（', 3), ('英', 3), ('語', 2), ('：', 3), ('Col', 1), ('t', 1), (' Mustang', 1), (' X', 1), ('SP', 1), ('）', 3), ('是', 1), ('一', 1), ('款', 3), ('由', 2), ('柯', 3), ('特', 2), ('所', 2), ('設', 3), ('計', 3), ('和', 3), ('生', 1), ('產', 2), ('以', 2), ('後', 2), ('，', 3), ('於', 2), (' 2013', 1), (' �', 1), ('�', 1), ('�', 1), ('推', 3), ('出', 2), ('的', 1), ('輕', 3), ('便', 3), ('袖', 3), ('珍', 3), ('型', 3), ('單', 3), ('動', 2), ('操', 3), ('作', 1), ('式', 2), ('半', 2), ('自', 3), ('動', 2), ('手', 2), ('槍', 3), ('，', 3), ('為', 3), ('採', 3), ('用', 2), ('野', 2), ('馬', 3), ('袖', 3), ('珍', 3), ('精', 2), ('簡', 3), ('型', 3), ('結', 3), ('構', 3), ('的', 1), ('更', 3), ('新', 2), ('型', 3), ('聚', 3), ('合', 2), ('物', 2), ('底', 2), ('把', 3), ('版', 1), ('本', 2), ('，', 3), ('發', 3), ('射', 2), ('.', 1), ('380', 1), (' AC', 1), ('P', 1), (' �', 1), ('�', 1), ('�', 1), ('徑', 2), ('手', 2), ('槍', 3), ('子', 1), ('彈', 2), ('。', 1)]\n",
            "new:  [('柯', 1), ('特', 1), ('野', 1), ('馬', 1), (' X', 1), ('SP', 1), (' �', 1), ('�', 1), ('�', 1), ('槍', 1), ('（', 1), ('英語', 1), ('：', 1), ('Col', 1), ('t', 1), (' Mustang', 1), (' X', 1), ('SP', 1), ('）', 1), ('是一款', 1), ('由', 1), ('柯', 1), ('特', 1), ('所', 1), ('設計', 1), ('和', 1), ('生產', 1), ('以後', 1), ('，', 1), ('於', 1), (' 2013', 1), (' �', 1), ('�', 1), ('�', 1), ('推出的', 1), ('輕', 1), ('便', 1), ('袖', 1), ('珍', 1), ('型', 1), ('單', 1), ('動', 1), ('操作', 1), ('式', 1), ('半', 1), ('自動', 1), ('手', 1), ('槍', 1), ('，', 1), ('為', 1), ('採用', 1), ('野', 1), ('馬', 1), ('袖', 1), ('珍', 1), ('精', 2), ('簡', 1), ('型', 1), ('結構', 1), ('的', 1), ('更', 1), ('新', 1), ('型', 1), ('聚', 1), ('合物', 1), ('底', 1), ('把', 1), ('版本', 1), ('，', 1), ('發射', 1), ('.', 1), ('380', 1), (' AC', 1), ('P', 1), (' �', 1), ('�', 1), ('�', 1), ('徑', 1), ('手', 1), ('槍', 1), ('子', 1), ('彈', 1), ('。', 1)]\n",
            "\n",
            "example_text:  元靈宮，位於北京市朝陽區南苑小紅門稍西，是一座道教宮觀。\n",
            "old:  [('元', 2), ('靈', 3), ('宮', 2), ('，', 3), ('位', 2), ('於', 2), ('北', 2), ('京', 2), ('市', 2), ('朝', 2), ('陽', 2), ('區', 2), ('南', 2), ('苑', 3), ('小', 2), ('紅', 3), ('門', 2), ('稍', 3), ('西', 3), ('，', 3), ('是', 1), ('一', 1), ('座', 2), ('道', 2), ('教', 2), ('宮', 2), ('觀', 3), ('。', 1)]\n",
            "new:  [('元', 1), ('靈', 1), ('宮', 1), ('，', 1), ('位於', 1), ('北京市', 1), ('朝', 1), ('陽', 2), ('區', 1), ('南', 1), ('苑', 1), ('小', 1), ('紅', 1), ('門', 1), ('稍', 2), ('西', 1), ('，', 1), ('是一座', 1), ('道', 1), ('教', 1), ('宮', 1), ('觀', 1), ('。', 1)]\n",
            "\n",
            "example_text:  馬特米揚・柳廷（俄語：Мартемья́н Ники́тич Рю́тин，羅馬化：Martem'yán Nikítich Ryútin，1890 年 2 月 13 日 —1937 年 1 月 10 日），他是蘇聯馬克思主義活動家、布爾什維克革命家和蘇共高級幹部人員。\n",
            "old:  [('馬', 3), ('特', 2), ('米', 3), ('揚', 3), ('・', 1), ('柳', 3), ('廷', 3), ('（', 3), ('俄', 2), ('語', 2), ('：', 3), ('М', 2), ('а', 1), ('р', 1), ('т', 1), ('е', 1), ('м', 1), ('ь', 1), ('я', 1), ('́', 2), ('н', 1), (' �', 1), ('�', 1), ('и', 1), ('к', 1), ('и', 1), ('́', 2), ('т', 1), ('и', 1), ('ч', 2), (' �', 1), ('�', 1), ('ю', 2), ('́', 2), ('т', 1), ('и', 1), ('н', 1), ('，', 3), ('羅', 3), ('馬', 3), ('化', 2), ('：', 3), ('Mart', 1), ('em', 1), (\"'\", 1), ('y', 1), ('án', 1), (' Nik', 1), ('í', 1), ('t', 1), ('ich', 1), (' Ry', 1), ('ú', 1), ('tin', 1), ('，', 3), ('18', 1), ('90', 1), (' �', 1), ('�', 1), ('�', 1), (' 2', 1), (' �', 1), ('�', 1), (' 13', 1), (' �', 1), ('�', 1), ('�', 1), (' —', 1), ('19', 1), ('37', 1), (' �', 1), ('�', 1), ('�', 1), (' 1', 1), (' �', 1), ('�', 1), (' 10', 1), (' �', 1), ('�', 1), ('�', 1), ('）', 3), ('，', 3), ('他', 2), ('是', 1), ('蘇', 3), ('聯', 3), ('馬', 3), ('克', 2), ('思', 2), ('主', 2), ('義', 3), ('活', 3), ('動', 2), ('家', 2), ('、', 1), ('布', 2), ('爾', 3), ('什', 2), ('維', 3), ('克', 2), ('革', 3), ('命', 2), ('家', 2), ('和', 3), ('蘇', 3), ('共', 2), ('高', 2), ('級', 3), ('幹', 2), ('部', 2), ('人', 1), ('員', 3), ('。', 1)]\n",
            "new:  [('馬', 1), ('特', 1), ('米揚', 1), ('・', 1), ('柳', 1), ('廷', 1), ('（', 1), ('俄語', 1), ('：', 1), ('М', 1), ('ар', 1), ('т', 1), ('е', 1), ('м', 1), ('ь', 1), ('я', 1), ('́', 1), ('н', 1), (' �', 1), ('�', 1), ('и', 1), ('к', 1), ('и', 1), ('́', 1), ('т', 1), ('ич', 1), (' �', 1), ('�', 1), ('ю', 2), ('́', 1), ('т', 1), ('ин', 1), ('，', 1), ('羅馬化', 1), ('：', 1), ('Mart', 1), ('em', 1), (\"'\", 1), ('y', 1), ('án', 1), (' Nik', 1), ('í', 1), ('t', 1), ('ich', 1), (' Ry', 1), ('ú', 1), ('tin', 1), ('，', 1), ('18', 1), ('90', 1), (' �', 1), ('�', 1), ('�', 1), (' 2', 1), (' �', 1), ('�', 1), (' 13', 1), (' �', 1), ('�', 1), ('�', 1), (' —', 1), ('19', 1), ('37', 1), (' �', 1), ('�', 1), ('�', 1), (' 1', 1), (' �', 1), ('�', 1), (' 10', 1), (' �', 1), ('�', 1), ('�', 1), ('），', 1), ('他是', 1), ('蘇聯', 1), ('馬克', 1), ('思', 1), ('主義', 1), ('活動', 1), ('家', 1), ('、', 1), ('布爾', 1), ('什', 1), ('維', 1), ('克', 1), ('革命', 1), ('家', 1), ('和', 1), ('蘇', 1), ('共', 1), ('高', 2), ('級', 1), ('幹', 1), ('部', 1), ('人員', 1), ('。', 1)]\n",
            "\n",
            "example_text:  德克薩斯州刑事上訴法院（Texas Court of Criminal Appeals (CCA)）是美國德克薩斯州所有的刑事案件之終審法院。\n",
            "old:  [('德', 2), ('克', 2), ('薩', 3), ('斯', 2), ('州', 2), ('刑', 2), ('事', 2), ('上', 1), ('訴', 3), ('法', 2), ('院', 3), ('（', 3), ('Texas', 1), (' Court', 1), (' of', 1), (' Criminal', 1), (' Appeals', 1), (' (', 1), ('CC', 1), ('A', 1), (')', 1), ('）', 3), ('是', 1), ('美', 3), ('國', 2), ('德', 2), ('克', 2), ('薩', 3), ('斯', 2), ('州', 2), ('所', 2), ('有', 2), ('的', 1), ('刑', 2), ('事', 2), ('案', 3), ('件', 2), ('之', 1), ('終', 3), ('審', 2), ('法', 2), ('院', 3), ('。', 1)]\n",
            "new:  [('德克薩斯', 1), ('州', 1), ('刑', 1), ('事', 1), ('上', 1), ('訴', 1), ('法院', 1), ('（', 1), ('Texas', 1), (' Court', 1), (' of', 1), (' Criminal', 1), (' Appeals', 1), (' (', 1), ('CC', 1), ('A', 1), (')', 1), ('）', 1), ('是美國', 1), ('德克薩斯', 1), ('州', 1), ('所有的', 1), ('刑', 1), ('事', 1), ('案', 1), ('件', 1), ('之', 1), ('終', 1), ('審', 1), ('法院', 1), ('。', 1)]\n",
            "\n",
            "example_text:  《IT'z ICY》是韓國女子組合 ITZY 首張迷你專輯，於 2019 年 7 月 29 日推出。\n",
            "old:  [('《', 2), ('IT', 1), (\"'\", 1), ('z', 1), (' IC', 1), ('Y', 1), ('》', 2), ('是', 1), ('韓', 3), ('國', 2), ('女', 1), ('子', 1), ('組', 3), ('合', 2), (' IT', 1), ('Z', 1), ('Y', 1), (' �', 1), ('�', 1), ('�', 1), ('張', 2), ('迷', 2), ('你', 2), ('�', 1), ('��', 1), ('�', 1), ('�', 1), ('，', 3), ('於', 2), (' 2019', 1), (' �', 1), ('�', 1), ('�', 1), (' 7', 1), (' �', 1), ('�', 1), (' 29', 1), (' �', 1), ('�', 1), ('�', 1), ('推', 3), ('出', 2), ('。', 1)]\n",
            "new:  [('《', 1), ('IT', 1), (\"'\", 1), ('z', 1), (' IC', 1), ('Y', 1), ('》', 1), ('是韓國', 1), ('女子', 1), ('組合', 1), (' IT', 1), ('Z', 1), ('Y', 1), (' �', 1), ('�', 1), ('�', 1), ('張', 1), ('迷你', 1), ('�', 1), ('��', 1), ('�', 1), ('�', 1), ('，', 1), ('於', 1), (' 2019', 1), (' �', 1), ('�', 1), ('�', 1), (' 7', 1), (' �', 1), ('�', 1), (' 29', 1), (' �', 1), ('�', 1), ('�', 1), ('推出', 1), ('。', 1)]\n",
            "\n",
            "example_text:  包頭市回民中學 (1984 年 9 月 -）地處內蒙古包頭市東河區南海鄭二窯子。\n",
            "old:  [('包', 2), ('頭', 3), ('市', 2), ('回', 2), ('民', 2), ('中', 1), ('學', 2), (' (', 1), ('1984', 1), (' �', 1), ('�', 1), ('�', 1), (' 9', 1), (' �', 1), ('�', 1), (' -', 1), ('）', 3), ('地', 2), ('處', 3), ('內', 2), ('蒙', 3), ('古', 2), ('包', 2), ('頭', 3), ('市', 2), ('東', 2), ('河', 3), ('區', 2), ('南', 2), ('海', 2), ('鄭', 3), ('二', 2), ('窯', 3), ('子', 1), ('。', 1)]\n",
            "new:  [('包頭', 1), ('市', 1), ('回', 1), ('民', 1), ('中學', 1), (' (', 1), ('1984', 1), (' �', 1), ('�', 1), ('�', 1), (' 9', 1), (' �', 1), ('�', 1), (' -', 1), ('）', 1), ('地處', 1), ('內蒙古', 1), ('包頭', 1), ('市', 1), ('東', 1), ('河', 1), ('區', 1), ('南海', 1), ('鄭', 1), ('二', 1), ('窯', 2), ('子', 1), ('。', 1)]\n",
            "\n",
            "example_text:  《夢想星搭檔》第一季是一檔為央視品牌節目《夢想合唱團》的姊妹篇，央視綜合頻道打造的原創性音樂公益節目。\n",
            "old:  [('《', 2), ('夢', 2), ('想', 2), ('星', 2), ('搭', 3), ('檔', 3), ('》', 2), ('第', 3), ('一', 1), ('季', 2), ('是', 1), ('一', 1), ('檔', 3), ('為', 3), ('央', 2), ('視', 2), ('品', 3), ('牌', 2), ('節', 3), ('目', 2), ('《', 2), ('夢', 2), ('想', 2), ('合', 2), ('唱', 3), ('團', 2), ('》', 2), ('的', 1), ('姊', 2), ('妹', 2), ('篇', 3), ('，', 3), ('央', 2), ('視', 2), ('綜', 3), ('合', 2), ('頻', 3), ('道', 2), ('打', 2), ('造', 2), ('的', 1), ('原', 2), ('創', 2), ('性', 2), ('音', 3), ('樂', 3), ('公', 2), ('益', 2), ('節', 3), ('目', 2), ('。', 1)]\n",
            "new:  [('《', 1), ('夢', 1), ('想', 1), ('星', 1), ('搭', 1), ('檔', 1), ('》', 1), ('第一', 1), ('季', 1), ('是一', 1), ('檔', 1), ('為', 1), ('央', 1), ('視', 1), ('品牌', 1), ('節目', 1), ('《', 1), ('夢', 1), ('想', 1), ('合唱團', 1), ('》', 1), ('的', 1), ('姊妹', 1), ('篇', 1), ('，', 1), ('央', 1), ('視', 1), ('綜合', 1), ('頻道', 1), ('打', 1), ('造', 1), ('的', 1), ('原創', 1), ('性', 1), ('音樂', 1), ('公', 1), ('益', 1), ('節目', 1), ('。', 1)]\n",
            "\n",
            "example_text:  阿爾瓦羅・德・門達尼亞 - 內拉（Álvaro de Mendaña y Neira；或 Neyra；1542 年 10 月 1 日 —1595 年 10 月 18 日）是一名西班牙航海家，是秘魯總督洛佩・加西亞・德・卡斯楚（Lope García de Castro）的外甥，以 1567 年和 1595 年從秘魯兩次橫穿太平洋的航行為後人銘記，馬克薩斯群島、庫克群島和索羅門群島是他發現的。\n",
            "old:  [('阿', 3), ('爾', 3), ('瓦', 3), ('羅', 3), ('・', 1), ('德', 2), ('・', 1), ('門', 2), ('達', 2), ('尼', 2), ('亞', 2), (' -', 1), (' �', 1), ('�', 1), ('�', 1), ('拉', 3), ('（', 3), ('Á', 2), ('lv', 1), ('aro', 1), (' de', 1), (' M', 1), ('enda', 1), ('ña', 1), (' y', 1), (' Ne', 1), ('ira', 1), ('；', 3), ('或', 2), (' Ney', 1), ('ra', 1), ('；', 3), ('15', 1), ('42', 1), (' �', 1), ('�', 1), ('�', 1), (' 10', 1), (' �', 1), ('�', 1), (' 1', 1), (' �', 1), ('�', 1), ('�', 1), (' —', 1), ('15', 1), ('95', 1), (' �', 1), ('�', 1), ('�', 1), (' 10', 1), (' �', 1), ('�', 1), (' 18', 1), (' �', 1), ('�', 1), ('�', 1), ('）', 3), ('是', 1), ('一', 1), ('名', 2), ('西', 3), ('班', 3), ('牙', 2), ('航', 2), ('海', 2), ('家', 2), ('，', 3), ('是', 1), ('秘', 3), ('魯', 3), ('總', 3), ('督', 3), ('洛', 3), ('佩', 2), ('・', 1), ('加', 2), ('西', 3), ('亞', 2), ('・', 1), ('德', 2), ('・', 1), ('卡', 2), ('斯', 2), ('楚', 3), ('（', 3), ('L', 1), ('ope', 1), (' Garc', 1), ('ía', 1), (' de', 1), (' Castro', 1), ('）', 3), ('的', 1), ('外', 2), ('甥', 2), ('，', 3), ('以', 2), (' 15', 1), ('67', 1), (' �', 1), ('�', 1), ('�', 1), ('和', 3), (' 15', 1), ('95', 1), (' �', 1), ('�', 1), ('�', 1), ('從', 2), ('秘', 3), ('魯', 3), ('兩', 2), ('次', 3), ('橫', 2), ('穿', 3), ('太', 2), ('平', 2), ('洋', 3), ('的', 1), ('航', 2), ('行', 2), ('為', 3), ('後', 2), ('人', 1), ('銘', 3), ('記', 3), ('，', 3), ('馬', 3), ('克', 2), ('薩', 3), ('斯', 2), ('群', 3), ('島', 3), ('、', 1), ('庫', 2), ('克', 2), ('群', 3), ('島', 3), ('和', 3), ('索', 3), ('羅', 3), ('門', 2), ('群', 3), ('島', 3), ('是', 1), ('他', 2), ('發', 3), ('現', 3), ('的', 1), ('。', 1)]\n",
            "new:  [('阿爾', 1), ('瓦', 1), ('羅', 1), ('・', 1), ('德', 1), ('・', 1), ('門', 1), ('達', 1), ('尼亞', 1), (' -', 1), (' �', 1), ('�', 1), ('�', 1), ('拉', 1), ('（', 1), ('Á', 2), ('lv', 1), ('aro', 1), (' de', 1), (' M', 1), ('enda', 1), ('ña', 1), (' y', 1), (' Ne', 1), ('ira', 1), ('；', 1), ('或', 1), (' Ney', 1), ('ra', 1), ('；', 1), ('15', 1), ('42', 1), (' �', 1), ('�', 1), ('�', 1), (' 10', 1), (' �', 1), ('�', 1), (' 1', 1), (' �', 1), ('�', 1), ('�', 1), (' —', 1), ('15', 1), ('95', 1), (' �', 1), ('�', 1), ('�', 1), (' 10', 1), (' �', 1), ('�', 1), (' 18', 1), (' �', 1), ('�', 1), ('�', 1), ('）', 1), ('是一名', 1), ('西班牙', 1), ('航', 1), ('海', 1), ('家', 1), ('，', 1), ('是秘魯', 1), ('總督', 1), ('洛', 1), ('佩', 1), ('・', 1), ('加', 1), ('西亞', 1), ('・', 1), ('德', 1), ('・', 1), ('卡斯', 1), ('楚', 1), ('（', 1), ('L', 1), ('ope', 1), (' Garc', 1), ('ía', 1), (' de', 1), (' Castro', 1), ('）', 1), ('的', 1), ('外甥', 1), ('，', 1), ('以', 1), (' 15', 1), ('67', 1), (' �', 1), ('�', 1), ('�', 1), ('和', 1), (' 15', 1), ('95', 1), (' �', 1), ('�', 1), ('�', 1), ('從', 1), ('秘魯', 1), ('兩次', 1), ('橫', 1), ('穿', 1), ('太平洋', 1), ('的', 1), ('航', 1), ('行為', 1), ('後', 1), ('人', 1), ('銘', 1), ('記', 1), ('，', 1), ('馬克', 1), ('薩斯', 1), ('群島', 1), ('、', 1), ('庫', 1), ('克', 1), ('群島和', 1), ('索羅門群島', 1), ('是', 1), ('他', 1), ('發現的', 1), ('。', 1)]\n",
            "\n",
            "example_text:  劉秉賓（英語：Match Lau Ping Pan，1984 年 11 月 1 日 —），藝名皓聖，現為自由身藝人多元發展，前 ViuTV 藝人，前 TVB 娛樂新聞台主播，前無綫電視藝人。\n",
            "old:  [('劉', 2), ('秉', 3), ('賓', 3), ('（', 3), ('英', 3), ('語', 2), ('：', 3), ('Match', 1), (' Lau', 1), (' Ping', 1), (' Pan', 1), ('，', 3), ('1984', 1), (' �', 1), ('�', 1), ('�', 1), (' 11', 1), (' �', 1), ('�', 1), (' 1', 1), (' �', 1), ('�', 1), ('�', 1), (' —', 1), ('）', 3), ('，', 3), ('藝', 3), ('名', 2), ('皓', 2), ('聖', 2), ('，', 3), ('現', 3), ('為', 3), ('自', 3), ('由', 2), ('身', 3), ('藝', 3), ('人', 1), ('多', 2), ('元', 2), ('發', 3), ('展', 3), ('，', 3), ('前', 2), (' Vi', 1), ('u', 1), ('TV', 1), (' �', 1), ('�', 1), ('�', 1), ('人', 1), ('，', 3), ('前', 2), (' TV', 1), ('B', 1), (' �', 1), ('�', 1), ('�', 1), ('樂', 3), ('新', 2), ('聞', 3), ('台', 2), ('主', 2), ('播', 3), ('，', 3), ('前', 2), ('無', 2), ('綫', 3), ('電', 2), ('視', 2), ('藝', 3), ('人', 1), ('。', 1)]\n",
            "new:  [('劉', 1), ('秉', 1), ('賓', 1), ('（', 1), ('英語', 1), ('：', 1), ('Match', 1), (' Lau', 1), (' Ping', 1), (' Pan', 1), ('，', 1), ('1984', 1), (' �', 1), ('�', 1), ('�', 1), (' 11', 1), (' �', 1), ('�', 1), (' 1', 1), (' �', 1), ('�', 1), ('�', 1), (' —），', 1), ('藝', 1), ('名', 1), ('皓', 1), ('聖', 2), ('，', 1), ('現為', 1), ('自由', 1), ('身', 1), ('藝人', 1), ('多', 1), ('元', 1), ('發展', 1), ('，', 1), ('前', 1), (' Vi', 1), ('u', 1), ('TV', 1), (' �', 1), ('�', 1), ('�', 1), ('人', 1), ('，', 1), ('前', 1), (' TV', 1), ('B', 1), (' �', 1), ('�', 1), ('�', 1), ('樂', 1), ('新聞', 1), ('台', 1), ('主', 1), ('播', 1), ('，', 1), ('前', 1), ('無綫', 1), ('電視', 1), ('藝人', 1), ('。', 1)]\n",
            "\n",
            "example_text:  接觸器（Contactor），指利用線圈流過電流產生磁場，使觸頭閉合，以達到控制負載的電器。\n",
            "old:  [('接', 3), ('觸', 3), ('器', 3), ('（', 3), ('Cont', 1), ('actor', 1), ('）', 3), ('，', 3), ('指', 3), ('利', 2), ('用', 2), ('線', 2), ('圈', 2), ('流', 2), ('過', 2), ('電', 2), ('流', 2), ('產', 2), ('生', 1), ('磁', 3), ('場', 3), ('，', 3), ('使', 1), ('觸', 3), ('頭', 3), ('閉', 2), ('合', 2), ('，', 3), ('以', 2), ('達', 2), ('到', 2), ('控', 3), ('制', 2), ('負', 3), ('載', 3), ('的', 1), ('電', 2), ('器', 3), ('。', 1)]\n",
            "new:  [('接觸', 1), ('器', 1), ('（', 1), ('Cont', 1), ('actor', 1), ('），', 1), ('指', 1), ('利用', 1), ('線', 1), ('圈', 1), ('流', 1), ('過', 1), ('電', 1), ('流', 1), ('產生', 1), ('磁', 1), ('場', 1), ('，', 1), ('使', 1), ('觸', 1), ('頭', 1), ('閉', 1), ('合', 1), ('，', 1), ('以', 1), ('達到', 1), ('控制', 1), ('負', 1), ('載', 1), ('的電', 1), ('器', 1), ('。', 1)]\n",
            "\n",
            "example_text:  臺灣民主運動支援會（英語：Organization for the Support of Democratic Movement in Taiwan、OSDMT）是個活躍於七十 - 八十年代的美國留學生社團，宗旨為支援臺灣的民主運動\n",
            "。\n",
            "old:  [('臺', 3), ('灣', 3), ('民', 2), ('主', 2), ('運', 2), ('動', 2), ('支', 3), ('援', 3), ('會', 2), ('（', 3), ('英', 3), ('語', 2), ('：', 3), ('Organ', 1), ('ization', 1), (' for', 1), (' the', 1), (' Support', 1), (' of', 1), (' Democratic', 1), (' Movement', 1), (' in', 1), (' Taiwan', 1), ('、', 1), ('OS', 1), ('D', 1), ('MT', 1), ('）', 3), ('是', 1), ('個', 3), ('活', 3), ('躍', 3), ('於', 2), ('七', 2), ('十', 2), (' -', 1), (' �', 1), ('�', 1), ('�', 1), ('十', 2), ('年', 2), ('代', 1), ('的', 1), ('美', 3), ('國', 2), ('留', 2), ('學', 2), ('生', 1), ('社', 3), ('團', 2), ('，', 3), ('宗', 2), ('旨', 2), ('為', 3), ('支', 3), ('援', 3), ('臺', 3), ('灣', 3), ('的', 1), ('民', 2), ('主', 2), ('運', 2), ('動', 2), ('\\n', 1), ('。', 1)]\n",
            "new:  [('臺灣', 1), ('民主', 1), ('運動', 1), ('支援', 1), ('會', 1), ('（', 1), ('英語', 1), ('：', 1), ('Organ', 1), ('ization', 1), (' for', 1), (' the', 1), (' Support', 1), (' of', 1), (' Democratic', 1), (' Movement', 1), (' in', 1), (' Taiwan', 1), ('、', 1), ('OS', 1), ('D', 1), ('MT', 1), ('）', 1), ('是', 1), ('個', 1), ('活躍', 1), ('於', 1), ('七', 1), ('十', 1), (' -', 1), (' �', 1), ('�', 1), ('�', 1), ('十年', 1), ('代', 1), ('的美國', 1), ('留', 1), ('學生', 1), ('社', 1), ('團', 1), ('，', 1), ('宗', 1), ('旨', 1), ('為', 1), ('支援', 1), ('臺灣', 1), ('的', 1), ('民主', 1), ('運動', 1), ('\\n', 1), ('。', 1)]\n",
            "\n",
            "example_text:  約翰・羅伯特・沃姆斯利・斯托得，CBE（John Robert Walmsley Stott，1921 年 4 月 27 日 —2011 年 7 月 27 日）是當代的福音派領袖。\n",
            "old:  [('約', 3), ('翰', 3), ('・', 1), ('羅', 3), ('伯', 2), ('特', 2), ('・', 1), ('沃', 3), ('姆', 2), ('斯', 2), ('利', 2), ('・', 1), ('斯', 2), ('托', 2), ('得', 2), ('，', 3), ('C', 1), ('BE', 1), ('（', 3), ('John', 1), (' Robert', 1), (' Wal', 1), ('ms', 1), ('ley', 1), (' St', 1), ('ott', 1), ('，', 3), ('19', 1), ('21', 1), (' �', 1), ('�', 1), ('�', 1), (' 4', 1), (' �', 1), ('�', 1), (' 27', 1), (' �', 1), ('�', 1), ('�', 1), (' —', 1), ('2011', 1), (' �', 1), ('�', 1), ('�', 1), (' 7', 1), (' �', 1), ('�', 1), (' 27', 1), (' �', 1), ('�', 1), ('�', 1), ('）', 3), ('是', 1), ('當', 2), ('代', 1), ('的', 1), ('福', 3), ('音', 3), ('派', 3), ('領', 3), ('袖', 3), ('。', 1)]\n",
            "new:  [('約翰', 1), ('・', 1), ('羅伯特', 1), ('・', 1), ('沃', 1), ('姆斯', 1), ('利', 1), ('・', 1), ('斯', 1), ('托', 1), ('得', 1), ('，', 1), ('C', 1), ('BE', 1), ('（', 1), ('John', 1), (' Robert', 1), (' Wal', 1), ('ms', 1), ('ley', 1), (' St', 1), ('ott', 1), ('，', 1), ('19', 1), ('21', 1), (' �', 1), ('�', 1), ('�', 1), (' 4', 1), (' �', 1), ('�', 1), (' 27', 1), (' �', 1), ('�', 1), ('�', 1), (' —', 1), ('2011', 1), (' �', 1), ('�', 1), ('�', 1), (' 7', 1), (' �', 1), ('�', 1), (' 27', 1), (' �', 1), ('�', 1), ('�', 1), ('）', 1), ('是', 1), ('當', 1), ('代', 1), ('的', 1), ('福', 1), ('音', 1), ('派', 1), ('領袖', 1), ('。', 1)]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Save the Tokenizer to HF"
      ],
      "metadata": {
        "id": "Mjn5afcoow61"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "LfBg0iRoovep",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "75a59514ded647629c9c561f474daabd",
            "494dc4d8b28c40d896c8c00b876e906f",
            "f297569ea4444a9aa881116692122388",
            "ba46221d0d9444a682542a31abe8598f",
            "8639b4df35f2426ab2342f0d8435895f",
            "e0dd677f64924f7191e660c791748409",
            "ab990fde2d8b45b4913ae3959c5b713b",
            "610b1d1c5fc147a48296138fa47671cf",
            "d6a8f26f52944b3caee4af5f4356e317",
            "8d652845e4f84a799136ba2fde47acef",
            "994eb05ee3a8413f840ef1c80093e379",
            "fae6e10e34b842c79191fcf7aa20d14f",
            "6b7c5464bddd49168e04409aedaebc9e",
            "52441b0b16ca429abc4bd072f37339f6",
            "77a8f9422858493e8fe0fd643e9194c2",
            "1ad9aa2f907943089d1a1d5fc34ca6e7",
            "00f554f2ab86415fad31048071239d84",
            "6602929e1b6c4f3d906836487cfda55c",
            "c22b37837a814e60a7e455cbb9bf50e2",
            "7d3cea4b94724b7db46b74696095e994",
            "47bc465a6b9345cf8190e106b63e0be2",
            "5db3720d08b146dabf06570c6070b32d",
            "83426d9a21e841618e5df21c86ffd439",
            "6b3055f3d23541239987cee75f476ad7",
            "24ba6d800b61410d8f534d35eebcc54e",
            "7a955e918632465faaf5ac670243e394",
            "494b0f37dade49eca2b4eb381455196d",
            "21f29d0595524d5cbd9386ee8e3124af",
            "bf1f252b0250470db03b1c6fc086e22d",
            "f3c02ed49b194b08a0034e96f767f771",
            "c7c49a35a4b94fcca79a3f7e2a776025",
            "b56efd667d3c420599877dcd688d2f32"
          ]
        },
        "outputId": "dbd2a92e-0fa6-4530-8132-a78daaaf6eba"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75a59514ded647629c9c561f474daabd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_tokenizer.push_to_hub(\n",
        "    \"test-gpt-j-zh-tw-tokenizer-20230428\",\n",
        "    private=False\n",
        ")"
      ],
      "metadata": {
        "id": "cpZXpQNmo5Z6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bb908b7-851a-4d6f-b062-c939baf0f394"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/zetavg/test-gpt-j-zh-tw-tokenizer-20230428/commit/a2786ddf84efe676aa6a2f949a90a1df52e83481', commit_message='Upload tokenizer', commit_description='', oid='a2786ddf84efe676aa6a2f949a90a1df52e83481', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    }
  ]
}