tokenization_examples.ipynb

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      "source": [
        "# this file shows what the symbolic music data looks like \n",
        "# as well as the tokenization process we use for finetuning on CoLA (single sentence)\n",
        "# and MRPC (double sentence) datasets\n",
        "\n",
        "!git clone https://github.com/wazenmai/MIDI-BERT.git\n",
        "%cd MIDI-BERT\n",
        "!pip install -r requirements.txt\n",
        "%cd MidiBERT\n",
        "%cd CP"
      ],
      "metadata": {
        "id": "XCD_l2POo1il"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import random\n",
        "import pickle\n",
        "import os\n",
        "root = '../../data/CP'\n",
        "dataset = 'pop909' # dataset containing Pop music; used for melody classification\n",
        "data = np.load(os.path.join(root, f'{dataset}_train.npy'), allow_pickle=True)\n",
        "print (\"This is what the symbolic music data looks like\")\n",
        "print (data)\n",
        "# token types: [Bar, Position, Pitch, Duration]\n",
        "# Number of possible combinations for each token type: [4,18,88,66]\n",
        "# the vectors at the end of the training set are all [ 2 16 86 64], which corresponds to the pad token"
      ],
      "metadata": {
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        },
        "outputId": "cbc128a8-377d-4c84-f5c9-10fc05e2d452"
      },
      "execution_count": 2,
      "outputs": [
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          "name": "stdout",
          "text": [
            "This is what the symbolic music data looks like\n",
            "[[[ 0  0 55  3]\n",
            "  [ 1  0 24 13]\n",
            "  [ 1  2 54  3]\n",
            "  ...\n",
            "  [ 1  3 43  5]\n",
            "  [ 1  5 57  1]\n",
            "  [ 1  5 31  3]]\n",
            "\n",
            " [[ 1  6 38  3]\n",
            "  [ 1  6 36  1]\n",
            "  [ 1  7 43  3]\n",
            "  ...\n",
            "  [ 1  9 33  3]\n",
            "  [ 1 10 38  1]\n",
            "  [ 1 11 38  5]]\n",
            "\n",
            " [[ 1 11 42  5]\n",
            "  [ 1 12 43  1]\n",
            "  [ 1 14 50  9]\n",
            "  ...\n",
            "  [ 1  0 50 11]\n",
            "  [ 1  0 23 13]\n",
            "  [ 1  0 35  3]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[ 0  0 18 11]\n",
            "  [ 1  0 30  3]\n",
            "  [ 1  0 37  5]\n",
            "  ...\n",
            "  [ 1 11 41  1]\n",
            "  [ 1 11 44  1]\n",
            "  [ 1 12 37  3]]\n",
            "\n",
            " [[ 1 13 54  3]\n",
            "  [ 1 13 42  1]\n",
            "  [ 1 13 46  1]\n",
            "  ...\n",
            "  [ 1  0 39  7]\n",
            "  [ 1  0 42  7]\n",
            "  [ 1  0 46  1]]\n",
            "\n",
            " [[ 1  0 47  7]\n",
            "  [ 1  1 27  1]\n",
            "  [ 1  2 56  1]\n",
            "  ...\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show the tokenization process for CoLA\n",
        "!pip install datasets==1.11.0\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "max_seq_length = 512\n",
        "task =  \"cola\"\n",
        "dataset = load_dataset(\"glue\", task)\n"
      ],
      "metadata": {
        "id": "YSmq85Kp2pD_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame(dataset[\"train\"][:])\n",
        "\n",
        "print('Size of training set: {:,}\\n'.format(df.shape[0]))\n",
        "\n",
        "# Display 10 random rows of data\n",
        "df.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "wmLP6VuTkkAe",
        "outputId": "0c0e892e-d756-47f8-ed59-f191bacdadcf"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of training set: 8,551\n",
            "\n"
          ]
        },
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              "                                               sentence  label   idx\n",
              "4359           There is likely to be no student absent.      1  4359\n",
              "6589               A woman hit a girl who was pregnant.      1  6589\n",
              "4306             Tom believes Stephen to be irritating.      1  4306\n",
              "6311            Brandon read every book that Megan did.      1  6311\n",
              "0     Our friends won't buy this analysis, let alone...      1     0\n",
              "8075  Peter is some disgruntled old pigs in those di...      0  8075\n",
              "2227                            This knife doesn't cut.      1  2227\n",
              "7205  it is the tall man come from the back that Mar...      0  7205\n",
              "7487                      John asked whether Bill left.      1  7487\n",
              "7492                          Mary sent a book to Bill.      1  7492"
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              "      \n",
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              "    .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",
              "      cursor: pointer;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-ebec3536-d5b0-44b0-855a-8d7392a2ab62 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-ebec3536-d5b0-44b0-855a-8d7392a2ab62');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertTokenizer\n",
        "\n",
        "sentences = df.sentence.values\n",
        "labels = df.label.values\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n",
        "\n",
        "input_ids = []\n",
        "\n",
        "\n",
        "for sent in sentences:\n",
        "\n",
        "    encoded_dict = tokenizer.encode(\n",
        "                        sent,                  \n",
        "                        max_length = max_seq_length ,           # Pad sentences\n",
        "                        add_special_tokens = False,       # Pad is the only special token\n",
        "                        truncation=True,\n",
        "                        pad_to_max_length = True,\n",
        "                        return_tensors = 'pt',     # Return pytorch tensors\n",
        "                   )\n",
        "  \n",
        "    input_ids.append(encoded_dict)\n",
        "    \n",
        "input_ids = torch.cat(input_ids, dim=0)\n",
        "labels = torch.tensor(labels)\n",
        "\n",
        "print('Original: ', sentences[0])\n",
        "print('Tokenized: ', tokenizer.convert_ids_to_tokens(input_ids[0]))\n",
        "print('Token IDs:', input_ids[0]) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "outputId": "005bf1d8-7bbe-45e0-8ea3-23d21a5b0210"
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      "outputs": [
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2132: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original:  Our friends won't buy this analysis, let alone the next one we propose.\n",
            "Tokenized:  ['Our', 'friends', 'won', \"'\", 't', 'buy', 'this', 'analysis', ',', 'let', 'alone', 'the', 'next', 'one', 'we', 'propose', '.', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
            "Token IDs: tensor([ 3458,  2053,  1281,   112,   189,  4417,  1142,  3622,   117,  1519,\n",
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            "            0,     0])\n"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now convert the token IDs to 4 dimensional vectors\n",
        "\n",
        "x = input_ids.numpy()\n",
        "y_train = labels.numpy()\n",
        "X_train = np.empty((x.shape[0], max_seq_length, 4))\n",
        "\n",
        "# token types: [Bar, Position, Pitch, Duration]\n",
        "# Number of possible combinations for each token type: [4,18,88,66]\n",
        "\n",
        "#PAD:       [ 2 16 86 64]\n",
        "#MASK:      [ 3 17 87 65]\n",
        "\n",
        "#The total number of possible combinations (excluding pad and mask) is thus 2*16*86*64 = 176,128\n",
        "#To constrain this closer to the vocabulary size of English BERT (roughly 30,000),\n",
        "#we don’t map to zero values in the Bar column since they correspond to the beginning of a musical bar\n",
        "#We also don’t map to values greater than 32 in the Duration column, since duration is measured in 32nd notes,\n",
        "#and there are very few data points in the pretraining sets that have a duration greater than 32\n",
        "\n",
        "# use modular arithmetic to map each ID to a unique 4D vector\n",
        "\n",
        "X_train[:,:,0] = 1                                          # map to 1 in the Bar column\n",
        "X_train[:,:,1] = x%16                                       # map to 0-16 in the Position column\n",
        "X_train[:,:,2] = ((x/16).astype(int))%86                    # map to 0-86 in the Pitch column\n",
        "X_train[:,:,3] = ((x/(16*86)).astype(int))%32               # map to 0-32 in the Duration column\n",
        "\n",
        "# the pad token is initially mapped to [1 0 0 0]\n",
        "# but we want it mapped to [ 2 16 86 64]\n",
        "for i in range (0,x.shape[0]):\n",
        "  for j in range (0,max_seq_length):\n",
        "    X_train[i,j,:] = np.array([2, 16, 86, 64]) if(np.sum(X_train[i,j,:]) == 1) else X_train[i,j,:]\n",
        "                              \n",
        "X_train = X_train.astype(int)      \n",
        "print('X_train shape: {}, y_train shape: {}'.format(X_train.shape,y_train.shape))     \n",
        "print (\" original ids\")\n",
        "print (x)       \n",
        "print (\" 4d vectors\")  \n",
        "print (X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qa2KRNitHub3",
        "outputId": "55d99f7e-d6fd-4817-bdea-8eddcbd61474"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (8551, 512, 4), y_train shape: (8551,)\n",
            " original ids\n",
            "[[ 3458  2053  1281 ...     0     0     0]\n",
            " [ 1448  1167 23563 ...     0     0     0]\n",
            " [ 1448  1167 23563 ...     0     0     0]\n",
            " ...\n",
            " [ 1135  1110  3123 ...     0     0     0]\n",
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            " 4d vectors\n",
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            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]]\n",
            "\n",
            " ...\n",
            "\n",
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            "  [ 1  3 23  2]\n",
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            "  [ 2 16 86 64]\n",
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            "  ...\n",
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      "cell_type": "code",
      "source": [
        "# now demonstrate the tokenization process for MRPC\n",
        "\n",
        "max_seq_length = 512\n",
        "task =  \"mrpc\"\n",
        "dataset = load_dataset(\"glue\", task)\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame(dataset[\"train\"][:])\n",
        "\n",
        "print('Size of training set: {:,}\\n'.format(df.shape[0]))\n",
        "\n",
        "df.sample(10)"
      ],
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        "id": "f5ds4_L-_aoK",
        "outputId": "8588a41d-71e5-401d-cd6b-a77cd6aa2beb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset glue/mrpc (download: 1.43 MiB, generated: 1.43 MiB, post-processed: Unknown size, total: 2.85 MiB) to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading: 0.00B [00:00, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading: 0.00B [00:00, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading: 0.00B [00:00, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "4cf9c4d15d7841189a289331c730beba"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7bbc35565f54458ab8d5b50906aa0112"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e793e9c6d1184a6f9afd70bdeabf2d2a"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "40dce04214304e0aad05294c6472f5ca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n",
            "Size of training set: 3,668\n",
            "\n"
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentences1 = df.sentence1.values\n",
        "sentences2 = df.sentence2.values\n",
        "sentences = df[[\"sentence1\", \"sentence2\"]].values.astype(\"str\")\n",
        "labels = df.label.values\n",
        "\n",
        "from transformers import BertTokenizer\n",
        "\n",
        "import torch\n",
        "\n",
        "input_ids = []\n",
        "attention_masks = []\n",
        "\n",
        "for sent in sentences:\n",
        "    encoded_dict = tokenizer.encode(\n",
        "                        sent[0], sent[1],           \n",
        "                        max_length = max_seq_length,           # Pad sentences\n",
        "                        add_special_tokens = False,       # Pad is the only special token\n",
        "                        truncation=True,\n",
        "                        pad_to_max_length = True,\n",
        "                        return_tensors = 'pt',     # Return pytorch tensors\n",
        "                   )\n",
        "    input_ids.append(encoded_dict)\n",
        "    # We don't use segment encodings\n",
        "\n",
        "input_ids = torch.cat(input_ids, dim=0)\n",
        "labels = torch.tensor(labels)\n",
        "\n",
        "print(' Original 1: ', sentences1[0])\n",
        "print(' Original 2: ', sentences2[0])\n",
        "print('Tokenized: ', tokenizer.convert_ids_to_tokens(input_ids[0]))\n",
        "print('Token IDs:', input_ids[0]) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGEiz8PU_cb6",
        "outputId": "362b5bf5-0ef9-4fa5-8ea5-d584ea789aca"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2132: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Original 1:  Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .\n",
            " Original 2:  Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .\n",
            "Tokenized:  ['Am', '##ro', '##zi', 'accused', 'his', 'brother', ',', 'whom', 'he', 'called', '\"', 'the', 'witness', '\"', ',', 'of', 'deliberately', 'di', '##sto', '##rting', 'his', 'evidence', '.', 'Re', '##fer', '##ring', 'to', 'him', 'as', 'only', '\"', 'the', 'witness', '\"', ',', 'Am', '##ro', '##zi', 'accused', 'his', 'brother', 'of', 'deliberately', 'di', '##sto', '##rting', 'his', 'evidence', '.', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
            "Token IDs: tensor([ 7277,  2180,  5303,  4806,  1117,  1711,   117,  2292,  1119,  1270,\n",
            "          107,  1103,  7737,   107,   117,  1104,  9938,  4267, 12223, 21811,\n",
            "         1117,  2554,   119, 11336,  6732,  3384,  1106,  1140,  1112,  1178,\n",
            "          107,  1103,  7737,   107,   117,  7277,  2180,  5303,  4806,  1117,\n",
            "         1711,  1104,  9938,  4267, 12223, 21811,  1117,  2554,   119,     0,\n",
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            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = input_ids.numpy()\n",
        "y_train = labels.numpy()\n",
        "X_train = np.empty((x.shape[0], max_seq_length, 4))\n",
        "\n",
        "# token types: [Bar, Position, Pitch, Duration]\n",
        "# Number of possible combinations for each token type: [4,18,88,66]\n",
        "\n",
        "#PAD:       [ 2 16 86 64]\n",
        "#MASK:      [ 3 17 87 65]\n",
        "\n",
        "#The total number of possible combinations (excluding pad and mask) is thus 2*16*86*64 = 176,128\n",
        "#To constrain this closer to the vocabulary size of English BERT (roughly 30,000),\n",
        "#we don’t map to zero values in the Bar column since they correspond to the beginning of a musical bar\n",
        "#We also don’t map to values greater than 32 in the Duration column, since duration is measured in 32nd notes,\n",
        "#and there are very few data points in the pretraining sets that have a duration greater than 32\n",
        "\n",
        "# use modular arithmetic to map each ID to a unique 4D vector\n",
        "\n",
        "X_train[:,:,0] = 1                                          # map to 1 in the Bar column\n",
        "X_train[:,:,1] = x%16                                       # map to 0-16 in the Position column\n",
        "X_train[:,:,2] = ((x/16).astype(int))%86                    # map to 0-86 in the Pitch column\n",
        "X_train[:,:,3] = ((x/(16*86)).astype(int))%32               # map to 0-32 in the Duration column\n",
        "\n",
        "# the pad token is initially mapped to [1 0 0 0]\n",
        "# but we want it mapped to [ 2 16 86 64]\n",
        "for i in range (0,x.shape[0]):\n",
        "  for j in range (0,max_seq_length):\n",
        "    X_train[i,j,:] = np.array([2, 16, 86, 64]) if(np.sum(X_train[i,j,:]) == 1) else X_train[i,j,:]\n",
        "                              \n",
        "X_train = X_train.astype(int)      \n",
        "print('X_train shape: {}, y_train shape: {}'.format(X_train.shape,y_train.shape))     \n",
        "print (\" original ids\")\n",
        "print (x)       \n",
        "print (\" 4d vectors\")  \n",
        "print (X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iabpWAK9-zBI",
        "outputId": "e404b842-c2d7-402b-f09e-9b7edb962869"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (3668, 512, 4), y_train shape: (3668,)\n",
            " original ids\n",
            "[[ 7277  2180  5303 ...     0     0     0]\n",
            " [10684  2599  9717 ...     0     0     0]\n",
            " [ 1220  1125  1502 ...     0     0     0]\n",
            " ...\n",
            " [  107  1284  1138 ...     0     0     0]\n",
            " [ 1109  1136  5783 ...     0     0     0]\n",
            " [ 1109  1476   118 ...     0     0     0]]\n",
            " 4d vectors\n",
            "[[[ 1 13 24  5]\n",
            "  [ 1  4 50  1]\n",
            "  [ 1  7 73  3]\n",
            "  ...\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]]\n",
            "\n",
            " [[ 1 12 65  7]\n",
            "  [ 1  7 76  1]\n",
            "  [ 1  5  5  7]\n",
            "  ...\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]]\n",
            "\n",
            " [[ 1  4 76  0]\n",
            "  [ 1  5 70  0]\n",
            "  [ 1 14  7  1]\n",
            "  ...\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[ 1 11  6  0]\n",
            "  [ 1  4 80  0]\n",
            "  [ 1  2 71  0]\n",
            "  ...\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]]\n",
            "\n",
            " [[ 1  5 69  0]\n",
            "  [ 1  0 71  0]\n",
            "  [ 1  7 17  4]\n",
            "  ...\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]]\n",
            "\n",
            " [[ 1  5 69  0]\n",
            "  [ 1  4  6  1]\n",
            "  [ 1  6  7  0]\n",
            "  ...\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]\n",
            "  [ 2 16 86 64]]]\n"
          ]
        }
      ]
    }
  ]
}