Train_BERT_Tokenizer.ipynb

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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "#some pip installs"
      ]
    },
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      "source": [
        "!pip install tokenizers\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# some imports\n",
        "import os\n",
        "from tokenizers import BertWordPieceTokenizer\n",
        "from datasets import load_dataset, Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ZCW55nUlhY6"
      },
      "source": [
        "#Loading the dataset\n",
        "We are using Huggingface's data hub"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUCEP3PNl2Mq"
      },
      "source": [
        "##Loading the data"
      ]
    },
    {
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      "execution_count": 24,
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      "source": [
        "# You should just change this part in order to download your\n",
        "# parts of corpus.\n",
        "# There are a total of 126 (indexing from 0-> 125) files 0f training text each with size of 1.07 GB roughly\n",
        "# There are a total of 3 (indexing from 0-> 2) files of testing text each with size of 1.07 GB roughly \n",
        "# for more cehck the link -> https://huggingface.co/datasets/SLPL/naab\n",
        "\n",
        "indices = {\n",
        "    \"train\": [81, 14, 3,\n",
        "              # 94, 35,\n",
        "              # 41, 28, 67, 55, 79\n",
        "              ],\n",
        "    \"test\": [0,\n",
        "            #  1\n",
        "             ]\n",
        "}\n",
        "\n",
        "N_FILES = {\n",
        "    \"train\": 126,\n",
        "    \"test\": 3\n",
        "}\n",
        "_BASE_URL = \"https://huggingface.co/datasets/SLPL/naab/resolve/main/data/\"\n",
        "data_url = {\n",
        "    \"train\": [_BASE_URL + \"train-{:05d}-of-{:05d}.txt\".format(x, N_FILES[\"train\"]) for x in range(N_FILES[\"train\"])],\n",
        "    \"test\": [_BASE_URL + \"test-{:05d}-of-{:05d}.txt\".format(x, N_FILES[\"test\"]) for x in range(N_FILES[\"test\"])],\n",
        "}\n",
        "for index in indices['train']:\n",
        "    assert index < N_FILES['train']\n",
        "for index in indices['test']:\n",
        "    assert index < N_FILES['test']\n",
        "data_files = {\n",
        "    \"train\": [data_url['train'][i] for i in indices['train']],\n",
        "    \"test\": [data_url['test'][i] for i in indices['test']]\n",
        "}\n"
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        "dataset_train = load_dataset('text', data_files=data_files[\"train\"], use_auth_token=False)\n",
        "dataset_test = load_dataset('text', data_files=data_files[\"test\"], use_auth_token=False)"
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        "##Turn each dataset into a .txt file to feed to the tokenizer\n",
        "\n",
        "inorder to train the tokenizer we could use the huggingface dataset object or turn our data to .txt file and then feed it to the toknizer. we are using the latter."
      ]
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        "# in this for loop we iteratre through train and tet datset imported from huggingface's hub and write them as .txt file\n",
        "\n",
        "for index, dataset in enumerate([dataset_train, dataset_test]):\n",
        "  current_dataset = dataset['train']['text']\n",
        "  # Save the current slice to a file\n",
        "  current_file = f'dataset{index + 1}.txt'               # each datset will be named -> \"dataset\" + index\n",
        "  print(f\"starting writing dataset{index + 1}\")\n",
        "  with open(current_file, 'w', encoding='utf-8') as file:\n",
        "      for text in current_dataset:\n",
        "          file.write(text + '\\n')\n",
        "\n",
        "  print(f\"finished writing dataset{index + 1}\")\n",
        "  # Free up memory by deleting the variable\n",
        "  del current_dataset"
      ]
    },
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      "execution_count": 27,
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      "source": [
        "# Set your paths and file names\n",
        "tokenizer_output_dir = \"bert_tokenizer\"\n",
        "if not os.path.isdir(tokenizer_output_dir):\n",
        "    os.mkdir(tokenizer_output_dir)"
      ]
    },
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      "cell_type": "markdown",
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        "id": "g_fQdouQRTV0"
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      "source": [
        "##Training the tokenizer\n",
        "\n",
        "For more information check the website -> https://huggingface.co/docs/tokenizers/api/trainers#tokenizers.trainers.WordPieceTrainer , https://huggingface.co/docs/tokenizers/api/tokenizer"
      ]
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        "# Training a WordPiece Tokenizer\n",
        "files = [\"dataset1.txt\", \"dataset2.txt\"]\n",
        "\n",
        "# Parameters for Tokenizer Training\n",
        "vocab_size = 10_000                                                 # The size of the final vocabulary, including all tokens and alphabet.\n",
        "min_frequency = 20                                                  # The minimum frequency a pair should have in order to be merged.\n",
        "special_tokens = [\"[PAD]\", \"[MASK]\", \"[CLS]\", \"[SEP]\", \"[UNK]\"]     #  A list of special tokens the model should know of.\n",
        "\n",
        "# Initialize the WordPiece tokenizer for BERT\n",
        "tokenizer = BertWordPieceTokenizer()\n",
        "print(\"starting to train the toeknizer\")\n",
        "\n",
        "# Train the tokenizer\n",
        "tokenizer.train(\n",
        "    files=files,\n",
        "    vocab_size=vocab_size,\n",
        "    min_frequency=min_frequency,\n",
        "    show_progress=True,\n",
        "    special_tokens=special_tokens,\n",
        ")\n",
        "print(\"training is done now saving...\")\n",
        "# Save the trained tokenizer\n",
        "tokenizer.save_model(tokenizer_output_dir)"
      ]
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