hf_tokenizer.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ivlo8uraszN3",
        "outputId": "f3854383-1d7d-47f2-d7fb-f2c5f4c0e228"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[?25hInstalling collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-3.0.1 dill-0.3.8 multiprocess-0.70.16 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2i1A1yt4Er5"
      },
      "source": [
        "#  START\n",
        "Download the BookCorpus dataset. Take every 7-th sample (the indices are multiple of 7:[0,7,14,21,...]) from the entire dataset. This will result in a dataset with 10 million samples (exactly, 10,572,033). Use these samples to build a tokenizer with the BPE tokenization algorithm by varying the vocabulary size.\n",
        "\n",
        "Normalizer: LowerCase\n",
        "PreTokenizer: WhiteSpace\n",
        "Model: BPE\n",
        "Special tokens: [GO],[UNK],[PAD],[EOS]\n",
        "PostProcessing: None\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "66tX4fTatclE"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6xz74SethAj",
        "outputId": "8c57963b-b9df-4a60-9cff-480db284e352"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['text'],\n",
            "    num_rows: 74004228\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "ds = load_dataset('bookcorpus',split='all')\n",
        "print(ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6FmW-2fKtlh6"
      },
      "outputs": [],
      "source": [
        "ds = ds.select([i for i in range(len(ds)) if i%7 == 0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXkr3ycV34Fi"
      },
      "source": [
        "# Q1\n",
        "\n",
        "Tokenize the input text: “SEBI study finds 93% of individual F&O traders made losses between FY22 and FY24.” using the following configurations.\n",
        "\n",
        "Keep the vocabulary size at 5000 and tokenize the input text using the learned vocabulary. Choose the number of tokens returned by the tokenizer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DZcRwL0yvh7y",
        "outputId": "e148c18b-c7a8-4388-f30c-5c1aea20145a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text'],\n",
              "    num_rows: 10572033\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YwfVrS2uofbd"
      },
      "outputs": [],
      "source": [
        "from tokenizers import Tokenizer\n",
        "from tokenizers.models import BPE\n",
        "from tokenizers.pre_tokenizers import Whitespace\n",
        "from tokenizers import normalizers\n",
        "from tokenizers.normalizers import Lowercase\n",
        "from tokenizers.trainers import BpeTrainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yM6sU4qhvjMl"
      },
      "outputs": [],
      "source": [
        "tokenizer = Tokenizer(BPE(unk_token=\"[UNK]\"))\n",
        "tokenizer.pre_tokenizer = Whitespace()\n",
        "\n",
        "normalizer = normalizers.Sequence([Lowercase()])\n",
        "tokenizer.normalizer = normalizer\n",
        "\n",
        "trainer = BpeTrainer(special_tokens=[\"[GO]\", \"[UNK]\", \"[PAD]\", \"[EOS]\"], vocab_size=5000, continuing_subword_prefix=\"##\")\n",
        "tokenizer.train_from_iterator(ds['text'], trainer=trainer)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "z2CCMUnh3GEl",
        "outputId": "bc33fc79-e7d5-410c-ed99-170787b6af57"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'sebi study finds 93% of individual f&o traders made losses between fy22 and fy24.'"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# checking Normalizer\n",
        "normalizer.normalize_str(\"SEBI study finds 93% of individual F&O traders made losses between FY22 and FY24.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hyoHDosN4ZJX",
        "outputId": "bb784c46-fa1d-4713-9b29-93bb4547e9ce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Encoding(num_tokens=32, attributes=[ids, type_ids, tokens, offsets, attention_mask, special_tokens_mask, overflowing])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "tokenizer.encode(\"SEBI study finds 93% of individual F&O traders made losses between FY22 and FY24.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jqn0_ODf6ZX1",
        "outputId": "364bdcb1-dbb5-4538-e745-6c925cf1cb1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(tokenizer.encode(\"SEBI study finds 93% of individual F&O traders made losses between FY22 and FY24.\").tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0hh6F3Q8jVL"
      },
      "source": [
        "# Q2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THcsYZd66g2n",
        "outputId": "35807c33-8ffc-49df-bad0-0c91f15c19d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "##########\n",
            "5000 5000\n",
            "32\n",
            "##########\n",
            "\n",
            "\n",
            "\n",
            "##########\n",
            "10000 10000\n",
            "28\n",
            "##########\n",
            "\n",
            "\n",
            "\n",
            "##########\n",
            "15000 15000\n",
            "28\n",
            "##########\n",
            "\n",
            "\n",
            "\n",
            "##########\n",
            "32000 32000\n",
            "25\n",
            "##########\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for size in [5000, 10000, 15000, 32000]:\n",
        "  print(\"#\"*10)\n",
        "  # Step 4: Define the BPE tokenizer with required configuration\n",
        "  tokenizer = Tokenizer(BPE())\n",
        "\n",
        "  # Configure normalizer and pre-tokenizer\n",
        "  tokenizer.normalizer = normalizers.Sequence([Lowercase()])\n",
        "  tokenizer.pre_tokenizer = Whitespace()\n",
        "\n",
        "  trainer = BpeTrainer(special_tokens=[\"[GO]\", \"[UNK]\", \"[PAD]\", \"[EOS]\"], vocab_size=size, continuing_subword_prefix=\"##\")\n",
        "  tokenizer.train_from_iterator(ds['text'], trainer=trainer)\n",
        "  print(size, len(tokenizer.get_vocab()))\n",
        "\n",
        "  encoded = tokenizer.encode(\"SEBI study finds 93% of individual F&O traders made losses between FY22 and FY24.\")\n",
        "  print(len(encoded.tokens))\n",
        "  print(\"#\"*10)\n",
        "  print('\\n\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iNcyqoMH8hOn"
      },
      "outputs": [],
      "source": [
        "hopper = Tokenizer.from_file(\"/content/hopper.json\")\n",
        "enc = hopper.encode(\"SEBI study finds 93% of individual F&O traders made losses between FY22 and FY24.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYEYS6EzRDsr",
        "outputId": "1d2a5b1c-a199-44af-f4cf-edfe5382394c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(enc.tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVdX_LtSRG0Y",
        "outputId": "228b6a94-af07-40ec-b20c-30712d19dead"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['seb',\n",
              " '##i',\n",
              " 'study',\n",
              " 'finds',\n",
              " '9',\n",
              " '##3',\n",
              " '%',\n",
              " 'of',\n",
              " 'individual',\n",
              " 'f',\n",
              " '&',\n",
              " 'o',\n",
              " 'traders',\n",
              " 'made',\n",
              " 'losses',\n",
              " 'between',\n",
              " 'f',\n",
              " '##y',\n",
              " '##22',\n",
              " 'and',\n",
              " 'f',\n",
              " '##y',\n",
              " '##2',\n",
              " '##4',\n",
              " '.']"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "enc.tokens"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5g4pE4SUkPuC"
      },
      "source": [
        "#### Hopper config\n",
        "https://drive.google.com/file/d/1QNnyh8iMN-IqW_h1w8gAMtw09Em7-e1e/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JglPTAT_Wgmw"
      },
      "outputs": [],
      "source": [
        "hopper = Tokenizer.from_file(\"/content/hopper.json\")\n",
        "hopper.add_tokens([\"FY\"])\n",
        "enc = hopper.encode(\"SEBI study finds 93% of individual F&O traders made losses between FY22 and FY24.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ir1Qwn3jXyke",
        "outputId": "ea274d66-1bd9-423e-f3ab-e126a17993d9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "22"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(enc.tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKRNIOcZagVE"
      },
      "source": [
        "# Q5\n",
        "Load the “bert-base-uncased” and \"gpt2” tokenizers (use AutoTokenizer function from transformers). Which of the following special tokens are used in these tokenizers?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409,
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        },
        "id": "y0C-SZSEX2Di",
        "outputId": "b72196cf-2fd1-4e43-c3a4-1983c0b161fe"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "475290b50f424bd6a121c5a6b3741d7c",
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              "version_minor": 0
            },
            "text/plain": [
              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1118bdb930fc4178ab19e536b8a36bb4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "36537b9378a84f4ca7d852f12ffd0281",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "053520c5f71b421e8577903ccabe9be0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4d0445a888d24a36a1c12d276352fbeb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42fd1ff3ca4c4d52afa0db0a16b589bc",
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              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
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          },
          "metadata": {},
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "977f9b38a1704b40bc088a2ef387d560",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a2c32db757f04a25844e6801aacc2a19",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3abe3d370e1541c7b2d9d97f587eb9ac",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bfcedb27cff24c6696683dd1d57696b8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "bert_tok = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "gpt_tok = AutoTokenizer.from_pretrained(\"gpt2\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ygSdwLqauY8",
        "outputId": "29675a97-d0b6-4a75-957f-a9604028e61b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['SPECIAL_TOKENS_ATTRIBUTES',\n",
              " '__annotations__',\n",
              " '__call__',\n",
              " '__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__le__',\n",
              " '__len__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_add_tokens',\n",
              " '_additional_special_tokens',\n",
              " '_auto_class',\n",
              " '_batch_encode_plus',\n",
              " '_bos_token',\n",
              " '_call_one',\n",
              " '_cls_token',\n",
              " '_compile_jinja_template',\n",
              " '_convert_encoding',\n",
              " '_convert_id_to_token',\n",
              " '_convert_token_to_id_with_added_voc',\n",
              " '_create_repo',\n",
              " '_decode',\n",
              " '_decode_use_source_tokenizer',\n",
              " '_encode_plus',\n",
              " '_eos_token',\n",
              " '_eventual_warn_about_too_long_sequence',\n",
              " '_eventually_correct_t5_max_length',\n",
              " '_from_pretrained',\n",
              " '_get_files_timestamps',\n",
              " '_get_padding_truncation_strategies',\n",
              " '_in_target_context_manager',\n",
              " '_mask_token',\n",
              " '_pad',\n",
              " '_pad_token',\n",
              " '_pad_token_type_id',\n",
              " '_processor_class',\n",
              " '_render_with_assistant_indices',\n",
              " '_save_pretrained',\n",
              " '_sep_token',\n",
              " '_set_processor_class',\n",
              " '_switch_to_input_mode',\n",
              " '_switch_to_target_mode',\n",
              " '_tokenizer',\n",
              " '_unk_token',\n",
              " '_upload_modified_files',\n",
              " 'add_special_tokens',\n",
              " 'add_tokens',\n",
              " 'added_tokens_decoder',\n",
              " 'added_tokens_encoder',\n",
              " 'additional_special_tokens',\n",
              " 'additional_special_tokens_ids',\n",
              " 'all_special_ids',\n",
              " 'all_special_tokens',\n",
              " 'all_special_tokens_extended',\n",
              " 'apply_chat_template',\n",
              " 'as_target_tokenizer',\n",
              " 'backend_tokenizer',\n",
              " 'batch_decode',\n",
              " 'batch_encode_plus',\n",
              " 'bos_token',\n",
              " 'bos_token_id',\n",
              " 'build_inputs_with_special_tokens',\n",
              " 'can_save_slow_tokenizer',\n",
              " 'chat_template',\n",
              " 'clean_up_tokenization',\n",
              " 'clean_up_tokenization_spaces',\n",
              " 'cls_token',\n",
              " 'cls_token_id',\n",
              " 'convert_added_tokens',\n",
              " 'convert_ids_to_tokens',\n",
              " 'convert_tokens_to_ids',\n",
              " 'convert_tokens_to_string',\n",
              " 'create_token_type_ids_from_sequences',\n",
              " 'decode',\n",
              " 'decoder',\n",
              " 'deprecation_warnings',\n",
              " 'do_lower_case',\n",
              " 'encode',\n",
              " 'encode_plus',\n",
              " 'eos_token',\n",
              " 'eos_token_id',\n",
              " 'from_pretrained',\n",
              " 'get_added_vocab',\n",
              " 'get_chat_template',\n",
              " 'get_special_tokens_mask',\n",
              " 'get_vocab',\n",
              " 'init_inputs',\n",
              " 'init_kwargs',\n",
              " 'is_fast',\n",
              " 'mask_token',\n",
              " 'mask_token_id',\n",
              " 'max_len_sentences_pair',\n",
              " 'max_len_single_sentence',\n",
              " 'model_input_names',\n",
              " 'model_max_length',\n",
              " 'name_or_path',\n",
              " 'num_special_tokens_to_add',\n",
              " 'pad',\n",
              " 'pad_token',\n",
              " 'pad_token_id',\n",
              " 'pad_token_type_id',\n",
              " 'padding_side',\n",
              " 'prepare_for_model',\n",
              " 'prepare_seq2seq_batch',\n",
              " 'pretrained_vocab_files_map',\n",
              " 'push_to_hub',\n",
              " 'register_for_auto_class',\n",
              " 'sanitize_special_tokens',\n",
              " 'save_pretrained',\n",
              " 'save_vocabulary',\n",
              " 'sep_token',\n",
              " 'sep_token_id',\n",
              " 'set_truncation_and_padding',\n",
              " 'slow_tokenizer_class',\n",
              " 'special_tokens_map',\n",
              " 'special_tokens_map_extended',\n",
              " 'split_special_tokens',\n",
              " 'tokenize',\n",
              " 'train_new_from_iterator',\n",
              " 'truncate_sequences',\n",
              " 'truncation_side',\n",
              " 'unk_token',\n",
              " 'unk_token_id',\n",
              " 'verbose',\n",
              " 'vocab',\n",
              " 'vocab_files_names',\n",
              " 'vocab_size']"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dir(bert_tok)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k4RJnYLnawHL",
        "outputId": "95ec61c5-5bce-4500-ef4f-69c04721f403"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "({'bos_token': '<|endoftext|>',\n",
              "  'eos_token': '<|endoftext|>',\n",
              "  'unk_token': '<|endoftext|>'},\n",
              " {'unk_token': '[UNK]',\n",
              "  'sep_token': '[SEP]',\n",
              "  'pad_token': '[PAD]',\n",
              "  'cls_token': '[CLS]',\n",
              "  'mask_token': '[MASK]'})"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gpt_tok.special_tokens_map, bert_tok.special_tokens_map"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bX0694hvhp2_"
      },
      "source": [
        "# Q6\n",
        "By now, we have four tokenizers.\n",
        "\n",
        "1. Custom tokenizer (vocab size 32K, trained on 10 million samples)\n",
        "2. bert-base-uncased\n",
        "3. gpt2\n",
        "4. hopper\n",
        "\n",
        "Use these four tokenizers to count the number of tokens for the entire “imdb” dataset (drop the “unsupervised” part of the dataset). Enter the tokenizers in order such that the size of the dataset (measured in tokens) as returned by the tokenizers is in decreasing order. For example, if the first tokenizer yields the smallest number of tokens and the fourth tokenizer yields the largest, you would enter 1234 (without any spaces).”\n"
      ]
    },
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              "Generating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
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          },
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        {
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              "Generating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
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              "Generating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
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      ],
      "source": [
        "imdb = load_dataset(\"imdb\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qbimy9EDhu3Q"
      },
      "outputs": [],
      "source": [
        "del imdb['unsupervised']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkPrPd9QhyFZ",
        "outputId": "a8d919e2-ea5e-4f39-cd0f-2a08edbc0baa"
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        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "imdb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnUHIhzyjCdb"
      },
      "source": [
        "#### Custom Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yIQf02TSwMFF"
      },
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      "source": [
        "# Step 1: Load BookCorpus dataset\n",
        "# dataset = load_dataset(\"bookcorpus\")"
      ]
    },
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        {
          "name": "stdout",
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          "text": [
            "Number of selected samples: 10572033\n"
          ]
        }
      ],
      "source": [
        "def custom_tokenizer(dataset, vocab_size):\n",
        "\n",
        "  # Select every 7th sample from the dataset\n",
        "  # Merge the 'train' split into a list of texts, then filter every 7th text\n",
        "  texts = [dataset['train'][i]['text'] for i in range(0, len(dataset['train']), 7)]\n",
        "  print(f\"Number of selected samples: {len(texts)}\")  # Should result in approximately 10 million samples\n",
        "  # Define the BPE tokenizer with required configuration\n",
        "  tokenizer = Tokenizer(BPE())\n",
        "\n",
        "  # Configure normalizer and pre-tokenizer\n",
        "  tokenizer.normalizer = normalizers.Sequence([Lowercase()])\n",
        "  tokenizer.pre_tokenizer = Whitespace()\n",
        "\n",
        "  # Special tokens\n",
        "  special_tokens = [\"[GO]\", \"[UNK]\", \"[PAD]\", \"[EOS]\"]\n",
        "\n",
        "  # Train the tokenizer on the selected texts with a vocabulary size of 5000\n",
        "  trainer = BpeTrainer(vocab_size=vocab_size, special_tokens=special_tokens, continuing_subword_prefix=\"##\")\n",
        "\n",
        "  # Train the tokenizer on the selected dataset\n",
        "  tokenizer.train_from_iterator(texts, trainer=trainer)\n",
        "  return tokenizer\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "custom_tok = custom_tokenizer(ds, 32000)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oNjNqQBzkJeT"
      },
      "source": [
        "We have custom_tok, hopper, gpt_tok and bert_tok"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3hKn4c_j_y1",
        "outputId": "93c2d9e8-1b75-45a5-f686-1a21a833ad11"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (720 > 512). Running this sequence through the model will result in indexing errors\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (1168 > 1024). Running this sequence through the model will result in indexing errors\n"
          ]
        }
      ],
      "source": [
        "texts = imdb['train']['text'] + imdb['test']['text']  # Combine train and test splits\n",
        "\n",
        "# Function to count tokens for a dataset using a given tokenizer\n",
        "def count_tokens_with_tokenizer(tokenizer, texts, is_hf_tokenizer=False):\n",
        "    total_tokens = 0\n",
        "    for text in texts:\n",
        "        if is_hf_tokenizer:\n",
        "            # Use HuggingFace tokenizers\n",
        "            encoded = tokenizer.encode(text)\n",
        "            total_tokens += len(encoded)\n",
        "        else:\n",
        "            # Use custom tokenizers (e.g., Tokenizer class)\n",
        "            encoded = tokenizer.encode(text)\n",
        "            total_tokens += len(encoded.tokens)\n",
        "    return total_tokens\n",
        "\n",
        "# Step 3: Count tokens for each tokenizer\n",
        "custom_token_count = count_tokens_with_tokenizer(custom_tok, texts)\n",
        "bert_token_count = count_tokens_with_tokenizer(bert_tok, texts, is_hf_tokenizer=True)\n",
        "gpt2_token_count = count_tokens_with_tokenizer(gpt_tok, texts, is_hf_tokenizer=True)\n",
        "hopper_token_count = count_tokens_with_tokenizer(hopper, texts)"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0g9hzgdottB",
        "outputId": "fb6d8a4e-eee6-44d4-f802-be216d047e1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(13526933, 'Hopper'),\n",
              " (14812432, 'GPT2'),\n",
              " (15092609, 'Custom'),\n",
              " (15516058, 'Bert')]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "token_arr = [(custom_token_count, \"Custom\"), (bert_token_count, \"Bert\"), (gpt2_token_count, \"GPT2\"), (hopper_token_count, \"Hopper\")]\n",
        "sorted(token_arr, key=lambda x: x[0])"
      ]
    },
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