course/en/chapter6/section5.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zkmn73/colab/blob/main/course/en/chapter6/section5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUGua6SKm6uf"
      },
      "source": [
        "# Byte-Pair Encoding tokenization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BjNmUKiQm6uk"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYkKgUi2m6uk",
        "outputId": "dfba73df-b551-473c-a0ab-d5ec0fb9f8c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "Installing collected packages: sentencepiece, xxhash, safetensors, dill, responses, multiprocess, huggingface-hub, tokenizers, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.17.3 multiprocess-0.70.15 responses-0.18.0 safetensors-0.4.0 sentencepiece-0.1.99 tokenizers-0.14.1 transformers-4.34.0 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Xf8qUwAsm6um"
      },
      "outputs": [],
      "source": [
        "corpus = [\n",
        "    \"This is the Hugging Face Course.\",\n",
        "    \"This chapter is about tokenization.\",\n",
        "    \"This section shows several tokenizer algorithms.\",\n",
        "    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "7a5fc605660d4531bcd9bfed0e708b76",
            "074b7d1fb0b24a78ae70d73c436a3829",
            "c3892bfa43834d69ab0274a0a06700b7",
            "c8a805a8d9d44ef8bdb57d45e2415563",
            "0462a1a10eea4639985ff8f07c743bbc",
            "602814aa340c4473adbbc31c5eb84852",
            "6076caba9eb948e9b77a192539ad1b74",
            "dc1dd245ce164a379bd6c2bc88ac6ac1",
            "970ec77307a248f29cd8bce17233005f",
            "af1ebf403f204a8f874bb32f31feec6f",
            "4af833d37381464baa26a8e22a1e8490",
            "5ac62b8104744f3e907a1ade1cdf0e0d",
            "b9a855a10ce848949ab41264aa92c6fc",
            "7bffe5ead0874b09a4b12ff10f5f8c9e",
            "4350895b0ed9400593d9eaf399ce2951",
            "20f0c1bc8a744f4d9190aadfe01a3b69",
            "939afd1dfc0a4f56b2d5896e15f546dd",
            "eb4a8c01ca8b42c8aa5f4963aa28cdf5",
            "adb30f5ab6ff48ae893293cee2d79fa8",
            "1b5fe1b476ca4fec934a3e9e85cd2b57",
            "009939a7dcc8401fb7f6b70e94f6c3e2",
            "d2d5ff9e8b334cc3b4a800088d83c569",
            "238c7b87364c42e88193fd71a0171f46",
            "72862cdccd804319859da0d0b85d5b65",
            "79957e17ff2e4849bbbe397ed367a2c8",
            "f26c8fbf032f4391a817d178a56677a3",
            "5bc02429cd6345798d922738cd4ec33a",
            "5b7201f5e9844ca4a7feb93ffe3af482",
            "774b69aea5cf46078d556e420e135de1",
            "d53c8f0fbed749598a2952a8d4e4739e",
            "40b0e2b9ca1145b7bea0c9e71a5d9b1d",
            "8d5c38b837334cbabe57312a6c2a442d",
            "48cb478bea2c4feb8853072e437fb88a",
            "bac9286627f04f958f5d6ae602f4a198",
            "c70c60efa10044038a714cc0ab74d130",
            "93f2ddcedee4432db1a50603c9da75ec",
            "7eb24360ea3143e6b1c50432bdb011db",
            "25d928029c5e473a8455d01b1b83538a",
            "8a7d9d58a99e4198a50d2c00cdf51f10",
            "a96ef373eebe4e34a98b05663de0e737",
            "ee31c560aa304f248836b85092f85e69",
            "d3e0794c5ba54150bd7deb5bf18894d6",
            "2538354693e843f9a39a42b83d2600d4",
            "a9b823652c31414296b34358957f51ed"
          ]
        },
        "id": "qr8vi5jZm6un",
        "outputId": "3638481d-0e44-40ef-ce9d-ec36092d9802"
      },
      "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,
              "version_minor": 0,
              "model_id": "7a5fc605660d4531bcd9bfed0e708b76"
            }
          },
          "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,
              "version_minor": 0,
              "model_id": "5ac62b8104744f3e907a1ade1cdf0e0d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "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": "238c7b87364c42e88193fd71a0171f46"
            }
          },
          "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": "bac9286627f04f958f5d6ae602f4a198"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIQNZRCRm6un",
        "outputId": "c7beb7a1-d505-49d3-f968-1359f0a578b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "defaultdict(<class 'int'>, {'This': 3, 'Ġis': 2, 'Ġthe': 1, 'ĠHugging': 1, 'ĠFace': 1, 'ĠCourse': 1, '.': 4, 'Ġchapter': 1, 'Ġabout': 1, 'Ġtokenization': 1, 'Ġsection': 1, 'Ġshows': 1, 'Ġseveral': 1, 'Ġtokenizer': 1, 'Ġalgorithms': 1, 'Hopefully': 1, ',': 1, 'Ġyou': 1, 'Ġwill': 1, 'Ġbe': 1, 'Ġable': 1, 'Ġto': 1, 'Ġunderstand': 1, 'Ġhow': 1, 'Ġthey': 1, 'Ġare': 1, 'Ġtrained': 1, 'Ġand': 1, 'Ġgenerate': 1, 'Ġtokens': 1})\n"
          ]
        }
      ],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "word_freqs = defaultdict(int)\n",
        "\n",
        "for text in corpus:\n",
        "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
        "    # print(words_with_offsets)\n",
        "    new_words = [word for word, offset in words_with_offsets]\n",
        "    for word in new_words:\n",
        "        word_freqs[word] += 1\n",
        "\n",
        "print(word_freqs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYL2LaIQm6up",
        "outputId": "b9b6e352-0cda-4b2f-c5f8-1a25403c3fd3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'Ġ']\n"
          ]
        }
      ],
      "source": [
        "alphabet = []\n",
        "\n",
        "for word in word_freqs.keys():\n",
        "    for letter in word:\n",
        "        if letter not in alphabet:\n",
        "            alphabet.append(letter)\n",
        "alphabet.sort()\n",
        "\n",
        "print(alphabet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "CTG-rF0Wm6uq"
      },
      "outputs": [],
      "source": [
        "vocab = [\"<|endoftext|>\"] + alphabet.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Vuj9EoMfm6uq"
      },
      "outputs": [],
      "source": [
        "splits = {word: [c for c in word] for word in word_freqs.keys()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "1XdVYfr0m6ur"
      },
      "outputs": [],
      "source": [
        "def compute_pair_freqs(splits):\n",
        "    pair_freqs = defaultdict(int)\n",
        "    for word, freq in word_freqs.items():\n",
        "        split = splits[word]\n",
        "        if len(split) == 1:\n",
        "            continue\n",
        "        for i in range(len(split) - 1):\n",
        "            pair = (split[i], split[i + 1])\n",
        "            pair_freqs[pair] += freq\n",
        "    return pair_freqs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jouU5ebym6ur",
        "outputId": "5a4f0b38-e816-437b-9203-868306074db3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('T', 'h'): 3\n",
            "('h', 'i'): 3\n",
            "('i', 's'): 5\n",
            "('Ġ', 'i'): 2\n",
            "('Ġ', 't'): 7\n",
            "('t', 'h'): 3\n"
          ]
        }
      ],
      "source": [
        "pair_freqs = compute_pair_freqs(splits)\n",
        "\n",
        "for i, key in enumerate(pair_freqs.keys()):\n",
        "    print(f\"{key}: {pair_freqs[key]}\")\n",
        "    if i >= 5:\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t12_gtf8m6us",
        "outputId": "7c657d17-0f9c-41c2-c9a8-978511e8e6ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('Ġ', 't') 7\n"
          ]
        }
      ],
      "source": [
        "best_pair = \"\"\n",
        "max_freq = None\n",
        "\n",
        "for pair, freq in pair_freqs.items():\n",
        "    if max_freq is None or max_freq < freq:\n",
        "        best_pair = pair\n",
        "        max_freq = freq\n",
        "\n",
        "print(best_pair, max_freq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLkmM7yEm6us",
        "outputId": "18c372fe-d145-45de-dd01-3dcb5e2d84e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['<|endoftext|>', ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'Ġ', 'Ġt']\n"
          ]
        }
      ],
      "source": [
        "merges = {(\"Ġ\", \"t\"): \"Ġt\"}\n",
        "vocab.append(\"Ġt\")\n",
        "print(vocab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "k3jVZ6UGm6ut"
      },
      "outputs": [],
      "source": [
        "def merge_pair(a, b, splits):\n",
        "    for word in word_freqs:\n",
        "        split = splits[word]\n",
        "        if len(split) == 1:\n",
        "            continue\n",
        "\n",
        "        i = 0\n",
        "        while i < len(split) - 1:\n",
        "            if split[i] == a and split[i + 1] == b:\n",
        "                split = split[:i] + [a + b] + split[i + 2 :]\n",
        "            else:\n",
        "                i += 1\n",
        "        splits[word] = split\n",
        "    return splits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YHXQBA0m6ut",
        "outputId": "efb99e18-5b36-4f76-d519-5c2906049157"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Ġt', 'r', 'a', 'i', 'n', 'e', 'd']\n"
          ]
        }
      ],
      "source": [
        "splits = merge_pair(\"Ġ\", \"t\", splits)\n",
        "print(splits[\"Ġtrained\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "RHHyJpCTm6ut"
      },
      "outputs": [],
      "source": [
        "vocab_size = 50\n",
        "\n",
        "while len(vocab) < vocab_size:\n",
        "    pair_freqs = compute_pair_freqs(splits)\n",
        "    best_pair = \"\"\n",
        "    max_freq = None\n",
        "    for pair, freq in pair_freqs.items():\n",
        "        if max_freq is None or max_freq < freq:\n",
        "            best_pair = pair\n",
        "            max_freq = freq\n",
        "    splits = merge_pair(*best_pair, splits)\n",
        "    merges[best_pair] = best_pair[0] + best_pair[1]\n",
        "    vocab.append(best_pair[0] + best_pair[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdafhb1vm6ut",
        "outputId": "7afe47da-032a-4167-97ca-521c977b9424"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{('Ġ', 't'): 'Ġt', ('i', 's'): 'is', ('e', 'r'): 'er', ('Ġ', 'a'): 'Ġa', ('Ġt', 'o'): 'Ġto', ('e', 'n'): 'en', ('T', 'h'): 'Th', ('Th', 'is'): 'This', ('o', 'u'): 'ou', ('s', 'e'): 'se', ('Ġto', 'k'): 'Ġtok', ('Ġtok', 'en'): 'Ġtoken', ('n', 'd'): 'nd', ('Ġ', 'is'): 'Ġis', ('Ġt', 'h'): 'Ġth', ('Ġth', 'e'): 'Ġthe', ('i', 'n'): 'in', ('Ġa', 'b'): 'Ġab', ('Ġtoken', 'i'): 'Ġtokeni'}\n"
          ]
        }
      ],
      "source": [
        "print(merges)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "21_nzv8Km6ut",
        "outputId": "bacca79a-c78e-42bf-9a11-cc9021a30ec2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['<|endoftext|>', ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'Ġ', 'Ġt', 'is', 'er', 'Ġa', 'Ġto', 'en', 'Th', 'This', 'ou', 'se', 'Ġtok', 'Ġtoken', 'nd', 'Ġis', 'Ġth', 'Ġthe', 'in', 'Ġab', 'Ġtokeni']\n"
          ]
        }
      ],
      "source": [
        "print(vocab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "rcUFG5d5m6uu"
      },
      "outputs": [],
      "source": [
        "def tokenize(text):\n",
        "    pre_tokenize_result = tokenizer._tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
        "    pre_tokenized_text = [word for word, offset in pre_tokenize_result]\n",
        "    splits = [[l for l in word] for word in pre_tokenized_text]\n",
        "    for pair, merge in merges.items():\n",
        "        for idx, split in enumerate(splits):\n",
        "            i = 0\n",
        "            while i < len(split) - 1:\n",
        "                if split[i] == pair[0] and split[i + 1] == pair[1]:\n",
        "                    split = split[:i] + [merge] + split[i + 2 :]\n",
        "                else:\n",
        "                    i += 1\n",
        "            splits[idx] = split\n",
        "\n",
        "    return sum(splits, [])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdvUr98fm6uu",
        "outputId": "b4798c05-dd6e-415e-8387-96c3649052d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This', 'Ġis', 'Ġ', 'n', 'o', 't', 'Ġa', 'Ġtoken', '.']"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "tokenize(\"This is not a token.\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_code = \"\"\"#ifdef _MSC_VER\n",
        "#  define _CRT_SECURE_NO_WARNINGS\n",
        "#endif\n",
        "\n",
        "#include <stdio.h>\n",
        "#include <string.h>\n",
        "#include <stdlib.h>\n",
        "#include <ctype.h>\n",
        "#include <limits.h>\n",
        "\n",
        "#ifndef PATH_MAX\n",
        "#  define PATH_MAX 260\n",
        "#endif\n",
        "\n",
        "#define SEARCH_PATHS_MAX 64\n",
        "#define FILE_PATHS_MAX 1024\n",
        "\n",
        "static int fline(char **line, size_t *n, FILE *fp) {\n",
        "    int chr;\n",
        "    char *pos;\n",
        "    if (!line || !n || !fp)\n",
        "        return -1;\n",
        "    if (!*line)\n",
        "        if (!(*line = (char *)malloc((*n=64))))\n",
        "            return -1;\n",
        "    chr = *n;\n",
        "    pos = *line;\n",
        "    for (;;) {\n",
        "        int c = fgetc(fp);\n",
        "        if (chr < 2) {\n",
        "            *n += (*n > 16) ? *n : 64;\n",
        "            chr = *n + *line - pos;\n",
        "            if (!(*line = (char *)realloc(*line,*n)))\n",
        "                return -1;\n",
        "            pos = *n - chr + *line;\n",
        "        }\n",
        "        if (ferror(fp))\n",
        "            return -1;\n",
        "        if (c == EOF) {\n",
        "            if (pos == *line)\n",
        "                return -1;\n",
        "            else\n",
        "                break;\n",
        "        }\n",
        "        *pos++ = c;\n",
        "        chr--;\n",
        "        if (c == '\\n')\n",
        "            break;\n",
        "    }\n",
        "    *pos = '\\0';\n",
        "    return pos - *line;\n",
        "}\"\"\"\n",
        "\n",
        "tokenize(a_code)"
      ],
      "metadata": {
        "id": "a0W-Ih7Kx25t",
        "outputId": "59404f7d-d64f-4be6-83db-6cb3c06097a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['#',\n",
              " 'i',\n",
              " 'f',\n",
              " 'd',\n",
              " 'e',\n",
              " 'f',\n",
              " 'Ġ',\n",
              " '_',\n",
              " 'M',\n",
              " 'S',\n",
              " 'C',\n",
              " '_',\n",
              " 'V',\n",
              " 'E',\n",
              " 'R',\n",
              " 'Ċ',\n",
              " '#',\n",
              " 'Ġ',\n",
              " 'Ġ',\n",
              " 'd',\n",
              " 'e',\n",
              " 'f',\n",
              " 'in',\n",
              " 'e',\n",
              " 'Ġ',\n",
              " '_',\n",
              " 'C',\n",
              " 'R',\n",
              " 'T',\n",
              " '_',\n",
              " 'S',\n",
              " 'E',\n",
              " 'C',\n",
              " 'U',\n",
              " 'R',\n",
              " 'E',\n",
              " '_',\n",
              " 'N',\n",
              " 'O',\n",
              " '_',\n",
              " 'W',\n",
              " 'A',\n",
              " 'R',\n",
              " 'N',\n",
              " 'I',\n",
              " 'N',\n",
              " 'G',\n",
              " 'S',\n",
              " 'Ċ',\n",
              " '#',\n",
              " 'en',\n",
              " 'd',\n",
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